Internet DRAFT - draft-taddei-cless-introduction
draft-taddei-cless-introduction
IETF A. Taddei
Internet-Draft C. Wueest
Intended status: Informational K. Roundy
Expires: September 26, 2019 Symantec Corporation
D. Lazanski
Last Press Label
March 25, 2019
Capabilities and Limitations of an Endpoint-only Security Solution
draft-taddei-cless-introduction-00
Abstract
In the context of existing, proposed and newly published protocols,
this draft RFC is to establish the capabilities and limitations of
endpoint-only security solutions and explore benefits and
alternatives to mitigate those limits with the support of real case
studies.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
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Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
This Internet-Draft will expire on September 26, 2019.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . 4
3. Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 5
4. Disclaimer . . . . . . . . . . . . . . . . . . . . . . . . . 6
5. Endpoints: definitions, models and scope . . . . . . . . . . 6
5.1. Internal representation of an endpoint . . . . . . . . . 7
5.2. Endpoints modeled in an end-to-end context . . . . . . . 8
6. Threat Landscape . . . . . . . . . . . . . . . . . . . . . . 8
7. Endpoint Security Capabilities . . . . . . . . . . . . . . . 10
8. What would be a perfect endpoint security solution? . . . . . 13
9. The defence-in-depth principle . . . . . . . . . . . . . . . 15
10. Endpoint Security Limits . . . . . . . . . . . . . . . . . . 16
10.1. No possibility to put an endpoint security add-on on the
UE . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
10.1.1. Not receiving any updates or functioning patches . . 18
10.1.2. Mirai IoT bot . . . . . . . . . . . . . . . . . . . 19
10.2. Endpoints may not see the malware on the endpoint . . . 19
10.2.1. LoJax UEFI rootkit . . . . . . . . . . . . . . . . . 19
10.2.2. SGX Malware . . . . . . . . . . . . . . . . . . . . 20
10.2.3. AMT Takeover . . . . . . . . . . . . . . . . . . . . 20
10.2.4. AMT case study (anonymised) . . . . . . . . . . . . 21
10.2.5. Users bypass the endpoint security . . . . . . . . . 22
10.3. Endpoints may miss information leakage attacks . . . . . 22
10.3.1. Meltdown/Specter . . . . . . . . . . . . . . . . . . 22
10.3.2. Network daemon exploits . . . . . . . . . . . . . . 22
10.3.3. SQL injection attacks . . . . . . . . . . . . . . . 23
10.3.4. Low and slow data exfiltration . . . . . . . . . . . 23
10.4. Suboptimality and gray areas . . . . . . . . . . . . . . 24
10.4.1. Stolen credentials . . . . . . . . . . . . . . . . . 24
10.4.2. Zero Day Vulnerability . . . . . . . . . . . . . . . 25
10.4.3. Port scan over the network . . . . . . . . . . . . . 25
10.4.4. DDoS attacks . . . . . . . . . . . . . . . . . . . . 26
11. Learnings from production data . . . . . . . . . . . . . . . 27
11.1. Endpoint only incidents . . . . . . . . . . . . . . . . 28
11.2. Security incidents detected primarily by network
security products . . . . . . . . . . . . . . . . . . . 29
11.2.1. Unauthorized external vulnerability scans . . . . . 29
11.2.2. Unauthorized internal vulnerability scans . . . . . 30
11.2.3. Malware downloads resulting in exposed endpoints . . 30
11.2.4. Exploit kit infections . . . . . . . . . . . . . . . 30
11.2.5. Attacks against servers . . . . . . . . . . . . . . 31
12. Regulatory Considerations . . . . . . . . . . . . . . . . . . 32
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12.1. IoT Security . . . . . . . . . . . . . . . . . . . . . . 32
12.2. Network infrastructure . . . . . . . . . . . . . . . . . 33
12.3. Auditing and Assessment . . . . . . . . . . . . . . . . 33
12.4. Privacy Considerations . . . . . . . . . . . . . . . . . 34
13. Human Rights Considerations . . . . . . . . . . . . . . . . . 34
14. Security Considerations . . . . . . . . . . . . . . . . . . . 34
15. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 34
16. Informative References . . . . . . . . . . . . . . . . . . . 34
Appendix A. Contributors . . . . . . . . . . . . . . . . . . . . 39
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 40
1. Introduction
This Internet Draft aims to be a reference to the designers of
protocols on the capabilities and limitations of security solutions
on endpoint devices against malware and other attacks. As security
is entering a new phase in the arms race between attackers and
defenders, with many technical, economic and regulatory changes, and
with a significant increase in major data breaches, it is a good
moment to propose a systematic review and update on what is an old
and constantly evolving problem: endpoint security.
With the above context in mind this document will focus on the
capabilities and limitations of an endpoint-only security solution.
We want to explore a number of questions:
o What endpoint models do we have?
o What is the threat landscape under consideration?
o Can we differentiate security and privacy threats?
o What are common endpoint security capabilities?
o What would be an ideal endpoint security solution?
o What are the limits to endpoint security?
o What is real production data telling us?
o What can defence-in-depth help us with?
o What are the economic considerations?
o What are the regulatory considerations and constraints?
o What are the human rights considerations?
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Our goal with this review is to describe the benefits and limitations
of endpoint security in the real world, rather than in the abstract.
We aim to highlight security limitations that cannot be addressed by
endpoint solutions and to suggest how these may be mitigated with the
concept of a defence-in-depth approach, in order to increase the
resilience against attacks and data breaches.
2. Abbreviations
In this section we provide main abbreviations expansions
ABAC Attribute Based Access Control
AI Artificial Intelligence
AMT Active Management Technology
C&C Command and Control
CFI Control Flow Integrity
CFG Control Flow Guard
DDoS Distributed Denial of Service
DEP Data Execution Prevention
DGA Domain Generating Algorithms
DLP Data Loss Prevention
DMARC Domain-based Message Authentication, Reporting and Conformance
DoS Denial of Service
EE Execution Environment
EDR Endpoint Detection and Response
EPP Endpoint Protection Platform
FP False Positive
HIPS Host Intrusion Prevention System
ICD Integrated Cyber Defence
ICMP Internet Control Message Protocol
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IDS Intrusion Detection System
IoT Internet of Things
IPS Intrusion Prevention System
ML Machine Learning
MSS Managed Security Services
MSSP Managed Security Services Provider
NIST National Institute of Standards and Technology
NX No Execute Bit
P2P Peer to Peer
RAP Reuse Attack Protector
RBAC Role Based Access Control
RDP Remote Desktop Protocol
ROP Return Oriented Programming
SANS System Administration, Networking, and Security
SGX Software Guard eXtensions
SSH Secure SHell
UE User Equipment
UEFI Unified Extensible Firmware Interface
UX User Experience
VM Virtual Machine
XSS Cross Site Scripting
3. Definitions
In this section we provide definitions that are marked
o (L) Local to this document
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o (G REFERENCE) Global and then will be preceded by a reference
DoS (L) Literally a Denial of Service. Not to be confused with a
Network DoS or DDoS.
Endpoint security capabilities (L) How to protect the endpoint with
three different aspects of protection:
o Prevention - The attack doesn't succeed by intrinsic or explicit
security capabilities.
o Detection - The attack is happening or has happened and is
recorded and/or signalled to another component for action.
o Mitigation - Once detected, the attack can be halted or its
effects can at least be reduced or reversed.
System (L) A system is a heterogeneous set of any IT capabilities
including hardware, software, endpoints (including IoT), networks,
data centers and platforms with no assumptions on deployment form
factor (physical, virtual, microservices), deployment scenario,
geographic distribution, or dispersion.
User Equipment (G ITU-T H.360) Equipment under the control of an End
User
4. Disclaimer
This document is a first draft and is incomplete on purpose. Indeed
there are several areas where there are different ways to develop
this draft and the authors are seeking for feedback and extended
collaboration. This is to be noted too, that this is the first draft
RFC for the authors and contributors, so, coaching and help will be
appreciated. Overall, 'a bon entendeur, salut'.
Comments are solicited and should be addressed to the authors.
5. Endpoints: definitions, models and scope
Endpoints are the origin and destination for a communication between
parties. This encompasses User Equipment (UE) and the Host at the
other end of the communication. More work to model the various
endpoint types would be helpful for this draft (in the same spirit as
the IETF TEEP Working Group generalized its work, see [TEEP]).
We require a framework in order to define and model the endpoint
itself and the position of the endpoint in the network. In this
initial analysis we focus on endpoints that are User Equipment (UE)
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rather than on hosts. In the future, we hope to balance and unify
the model.
For example:
o The following would be considered as UEs: a smartphone, a smart
device, any IoT device, a laptop, a desktop, a workstation, etc.
o Hosts represent too, physical servers, virtual servers/machines,
etc.
We need two models for the endpoint, internally and in an end-to-end
context within the network. With this approach we expect both models
to help us cover all the threat landscape and capabilities for
endpoint security. This will help us understand point attacks versus
composite attacks within context, and, accordingly, understand
holistically the capabilities and the limitations of endpoint
security. For example to differentiate when only an application on
the end point is affected.
5.1. Internal representation of an endpoint
An internal representation of an endpoint could be generalized by the
simple diagram below:
+----------------------------+
| Application |
+----------------------------+
| OS / Execution Environment |
+----------------------------+
| Hardware |
+----------------------------+
Today there are many combinations of Hardware, OS/EE pairing and
Application layers, offering the user a vast set of features with a
wide spectrum of capabilities.
Furthermore we can consider that an application running on a UE or a
host is an endpoint too, so we have multiple ways to read the above
diagram.
In essence we want to consider here endpoints including those which
have a variance in electrical power, computational power, memory,
disk, network interfaces, size, ownership, etc.
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5.2. Endpoints modeled in an end-to-end context
A representation of endpoints in an end-to-end context could look
like the following diagram:
+-------+ +---------------------+---------+
| Human | <- (1) -> | Digital Persona | Application | <- (2) ->
+-------+ +-----------+-------------------+
| User Equipment |
+-------------------------------+
+----------------+ +----------------+
<- (2) -> | Network | <- (3) -> | Platform/Hosts |
| Infrastructure | +----------------+
+----------------+
1. Humans have a user experience (UX) with the UE, starting with an
explicit or implicit Digital Persona, engaging with an
application
2. The application will have sessions through a large Network
Infrastructure where we do not assume anything of the
infrastructure (could be landlines, mobile networks, satellites,
etc.) and those sessions reach
3. a Platform consisting of many Hosts either physical or virtual
and it ensures a large part of the end-to-end user experience.
In this end-to-end model we see that many other systems may have
interactions with the UE: the human, the UX, the digital persona, the
sessions, the intermediate network infrastructure, and the hosts and
application at the destination.
If we now look at security aspects of the above models, the threat
landscape is very large and the attack surface will cover all the
components and interactions at any level.
6. Threat Landscape
(Editor's note: this section will require a significant amount of
future development.)
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Given the vast number of combinations that the above generic modeling
offers us, defining a threat landscape should be done carefully and
will require a systematic methodology.
Therefore this entire section will be developed through future
iterations of the document, in this initial version we will start
structuring an approach and then adjust this based on feedback.
There is no doubt that we want to cover typical known attacks such
as:
o Malware (Trojans, viruses, backdoors, bots, etc.)
o Adware and spyware
o Exploits
o Phishing
o Script based attacks
o Ransomware, local Denial of Service (DoS) attacks
o Denial of Service (DoS) attacks
o Malicious removable storage devices (USB)
o In memory attacks
o Rootkits and firmware attacks
o Scams and online fraud
o System abuse (staging/proxying)
o etc.
To illustrate the difficulty to define a good threat landscape, when
it comes to cryptojacking and coinmining that were on the rise, in
which category do they fall: malware? DoS? system abuse? or a
category on its own?
This is why we wanted to conduct a thorough gap analysis using
existing definitions and frameworks, but we couldn't find an existing
comprehensive and recognized taxonomy dedicated to the threat
landscape on endpoints. We found however different models in this
field, and have considered two. We are open to further suggestions.
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Indeed both of the analysed frameworks contain threat landscape
descriptions:
o MITRE Common Attack Pattern Enumeration Classification (CAPEC).
See [CAPEC].
o MITRE ATT&CK. See [ATTACK].
These offer us interesting ways to assess the threat landscape:
o CAPEC offers a hierarchical view of attack patterns by domains
which can match some aspects of both of our above models, but we
will need to identify those attacks that fit exactly in our scope.
o ATT&CK offers a very straightforward categorized knowledge base of
attacks, but it concentrates on the entreprise attack chain, so we
will need to do some work to extract what we need.
We recognise however that these frameworks do not address all of the
threats that can affect the security of a system, for example they do
not cover; routing hijacking, flooding, selective blocking,
unauthorised modification of data sent to an endpoint, etc. Further
work to define categories of threats is therefore required.
As a further example, phishing should be included as an attack, but
whilst this is indeed an attack that will materialize on a device
through an application (email, webmail, etc.), the real target of
this attack is not the device, but the human behind the digital
persona.
Having a methodology of assessment is necessary here, because it will
help decide what is in scope vs. out of scope.
We are aware that once a method and the categories are fully defined
in this section, it will force a review of all the following sections
in the document. Whilst remapping will be necessary, it should not
drastically change the draft.
7. Endpoint Security Capabilities
In this section we try to define some endpoint security capabilities
(Editor's note: this section will require future development.)
In this version of the document we will start by developing a
framework to categorize and position endpoint security capabilities
with the goal of defining what an ideal endpoint security capability
would look like.
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By endpoint security capabilities we mean how to protect the endpoint
against attacks. Protection has many meanings, we want to
distinguish three different aspects of protection:
o Prevention - The attack doesn't succeed by intrinsic or explicit
security capabilities.
o Detection - The attack is happening or has happened and is
recorded and/or signalled to another component for action.
o Mitigation - Once detected, the attack can be halted or its
effects can at least be reduced or reversed.
For example, prevention methods include keeping the software updated
and patching vulnerabilities, implementing measures to authenticate
the provenance of incoming data to stop the delivery of malicious
content, or choosing strong passwords. Detection methods include
inspecting logs or network traffic. Mitigation could include
deploying backups to recover from an attack with minimal disruption.
Our intention however is not just to consider each endpoint security
capability separately, but also the overall endpoint security
holistically with all its interdependencies. Indeed, we defined a
simple endpoint, but each layer may or may not have a certain
spectrum of intrinsic capabilities and there may be multiple ways to
provide add-on and third-party endpoint security capabilities,
allowing complex interactions between all of these components.
We define two different aspects of endpoint security capabilities and
their subdivisions as:
o (A) Intrinsic security capability can be built-into each of the
endpoint model layers
* (1) Hardware
* (2) OS/EE
* (3) Application
o (B) Add-on security capability can be
* (4) a component of the hardware
* (5) a component of the OS/EE
* (6) an application by itself
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In (A) we relate to a 'security by design' intention of the
developers and they will intrinsically offer a security model and
security capabilities as part of their design. A typical example of
this is the authorization model.
In (B) a 3rd party is offering an additional security component which
was not necessarily considered when the Hardware, OS/EE or
Application were designed.
In the future we will review all the main categories of security
capabilities that are known to date and assess security capability
enablers like Artificial Intelligence (AI) and Machine Learning (ML).
For each category we will try to give a review on how effective the
capability is in securing the system.
With regard to (6), there are many available options for add-on
security capabilities offered by third-parties as applications on a
commercial or open-source basis. Gartner (see [GARTNERREPORT])
highlights the evolution of endpoint security towards two directions
as shown in [EPPEDR], [EPPSECURITY], [EPPGUIDE].
o Endpoint Protection Platform (EPP) as an integrated security
solution designed to detect and block threats at the device level.
o Endpoint Detection and Response (EDR) as a combination of next
generation tools to provide anomaly detection and alerting,
forensic analysis and endpoint remediation capabilities.
Among the security capabilities that we list, the endpoint can
perform the following:
o Intrinsic
* Software updates / patching
* Access Control (RBAC, ABAC, etc.)
* Authentication
* Authorization
* Detailed event logging
o Execution protection
* Exploit mitigation (file/memory)
* Tamper protection
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* Whitelisting filter by signatures, signed code or other means
* System hardening and lockdown (HIPS, trusted boot, etc.)
o Malware protection
* Scanning - on access/on write/scheduled/quick scan (file/
memory)
* Reputation-based blocking on files or by ML
* Behavior-based detection - (heuristic based/ML)
* Rootkit and firmware detection
* Threat intelligence based detection (cloud-based/on premise)
* Static detection - generic, by emulation, by ML, by signature
o Attack/Exploit/Application Protection
* Application protection (browser, messaging clients, social
media, etc.)
+ Disinformation Protection (anti-phishing, fake news, anti-
spam, etc.)
+ Detection of unintended link location (URL blocklist, etc.)
+ Memory exploit mitigation, e.g. browsers
* Network Protection (local firewall, IDS, IPS and local proxy)
inbound and outbound
* Detection of network manipulation (ARP, DNS, etc.)
* Data Loss Prevention and exfiltration detection (incl. covert
channels)
8. What would be a perfect endpoint security solution?
With all the above knowledge, let's consider what we could expect
from a perfect endpoint security 'system'. It would:
o find instantly accurate reputation for any file before it gets
executed and block it if needed.
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o monitor any behavior on the endpoint, including inbound and
outbound network traffic, learn and identify normal behavior and
detect and block malicious actions, even if the attack is misusing
legitimate clean system tools or hiding with a rootkit.
o patch instantly across all devices/systems/OSes, including virtual
patching, meaning you can patch or shield an application even
before an official patch is released.
o exploit protection methods for all processes where applicable,
e.g. no execute bit (NX), data execution prevention (DEP), address
space layout randomization (ASLR), Control Flow Integrity Guard
(CFI/CFG), stack canaries, shadow stack, reuse attack protection
(RAP), etc. all of which are methods, which make it very difficult
to successfully run any exploit, even for zero day
vulnerabilities.
o detect attempts to re-route data to addresses other than those
which the user intended, e.g. detect incorrectly served DNS
entries, TLS connections to sites with invalid certificates, data
that is being proxied without explicit user consent, etc.
o have an emulator/sandbox/micro virtualization to execute code and
analyse the outcome and perform a roll back of all actions if
needed, e.g. for ransomware.
o allow the endpoint to communicate with the other endpoints in the
local network and globally, to learn from 'the crowd' and
dynamically update rules based on its findings.
o be in constant sync with all other endpoints deployed on a network
and other security solutions, run on any OS, with no delay
(including offline modes and on legacy systems).
o run from the OS/EE when possible.
o run as one of the first process on the OS/EE and protect itself
from any form of unwanted tampering.
o offers a reliable logging that can't be tampered with, even in the
event of system compromise.
o receive updates instantly from a trusted central entity.
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9. The defence-in-depth principle
In this section we give a high level view of what we mean by
'defence-in-depth'.
Whilst endpoint security systems have good capabilities, sometimes it
is debatable and perhaps suboptimal to let the endpoint run the
capability alone or at all. It is generally considered good security
practice to adopt a defence-in-depth approach (see [USCERT]). The
Open Web Application Security Project group (OWASP) describes the
concept as follows: "The principle of defense-in-depth is that
layered security mechanisms increase security of the system as a
whole. If an attack causes one security mechanism to fail, other
mechanisms may still provide the necessary security to protect the
system." (see [OWASP])
Indeed there are many other constituencies as per our end-to-end
model that can participate in the defence process: The network, the
infrastructure itself, the platform, the human, the user experience
and in a hybrid of an on premise and cloud approach, an Integrated
Cyber Defence (ICD) of the entire chain.
The simple idea behind the concept is that "every little helps". If
the endpoint is not 100% secure itself, the detection chance can
increase with additional security capabilities from other entities.
We acknowledge that there are some case where adding an additional
component to the system may degrade the overall security level by
introducing new weaknesses.
There are various reference article in the industry highlighting
limitations of endpoint only solutions. For example this quote here,
which talks about multi-tier solutions: "There are limitations with
any endpoint protection solution, however, that can limit protection
to only the client layer. There is also a need for security above
the client layer, as endpoint protection products cannot intercept
traffic. Vendors will often sell a multi-tiered solution that
enables a network appliance to assist the endpoint protection client
by intercepting traffic between the attacker and the infected client.
Vendors will also sell solutions that monitor and intercept traffic
on internal or external network segments to protect the enterprise
from these threats. A prime example of the limitations of endpoint
protection software is infection via a phishing attack." [ADAPTURE].
Some sources point out that even the best solution might not get
deployed in the optimal way in a real world scenario as the
environment can be very complex: "While endpoint security has
improved significantly with the introduction of application
whitelisting and other technologies, our systems and devices are
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simply too diverse and too interconnected to ensure that host
security can be deployed 100% ubiquitously and 100% effectively."
[NETTODAY]
On these grounds it is considered a good idea to follow a layered
approach when it comes to security. "In today's complex threat
environment, companies need to adopt a comprehensive, layered
approach to security, which is a challenging task in such as rapidly
evolving, crowded market." [HSTODAY]
It is important to comprehend the capabilities of endpoint security
solutions in this overall picture of the connected environment, which
includes other systems, networks and various protocols that are used
to interact with these entities. Understanding possible shortcomings
from single layered solutions can help counterbalance such weaknesses
in the architectural concept or the protocol design.
In order to quantify any potential benefits or limitations of the
various layered scenarios in regards to security a solid data set is
needed. This section requires statistics about proportions of
attacks that go undetected in various cases. We propose analysing
data for the following four cases:
o There is no security solution
o Security is only on the endpoint
o Security is only on the network
o Security is on both the endpoint and the network
However reconciling various statistics requires a lot of caution and
time, a methodology and consistent classification to avoid any
misinterpretation.
10. Endpoint Security Limits
The previous section defines an ideal endpoint security 'system',
however, from the real world, the expectation of what we can get from
an endpoint security solution will look more along the following
lines:
o may not be able to run at full capacity due to computational power
limits, battery life, performance, or policies (such as BYOD
restrictions in enterprise networks), etc.
o may not be able to run at full capacity as it slows down
performance too much.
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o will miss some of the malware or attacks, regardless of detection
method used, like signatures, heuristics, machine learning (ML),
artificial intelligence (AI), etc.
o have some level of False Positives (FP).
o not monitoring or logging all activities on the system, e.g. due
to constraints of disk space or when a clean windows tool is being
triggered to do something malicious but the activity is not
logged. Such activity can be logged, but a decision needs to be
made if it's clean or not.
o have its own vulnerabilities or simple instabilities that could be
used to compromise the system.
o be tampered with by the user, e.g. disabled or reconfigured.
o be tampered with by the attacker, e.g. exceptions added or log
files wiped.
In the section below we review a number of these limitations through
real examples, step by step. Some limitations are absolute, and some
limitations result in a grey area or suboptimality for the solution.
10.1. No possibility to put an endpoint security add-on on the UE
UEs will vary a lot; by 2022, an estimated 29 billion devices will be
connected, with 18 billion of them related to IoT [ERICSSON]. Many
IoT products lack the capacity to install any endpoint security
capabilities, are unable to update the software, and it is not
possible to force the UE provider to improve or even offer an
intrinsic security capability.
We acknowledge that the numbers do vary significantly depending on
the source, for example:
o [STATISTA1] is showing the current trajectory of IoT devices from
25B to date to 40+B in 2022 and 75B in 2025.
o [ERICSSON] is more conservative and might requires an update, but
it was reaching 29B devices in 2022, with a nice breakdown between
device types and connectivity.
o [STATISTA2] is showing a breakdown by verticals and is even more
conservative than both of the above.
o [ENISA] it refers to a [GARTNERIOT] report from 2017 which sets a
trajectory to 20B devices by 2020.
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In IoT we find UEs such as medical devices which are limited by
regulation, welding robots that can't be slowed down, smart light
bulbs which are limited by the processing power, etc. There are many
factors influencing whether endpoint security can be added to a UE:
o The UE is simply not powerful enough or the performance hit is too
high.
o Adding your own security will breach the warranty or will
invalidate a certification or a regulation (breach of validity).
o The UE needs to run in real-time and any delay introduced by a
security process might break the process.
o Some UEs are simply locked by design and the manufacturer does not
provide a security solution (e.g. smart TV, fitness tracker or
personal artificial assistants) see [CANDID1], [CANDID2].
In the future, a possible research problem would be to find hard data
on the exact proportion of IoT devices that are unable to run any
endpoint security add-on or that have no intrinsic security built-in.
The other hidden dimension here is the economical aspect. Many
manufacturer are reluctant to invest in IoT device security, because
it can significantly increases the cost of their solution and there
is the perception that they will lose market shares, as customers are
not prepared to pay the extra cost for added security.
10.1.1. Not receiving any updates or functioning patches
The endpoint security system may lack a built-in capability to be
patched or it may be connected to a network that prevents the process
of downloading updates automatically. For example stand-alone
medical systems or industrial systems in isolated network segments
often do not have a communication channel to the Internet.
Even if security updates are received, they typically will only be
periodically updated; hence there will be a window of opportunity for
an attacker, between the time the attack is first used, and the time
the attack is discovered/patched and the patch is deployed.
In addition updates and patches may themselves be malicious by
mistake, or on purpose if not properly authenticated, or if the
source of the updates has malicious intent. This could be part of a
software update supply chain attack or an elaborate attacker breaking
the update process, as for example seen with the Flamer group (see
[FLAMER]).
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A recent survey found that fewer than 10% of consumer IoT companies
follow vulnerability disclosure guidelines at all, which is regarded
as a basic first step in patching vulnerabilities (see
[IOTPATCHING]). This indicates that many IoT devices do not have a
defined update process or may not even create patches for most of the
vulnerabilities.
Furthermore some endpoints system may reach the end of their support
period and therefore no longer receive any updates for the OS/EE or
the security solution due to missing licenses. However the systems
may remain in use and become increasingly vulnerable as time goes on
and new attacks are discovered.
10.1.2. Mirai IoT bot
+-------------+-----------------------------------------------------+
| Description | A Mirai bot infecting various IoT devices through |
| | weak passwords over Telnet port TCP 23 and by using |
| | various vulnerabilities, for example the SonicWall |
| | GMS XML-RPC Remote Code Execution Vulnerability |
| | (CVE-2018-9866) on TCP port 21009. Once a device is |
| | compromised it will scan for further victims and |
| | then start a DoS attack. |
+-------------+-----------------------------------------------------+
| Simplified | Compromised device scans network for multiple open |
| attack | ports, attempts infection through weak password and |
| process | exploits, downloads more payload, starts DoS |
| | attack. |
| | |
| UE | No security tool present on majority of IoT |
| | devices, hence no detection possible. If a |
| | rudimentary security solution with limited |
| | capabilities such as outgoing firewall is present |
| | on the IoT device e.g. router, then it might be |
| | able to detect the outbound DoS attack and slow it |
| | down. |
| | |
| References | [MIRAI1][MIRAI2] |
+-------------+-----------------------------------------------------+
10.2. Endpoints may not see the malware on the endpoint
10.2.1. LoJax UEFI rootkit
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+-------------+-----------------------------------------------------+
| Description | A device compromised with the LoJax UEFI rootkit, |
| | which is active before the OS/EE is started, hence |
| | before the endpoint security is active. It can pass |
| | back a clean 'image' when the security solution |
| | tries to scan the UEFI. Infection can either happen |
| | offline with physical access or through a dropper |
| | malware from the OS/EE. |
+-------------+-----------------------------------------------------+
| UE | A perfect endpoint security could potentially |
| | detect the installation process if it is done from |
| | the OS/EE and not with physical modification or in |
| | the factory. Once the device is compromised the |
| | endpoint security solution can neither detect nor |
| | remove the rootkit. The endpoint solution may |
| | detect any of the exhibited behaviour, for example |
| | if the rootkit drops another malware onto the OS/EE |
| | at a later stage. |
| | |
| Reference | [LOJAX] |
+-------------+-----------------------------------------------------+
10.2.2. SGX Malware
+-------------+-----------------------------------------------------+
| Description | Malware can hide in the Intel Software Guard |
| | eXtensions (SGX) enclave chip feature. This is a |
| | hardware-isolated section of the CPU's processing |
| | memory. Code running inside the SGX can use return- |
| | oriented programming (ROP) to perform malicious |
| | actions. |
+-------------+-----------------------------------------------------+
| UE | Since the SGX feature is by design out of reach for |
| | the OS/EE, an endpoint security solution can |
| | neither detect nor remove any injected malware. A |
| | perfect endpoint security solution could |
| | potentially detect the installation process if it |
| | is done from the OS/EE and not with physical |
| | modification or in the factory. |
| | |
| References | [SGX1] [SGX2] |
+-------------+-----------------------------------------------------+
10.2.3. AMT Takeover
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+-------------+-----------------------------------------------------+
| Description | A targeted attack group can remotely execute code |
| | on a system through the Intel AMT (Active |
| | Management Technology) vulnerability |
| | (CVE-2017-5689) over TCP ports 16992/16993. This |
| | provides full access to the computer, including |
| | remote keyboard and monitor access. The attacker |
| | can install malware, modify the system or steal |
| | information. |
+-------------+-----------------------------------------------------+
| UE | The AMT is accessible even if the PC is turned off. |
| | Therefore any endpoint security software installed |
| | on the OS, would not be able to see this traffic |
| | and therefore also not able to detect it. |
| | |
| References | [AMT1], [AMT2] |
+-------------+-----------------------------------------------------+
10.2.4. AMT case study (anonymised)
An enterprise has a data center containing very sensitive data.
Their workstations use a certain Intel chipset which integrates the
AMT feature for remote computer maintenance. AMT is an interface for
hardware management of the workstations, including transmission of
screen content and keyboard and mouse input for remote maintenance.
Communication with the management workstation is implemented by AMT
through the network interface card (NIC) on the motherboard. The
network packets generated in this way are invisible both to the main
processor and thus to the OS running on the workstation. In autumn
of 2015, it became known that some AMT-enabled computers had a flaw
that allowed AMT's remote maintenance component to be activated and
configured by attackers. This also worked when the workstations were
switched off. The leakage of data through this vulnerability is
elusive and difficult to detect. The identified threat situation led
the organization to a new requirement implementing a method that can
reliably detect this and similar vulnerabilities. In particular, the
detection of rootkits and manipulated firmware, and this includes
also (UEFI) BIOS - has also been a focus of their attention.
The method used as a solution, compares the desired data packets
generated by a client operating system - the user, with the data
packets received on the switch port. If more data has been received
on the switch port than was been sent by the operating system - the
user, there is a strong possibility that something bad is happening -
like for example an infection via modified firmware or by rootkit.
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10.2.5. Users bypass the endpoint security
+-------------+-----------------------------------------------------+
| Description | Endpoint security systems should not interfere with |
| | the normal operation of the endpoint to the extent |
| | that users become frustrated and want to disable |
| | them or configure them to disable a significant |
| | fraction of important security capabilities. |
+-------------+-----------------------------------------------------+
| UE | Add-on endpoint security is now bypassed or |
| | disabled by the user. Unless the endpoint is under |
| | monitored management or can prevent a user from |
| | modifying the configuration, then this is shutting |
| | down a significant fraction of the security |
| | capabilities. |
| | |
| References | [NINESIGNS] |
+-------------+-----------------------------------------------------+
10.3. Endpoints may miss information leakage attacks
Another aspect that endpoint security has issues in detecting are
information disclosure or leakage attacks, especially on shared
virtual/physical systems.
10.3.1. Meltdown/Specter
The Meltdown/Specter vulnerabilities and all its variants may allow
reading of physical memory belonging to another virtual machine (VM)
on the same physical system. This could reveal passwords,
credentials, certificates etc. The trick is that an attacker can
spin up his own VM on the same physical hardware. As this VM is
controlled by the attacker, they will ensure that there is no
endpoint security that detects the Meltdown exploit code when run.
It is very difficult for the attacked VM to detect the memory read-
outs. For know CPU vulnerabilities there are software patches
available than can be applied. If it is an external service
provider, it might not be in the power of the user to patch the
physical system or to determine if this has been done by the
provider.
10.3.2. Network daemon exploits
Other attack types, which leak memory data from a vulnerable web
server, are quite difficult to detect for an endpoint security. For
example the Heartbleed bug allows anyone on the Internet to read the
memory of the systems protected by the vulnerable versions of the
OpenSSL software. This could lead to credentials or keys being
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exposed. An endpoint solution needs to either patch the vulnerable
application or monitor it for any signs of exploitation or data
leakage and prevent the data from being exfiltrated.
10.3.3. SQL injection attacks
A SQL injection attack is an example of an attack that exploits the
backend logic of an application. Typically this is a web application
with access to a database. By encoding specific command characters
into the query string, additional SQL commands can be triggered. A
successful attack can lead to the content of the whole database being
exposed to the attacker. There are other similar attacks that can be
grouped together for the purpose of this task, such as command
injection or cross site scripting (XSS). Although they are different
attacks, they all at their core fail at input filtering and
validation, leading to unwanted actions being performed.
Applications that are vulnerable to SQL injections are very common
and are not restricted to web applications. An endpoint solution
needs to monitor all data entered into possible vulnerable
applications. This should include data received from the network. A
generic pattern matching for standard SQL injection attack strings
can be applied to potentially block some of the attacks. In order to
block all types of SQL injection attacks the endpoint solution should
have some knowledge about the logic of the monitored application,
which helps to determine how normal requests differ from attacks.
Applications can be analysed at source code level for potential
weaknesses, but dynamically patching is very difficult. See [SQL]
10.3.4. Low and slow data exfiltration
An endpoint security solution can detect low and slow data
exfiltration, for example when interesting data sources are tracked
and access to them is monitored. If the data source is not on the
endpoint itself, e.g. a database in the network, then the received
data needs to be tagged and its further use needs to be tracked. To
make detection difficult, an attacker could decide to use an
exfiltration process that sends only 10 bytes every Sunday to a
legitimate cloud service. If that is not in the normal behavior
pattern, then this anomaly could be detected by the endpoint. If the
process that sends the data or the destination IP address have a bad
reputation, then they could be stopped. Though it is very difficult
to reliably block such an attack and most solutions have a specific
threshold that needs to be exceeded before it is detected as an
anomaly.
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10.4. Suboptimality and gray areas
10.4.1. Stolen credentials
Stolen credentials and misuse of system tools such as RDP, Telnet or
SSH are a valid scenario during attacks. An attacker can use stolen
credentials to remotely log into a system and access data or execute
commands in this context like the legitimate user might do. An
endpoint security solution can restrict access from specific IP
addresses, but this is difficult in a dynamic environment and when an
attacker might have already compromised a trusted device and misuse
it as a stepping stone for lateral movement. The endpoint could
perform additional checks of the source device, such as verifying
installed applications and certain conditions. Again this will not
work in all scenarios, e.g. a hijacked valid device during lateral
movement.
This means that the system will not be able to simply block the
connection if the authentication with the stolen credentials
succeeds. A multi factor authentication (MFA) could limit the use of
stolen credentials, but depending on the system used and the
determination of the attacker they might be able to bypass this
hurdle as well e.g. cloning a SIM card to read text message codes.
As a next step, a solution on the endpoint can monitor the behavior
of the logged in user and determine if it represents expected normal
behavior. Unfortunately, there is the chance for false positives
that might block legitimate actions, hence the rules are usually not
applied too tightly. The system can monitor for suspicious behavior,
similar to malware detection, where every action is carefully
analyzed and all activity is tracked. For example if the SSH user is
adding all files to archives with passwords and then deletes the
original files in the file explorer, then this could result in a
ransomware case scenario. If only a few files are processed per
hour, then this activity will be very difficult for the endpoint to
distinguish from normal activity, in order to flag it as malicious.
The problem of attackers blending in with normal activity is one of
the biggest challenges with so called living off the land attack
methods. The attacker chooses to keep their profile low by not
installing any additional binary files on the system, but instead
misuses legitimate system tools to carry out their malicious intent.
This means that there is no malware file that could be identified and
the detection relies solely on other methods such as behaviour based
monitoring [LOTLSYMC].
If information is shared across multiple endpoints, then each one
could learn from the others and see how many connections came in from
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that source, what files were involved and what behavior the clients
exhibited. This crowd wisdom approach would allow blocking rules to
be applied after the first incident across multiple endpoints.
10.4.2. Zero Day Vulnerability
+-------------+-----------------------------------------------------+
| Description | An attacker exploits a zero day vulnerability or |
| | any recent vulnerability. |
+-------------+-----------------------------------------------------+
| UE | In theory this scenario could be handled by the |
| | endpoint security: a) Once the intrinsic security |
| | system has been patched, exploitation of the |
| | vulnerability can be prevented. b) The add-on |
| | security with enhanced capabilities or updated |
| | methods can detect and mitigate the vulnerability. |
| | It does not necessarily require the official patch. |
| | |
| Challenge | In practice many systems remain vulnerable to a |
| | vulnerability months or even years after a security |
| | fix has been released. Moreover there is a big gap |
| | between when a vulnerability is disclosed and when |
| | a security fix is available. Also there is a big |
| | gap between when a security fix is available and |
| | when the security fix is actually applied. A recent |
| | study over three years, examined the patching time |
| | of 12 client-side and 112 server-side applications |
| | in enterprise hosts and servers. It took over 6 |
| | months on average to patch 90% of the population |
| | across all vulnerabilities. [NDSSPATCH]. We note |
| | too: "The patching of servers is overall much worse |
| | than the patching of client applications. On |
| | average a server application remains vulnerable for |
| | 7.5 months." |
| | |
| References | [ZERODAY1][ZERODAY2] |
+-------------+-----------------------------------------------------+
10.4.3. Port scan over the network
An infected machine, let's say a Mirai bot on a router, is scanning a
class B network for IP addresses with TCP port 80 open. The malware
can slow it down to 1 IP address per 5 seconds (or any other
threshold) and it can go in randomized order (like for example the
nmap tool does) in order to make it difficult to find a sequential
pattern. To increase detection difficulties, legitimate requests to
existing web servers can be added in at random intervals.
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An endpoint solution might be able to detect this behaviour,
depending on the threshold, but it will be difficult. At some point
the pattern will be similar to browsing the web, so either the
endpoint blocks the bot scanning and also the user from surfing, or
it allows both.
To make it even harder, the attacker can use a botnet that
communicates over peer-to-peer (P2P) or a central command and control
server (C&C) and then distribute the scan load over multiple hosts.
This means each endpoint only scans a subset, let's say 100 IP
addresses, but all 1,000 bots scan a total of 100,000 IP addresses.
This attack is difficult to detect by a reasonable threshold on each
endpoint individually. If the endpoints talk to each other and
exchange information, then a collective decision can be made on the
bigger picture of the bot traffic.
Another option for the endpoint solution is to block the bot malware
from operating on the computer, for example by detecting the
installation, analyzing the behavior of the process or by preventing
the binary from accessing the network. This includes blocking any
form of communication for the process to its C&C server, regardless
of if it is using a P2P network or misusing legitimate system tools
or browsers to communicate with the Internet. Blocking indirect
communication over system tools as part of living off the land
tactics, can be very challenging.
See [BOT]
10.4.4. DDoS attacks
For this example let us consider a botnet of 100,000 compromised
computers and each one sends a burst of traffic to a remote target,
for one second each, alternating in groups. This will generate some
waves of pulse attack traffic. Similar comments can be made about
overall pulsed DDoS attacks [PDDoS].
A solution on the endpoint can attempt to detect the outgoing
traffic. If the DoS attack is volume based and the time span of each
pulse is large enough or the repeating frequency for each bot is
high, then detection with thresholds on the endpoint is feasible. It
is different, if it is an application layer DoS attack, where the
logic of the receiving application is targeted, for example with too
many search queries in HTTP GET requests. This would flood the
backend server with intensive search requests, which can result in
the web site no longer being responsive. Such attacks can succeed
with a low amount of requests being sent, especially if its
distributed over a botnet. This makes it very difficult for a single
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endpoint to detect such an ongoing attack, without knowledge from
other endpoints or the network.
Another option for the endpoint solution is to block the bot malware
from operating on the computer, for example by detection the
installation, analyzing the behavior of the process or by preventing
the binary from accessing the network. This includes blocking any
form of communication for the process to its C&C server, regardless
of if it is using a P2P network or misusing legitimate system tools
or browsers to communicate with the Internet. Blocking indirect
communication over system tools as part of living off the land
tactics, can be very challenging.
11. Learnings from production data
From the above limited considerations we can now check what we see
from real production data using
o the method described in [MONEYBALL]
o the anonymised production data of Symantec MSS production for the
past 3 months
The core idea is to consider, based on all the imperfections we
started to list above including the 'grey areas', that cybersecurity
analysts are often presented with suspicious machine activity that
does not conclusively indicate a compromise, resulting in undetected
incidents or costly investigations into the most appropriate remedial
actions.
As Managed Security Services Providers (MSSP's) are confronted with
these data quality issues, but also possess a wealth of cross-product
security data that enables innovative solutions, we decided to use
the Symantec MSS service for the past 3 months. The Symantec MSS
service monitors over 100 security products from a wide variety of
security vendors for hundreds of enterprise class customers from all
verticals.
We selected the subset of customers using the service that deploy
both network and endpoint security products to determine which types
of security incidents were most likely to be detected by endpoint
products vs. network products. In doing so, we were particularly
interested in identifying which categories of incidents are detected
by endpoint products and not network products, and vice versa. Thus,
we examined prevalent categories of incidents for which the only
actionable security alerts were predominantly produced by one type of
security product and not the other. To do so, we extracted all
security incidents detected by Symantec MSS on behalf of hundreds of
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customers that deploy both network and endpoint security products,
over a three-month period from December 2018 through the end of
February 2019. We acknowledge that some attacks might have been
blocked by the first product and therefore have never been seen by
the next security solution, which influences the final numbers.
With this in mind, we could identify incidents based on:
+------------+------------------------------------------------------+
| Severity | 4 - Emergency, 3 - Critical, 2 - Warning, 1 - |
| | Informational |
+------------+------------------------------------------------------+
| Incident | Malicious Code, Deception Activity, Improper Usage, |
| Category | Investigation, etc. |
| | |
| Incident | Trojan Horse Infection, Suspicious DGA Activity, |
| Type | Suspicious Traffic, Suspicious URL Activity, |
| | Backdoor infection, etc. |
| | |
| # network | Amount of network only security incidents |
| incidents | |
| | |
| # all | What is the total amount of incidents on all |
| incidents | security solutions |
| | |
| Percentage | Percentage of network security only incidents |
+------------+------------------------------------------------------+
We ended up with
o Hundreds of thousands of security incidents
o which we could categorize in 275 incident types by category and
severity (triplets Severity-Category-Type)
o out of which we searched how many incidents of each type were
detected by a network security product and missed by deployed
endpoint security products at least 75% of the time or vice versa
11.1. Endpoint only incidents
The categories of incidents that are detected primarily by endpoint
security products are fairly intuitive. They consist primarily of
detections of file-based threats and detection of malicious behaviors
through monitoring of system and network behavior at the process
level. The most prevalent of these behavioral detections include
detections of suspicious URLs based on heuristics and blacklists of
IP addresses or domain names. Since most of these alerts are not
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corroborated by network products, it seems probable that the
blacklists associated with network products tend to be more focused
on attacks while host-based intrusion prevention system alerts focus
more on malware command and control traffic. Most other behavioral
detections at the endpoint provide alerts based on system behavior
that is deemed dangerous and symptomatic of malicious intent by a
malicious or infected process. The highest severity incidents
detected on endpoints are instances of post-compromise outbound
network behavior that are symptomatic of command and control
communications traffic, but these did not show up as being primarily
detected by endpoint products as they are frequently corroborated by
network-based alerts.
11.2. Security incidents detected primarily by network security
products
Perhaps less intuitive are the results of examining categories of
security incidents that are detected primarily by network security
products and only rarely corroborated by endpoint security products.
Below we provide details regarding incident categories for which a
network security product produced a detection and for which there
were no actionable endpoint alerts for at least 75% of the incidents
in the category.
In our study we found 32 incident type, category, and severity
triplets of this type. The following categories critical incident
types were reported by MSS customers, and we discuss each in turn in
decreasing order of prevalence:
11.2.1. Unauthorized external vulnerability scans
Perhaps unsurprisingly, unauthorized external attempts to scan
corporate resources for vulnerabilities and other purposes are
detected in large volumes by a broad variety of network-focused
security products. 79% of incidents of this type were detected by
network security products with critical-severity alerts, these
security incident detections are not accompanied by any actionable
endpoint alerts, despite the fact that endpoint security products are
deployed by these enterprises. This category of threats encompasses
a broad variety of attacks, the most prevalent of which are the
following: Horizontal scans, SQL injection attacks, password
disclosure vulnerabilities, directory traversal attacks, and
blacklist hits. Of these categories of detections, horizontal scans
stand out as the category of detection that endpoint-security
products are least likely to detect on their own.
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11.2.2. Unauthorized internal vulnerability scans
Unauthorized internal vulnerability scans, though less frequent, are
more alarming, as they are likely to represent possible post-
compromise activity. We note that the Managed Security Service works
with its customers to maintain lists of devices that are authorized
to perform internal vulnerability scans, and their activity is
reported separately at a lower levels of incident severity. 89% of
detected unauthorized internal vulnerability scans are detected by
network products without any corroborating actionable alerts from
endpoint security products. As compared to unauthorized external
scan incidents, internal hosts that perform vulnerability scans are
far more active and the fraction of alerts that detect horizontal
scans is higher, representing half of the total alerts generated.
Alerts focused on Network-Behavior Anomaly Detection also appear for
internal hosts.
11.2.3. Malware downloads resulting in exposed endpoints
This category of threats is generally detected by network security
appliances. Despite these enterprises being purchasers of endpoint
security products, 76% of the incidents detected by the network
security products do not show a corresponding alert by an endpoint
security product. A broad variety of network appliances contributed
to the detection of a diverse collection of malware samples.
11.2.4. Exploit kit infections
This category of infections represents instances in which the
customer's machines are exposed to exploit kits. These threats were
detected by network appliances that extract suspicious URLs from
network traffic taps and use a combination of sandbox technology and
blacklists to identify websites that deploy a variety of exploit kits
that were not being caught by endpoint security products. In this
three month time period, the most prevalent categories of exploit
kits detected involved redirections to the Magnitude exploit kit and
exploit kits associated with phishing scams and attempts to expose
users to fake Anti-Virus warnings and tools. A breakdown of the
results is included below:
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+---------------------+-----------------------+
| Severity | 3 - Critical |
+---------------------+-----------------------+
| Incident Category | Malicious Code |
| | |
| Incident Type | Exploit Kit Infection |
| | |
| # network incidents | 26 |
| | |
| # all incidents | 26 |
| | |
| Percentage | 100% |
+---------------------+-----------------------+
The network security product that detected these incidents produced
the following alerts:
o Advanced Malware Payloads
o Exploit.Kit.FakeAV
o Exploit.Kit.Magnitude
o Exploit.Kit.MagnitudeRedirect
o Exploit.Kit.PhishScams
o HTMLMagnitudeLandingPage
11.2.5. Attacks against servers
In addition to detecting the aforementioned critical security
incident categories, network security devices frequently detect a
broad variety of attacks against servers that usually lack
corroboration at the endpoint. Most server attacks are not matched
by endpoint protection alerts: 62% are unmatched for critical
incidents, and 88% are unmatched as lower severity incidents. This
category of incidents is the most prevalent category of incidents
detected primarily by network products, but they are usually rated
lower in severity than the aforementioned classes of alerts as they
are very commonplace. Even when these alerts are corroborated by
endpoint protection alerts, the endpoint alerts are often low in
severity, as in the case of file-based threats that appear to have
been blocked or successfully cleaned up by an Anti-Virus solution.
The challenge in taking action against server attacks is that it can
be difficult to assess which of these attacks were successful in
causing actual damage, and for this reason, for the fraction of
server attacks that demonstrate corroborating endpoint security
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alerts, even if of low severity, should be examined. It is
interesting to note the cooperative role played by both network and
endpoint security devices in these instances.
12. Regulatory Considerations
This section will briefly look at the regulatory landscape and
develop a specific view on the impact on endpoints with the goal to
see what we might be able to learn.
Legal requirements, compliance, regulatory frameworks and mandatory
reporting are no longer separate from any security evaluation,
process or requirement within an organisation, enterprise system or
intranet. It is essential to look at the technical and regulatory
approaches together. This section will look at two examples of legal
requirements and guidance:
(1) IoT security (2) Network infrastructure
This section is by no means complete, but it does a discussion on
this aspect of endpoint and ecosystem regulation.
12.1. IoT Security
IoT security regulation is emerging in the form of voluntary
frameworks and self-assessments that relate to endpoint security
issues.. These frameworks focus first on the end point, or mobile
device, in the IoT environment and then on the holistic ecosystem
itself.
In 2017 the National Institute of Standards and Technology released
its draft IoT Cybersecurity Framework based on consultations and
interviews with all stakeholders over several years previously
[NISTIOTP]. Some of the themes which emerged was the need for IoT
governance, assessment frameworks, review of all aspects of the IoT
ecosystem and a process for coordinated vulnerability disclosure
inside an organisation. As evidenced by the 2018 Endpoint Protection
and Response Survey by SANS, only 47% of organisations know that
their endpoints have been breached and a further 20% are unsure
[EPRSANS]. So a systemic approach from NIST was welcomed and the
NIST framework became the gold standard for national IoT security
frameworks.
Other IoT security frameworks include the Singapore IoT Cyber
Security Guide from January 2019 and the UK's Secure by Design or The
Government's Code of Practice for Consumer Internet of Things (IoT)
Security for manufacturers, with guidance for consumers on smart
devices at home [IMDAIOTG], [SBDGOVUK]. Once again both look at
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securing the IoT device or endpoint, but also security for the entire
value chain of the IoT system. The Singapore framework makes the
point about the entire system clear, "Similar to any system, an IoT
system is as secure as its weakest link. It is thus important to
ensure that proper security considerations and measures are put in
place for both the implementation and operational stages of the
deployment of any IoT system." [IMDAIOTG] Finally, the IoT Security
Foundation, the GSMA and the Internet Society have all released their
own frameworks for IoT security. All have similar characteristics
which focus on the entire value chain and ecosystem, but also on
vulnerability disclosure and checklist assessments. What makes each
of these approaches slightly different is the differing perspectives
of the organization advocating it. The GSMA is the mobile trade
association and so it focuses on mobile devices while the Internet
Society focuses on the Internet ecosystem and a multistakeholder
approach. Systematically underpinning all the frameworks is the
holistic approach with voluntary best practices and implementation
based on the needs of the user or organisation adopting the framework
[IOTSFCF], [GSMAIOT], [ISTRUST].
12.2. Network infrastructure
In Europe, the Network and Information Security Directive, which was
passed in July 2016, require implementation by each European member
state with a threefold aim. First, to put into place a national
strategy for network and infrastructure security including best
practices, guidelines, training and stakeholder consultations.
Second, to coordinate national CSIRTs with CERT-EU and third to
provide incident control and response systems for critical
infrastructure and digital services [EURLEX]. This Directive
demonstrates the importance give across the EU to network resilience
and incident reporting. While securing the endpoint is acknowledged,
the focus is on ensuring the security of European interoperable
networks. In short, the importance of the security of the network
including incident response shows that it isn't only the endpoints
that should be the focus of the regulation and legal frameworks.
12.3. Auditing and Assessment
This section will talk about other risk assessment and auditing
regulatory requirements beyond the NIS directive.
One example of risk assessment as a regulatory requirement is the New
York State law 23 NYCRR 500 of the Regulations of the Superintendent
of Financial Services (Cybersecurity Requirements for Financial
Services Companies). Among the requirements, audit, risk assessment
and risk reporting are included like,
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(2) include audit trails designed to detect and respond to
Cybersecurity Events that have a reasonable likelihood of materially
harming any material part of the normal operations of the Covered
Entity. [NYCYBER]
12.4. Privacy Considerations
We may consider a specific focus on privacy in the future.
13. Human Rights Considerations
This section may develop a specific view of requirements, limits and
constraints coming from Human Rights perspective on endpoint
security.
14. Security Considerations
This document is about Security Considerations
15. IANA Considerations
This document has no actions for IANA
16. Informative References
[ADAPTURE]
Cullen, T., "Limits of endpoint only", July 2017,
<https://www.adapture.com/blog/
evaluating-leading-endpoint-security-vendors/>.
[AMT1] Khandelwal, S., "Explained - How Intel AMT Vulnerability
Allows to Hack Computers Remotely", May 2017,
<https://thehackernews.com/2017/05/
intel-amt-vulnerability.html>.
[AMT2] Symantec, ., "Web Attack Intel AMT Privilege Escalation
CVE-2017-5689", 2017,
<https://www.symantec.com/security_response/
attacksignatures/detail.jsp?asid=29888>.
[ATTACK] "MITRE ATT&CK", n.d., <https://attack.mitre.org>.
[BOT] Marinho, R., "Exploring a P2P transient botnet - From
Discovery to Enumeration", July 2017,
<https://morphuslabs.com/exploring-a-p2p-transient-botnet-
from-discovery-to-enumeration-e72870354950>.
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[CANDID1] Wueest, C., "How my TV got infected with ransomware and
what you can learn about it", November 2015,
<https://www.symantec.com/connect/blogs/how-my-tv-got-
infected-ransomware-and-what-you-can-learn-it>.
[CANDID2] Dickson, B., "Millions of smart TVs are vulnerable to
hackers", February 2014,
<https://www.dailydot.com/debug/protect-smart-tv/>.
[CAPEC] "MITRE CAPEC", n.d.,
<https://capec.mitre.org/data/definitions/3000.html>.
[ENISA] ENISA, ., "Baseline Security Recommendations for IoT in
the context of Critical Information Infrastructures",
November 2017, <https://www.enisa.europa.eu/publications/
baseline-security-recommendations-for-iot>.
[EPPEDR] Redscan, ., "EPP and EDR - What's the difference?", June
2018, <https://www.redscan.com/news/
epp-vs-edr-whats-the-difference/>.
[EPPGUIDE]
"IT Pro's Guide to Endpoint Protection", n.d.,
<https://www.barkly.com/
it-pros-guide-to-endpoint-protection>.
[EPPSECURITY]
Hunt, J., "Advantages and Disadvantages of Three Top
Endpoint Security Vendors", n.d.,
<https://www.adapture.com/blog/
evaluating-leading-endpoint-security-vendors/>.
[EPRSANS] Neely, L., "Endpoint Protection and Response A SANS
Survey", June 2018, <https://www.sans.org/reading-
room/whitepapers/clients/paper/38460>.
[ERICSSON]
Ericsson, ., "Internet of Things forecast", n.d.,
<https://www.ericsson.com/en/mobility-report/
internet-of-things-forecast>.
[EURLEX] EUP, ., "Directive (EU) 2016/1148", July 2016,
<https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=urise
rv:OJ.L_.2016.194.01.0001.01.ENG&toc=OJ:L:2016:194:TOC>.
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[FLAMER] Symantec, ., "W32.Flamer Microsoft Windows Update Man-in-
the-Middle", June 2012,
<https://www.symantec.com/connect/blogs/
w32flamer-microsoft-windows-update-man-middle>.
[GARTNERIOT]
Van der Meulen, R., "Gartner Says 8.4 Billion Connected
Things Will be in Use in 2017, Up 31 percent from 2016",
February 2017, <https://www.gartner.com/en/newsroom/press-
releases/2017-02-07-gartner-says-8-billion-connected-
things-will-be-in-use-in-2017-up-31-percent-from-2016>.
[GARTNERREPORT]
Crotty, J., "New Gartner Report Redefines Endpoints
Protection for 2018", January 2018,
<https://www.crowdstrike.com/blog/new-gartner-report-
redefines-endpoint-protection-for-2018/>.
[GSMAIOT] GSMA, ., "GSMA IoT Security Guidelines and Assessment",
n.d., <https://www.gsma.com/iot/iot-security/
iot-security-guidelines/>.
[HSTODAY] Hstoday, ., "Layered Approach Critical to Effective
Endpoint Protection", October 2016,
<https://www.hstoday.us/channels/federal-state-local/
layered-approach-critical-to-effective-endpoint-
protection/>.
[IMDAIOTG]
IMDA, ., "IMDA IoT Cyber Security Guide", January 2019,
<https://www.imda.gov.sg/-/media/imda/files/
regulation-licensing-and-consultations/consultations/
open-for-public-comments/
consultation-for-iot-cyber-security-guide/
imda-iot-cyber-security-guide.pdf>.
[IOTPATCHING]
Rogers, D., "Handling vulnerabilities as an IoT vendor",
December 2018, <https://www.iotsecurityfoundation.org/
less-than-10-of-consumer-iot-companies-follow-
vulnerability-disclosure-guidelines/>.
[IOTSFCF] IoTSF, ., "IoT Security Compliance Framework", December
2018, <https://www.iotsecurityfoundation.org/
best-practice-guidelines/>.
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[ISTRUST] ISOC, ., "Internet of Things (IoT) Trust Framework v2.5",
May 2018,
<https://www.internetsociety.org/resources/doc/2018/
iot-trust-framework-v2-5/>.
[LOJAX] ESET, ., "LoJax First UEFI rootkit found in the wild,
courtesy of the Sednit group", September 2018,
<https://www.welivesecurity.com/2018/09/27/lojax-first-
uefi-rootkit-found-wild-courtesy-sednit-group/>.
[LOTLSYMC]
Wueest, C., "Living off the land and fileless attack
techniques", July 2017,
<https://www.symantec.com/content/dam/symantec/docs/
security-center/white-papers/istr-living-off-the-land-and-
fileless-attack-techniques-en.pd>.
[MIRAI1] Symantec, ., "Mirai, what you need to know about the
botnet behind recent major DDoS attacks", October 2016,
<https://www.symantec.com/connect/blogs/mirai-what-you-
need-know-about-botnet-behind-recent-major-ddos-attacks>.
[MIRAI2] Krebsonsecurity, ., "19 Mirai Botnet Authors Avoid Jail
Time", September 2018,
<https://krebsonsecurity.com/tag/mirai-botnet/>.
[MONEYBALL]
Roundy, K., "Predicting Cyber Threats with Virtual
Security Products. ACSAC", 2017,
<https://www.cc.gatech.edu/~dchau/
papers/17-acsac-moneyball.pdf>.
[NDSSPATCH]
Caballero, J., "Mind Your Own Business A Longitudinal
Study of Threats and Vulnerabilities in Enterprises",
February 2019, <https://www.ndss-symposium.org/wp-
content/uploads/2019/02/
ndss2019_03B-1-2_Kotzias_paper.pdf>.
[NETTODAY]
Dix, J., "Layered Security Defenses What layer is most
critical network or endpoint", July 2011,
<https://www.networkworld.com/article/2220204/tech-
debates/layered-security-defenses--what-layer-is-most-
critical--network-or-endpoint-.html>.
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[NINESIGNS]
Smith, K., "9 signs your endpoint security isn't working",
May 2017, <https://securelement.com/9-signs-your-endpoint-
security-isnt-working/>.
[NISTIOTP]
NIST, ., "NIST Cybersecurity for IoT Program", November
2016, <https://www.nist.gov/programs-projects/
nist-cybersecurity-iot-program>.
[NYCYBER] NYCRR, ., "See 3 NYCRR 500 of the Regulations of the
Superintendent of Financial Services (Cybersecurity
Requirements for Financial Services Companies)", n.d.,
<https://www.dfs.ny.gov/docs/legal/regulations/adoptions/
dfsrf500txt.pdf>.
[OWASP] OWASP, ., "Defense in depth definition", August 2015,
<https://www.owasp.org/index.php/Defense_in_depth>.
[PDDoS] Seals, T., "Pulse-Wave DDoS Attacks Mark a New Tactics in
Q2", October 2017, <https://www.infosecurity-
magazine.com/news/pulsewave-ddos-attacks-mark-q2/>.
[SBDGOVUK]
UK, GOV., "Secure by Design", February 2019,
<https://www.gov.uk/government/collections/
secure-by-design>.
[SGX1] Claburn, T., "Intel SGX safe room easily trashed by white-
hat hacking marauders Enclave malware demoed", February
2019, <https://www.theregister.co.uk/2019/02/12/
intel_sgx_hacked/>.
[SGX2] Cimpanu, C., "Researchers hide malware in Intel SGX
enclaves", February 2019, <https://www.zdnet.com/article/
researchers-hide-malware-in-intel-sgx-enclaves/>.
[SQL] Cobb, M., "SQL injection detection tools and prevention
strategies", November 2009,
<https://www.computerweekly.com/tip/
SQL-injection-detection-tools-and-prevention-strategies>.
[STATISTA1]
Statista, ., "Internet of Things (IoT) connected devices
installed base worldwide from 2015 to 2025 (in billions)",
n.d., <https://www.statista.com/statistics/471264/
iot-number-of-connected-devices-worldwide/>.
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[STATISTA2]
Statista, ., "Size of Internet of Things market worldwide
in 2014 and 2020 by industry (in billion U.S dollars)",
n.d., <https://blogs-
images.forbes.com/louiscolumbus/files/2017/12/
size-of-IoT-Market-globally-2014-to-2020.jpg>.
[TEEP] Cam-Winget, N., "Trust Execution Environment Protocol",
March 2018, <https://datatracker.ietf.org/wg/teep/about>.
[USCERT] Michael, C., "Principles of defense-in-depth", September
2005, <https://www.us-
cert.gov/bsi/articles/knowledge/principles/
defense-in-depth>.
[ZERODAY1]
McHugh, J., "Windows of Vulnerability A Case Study
Analysis", 2000, <http://www.cs.colostate.edu/~cs635/
Windows_of_Vulnerability.pdf>.
[ZERODAY2]
Plattner, B., "Large-Scale Vulnerability Analysis",
September 2006, <http://citeseerx.ist.psu.edu/viewdoc/
download?doi=10.1.1.173.3056&rep=rep1&type=pdf>.
Appendix A. Contributors
o Arnaud Taddei
Symantec
arnaud_taddei@symantec.com
o Bret Jordan
Symantec
bret_jordan@symantec.com
o Candid Wueest
Symantec
candid_wueest@symantec.com
o Chris Larsen
Symantec
chris_larsen@symantec.com
o Andre Engel
Symantec
andre_ngel@symantec.com
o Kevin Roundy
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Symantec
kevin_roundy@symantec.com
o Yuqiong Sun
Symantec
Yuqiong_Sun@symantec.com
o David Wells
Symantec
David_Wells@symantec.com
o Dominique Lazanski
Last Press Label
dml@lastpresslabel.com
Authors' Addresses
Arnaud Taddei
Symantec Corporation
350 Ellis Street
Mountain View CA 94043
USA
Email: arnaud_taddei@symantec.com
Candid Wueest
Symantec Corporation
350 Ellis Street
Mountain View CA 94043
USA
Email: candid_wueest@symantec.com
Kevin A. Roundy
Symantec Corporation
350 Ellis Street
Mountain View CA 94043
USA
Email: kevin_roundy@symantec.com
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Dominique Lazanski
Last Press Label
Flat 1, 109A Columbia Road
London E2 7RL
UK
Email: dml@lastpresslabel.com
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