Internet DRAFT - draft-paine-smart-indicators-of-compromise
draft-paine-smart-indicators-of-compromise
Internet Engineering Task Force K. Paine
Internet-Draft Splunk Inc.
Intended status: Informational O. Whitehouse
Expires: 16 July 2022 NCC Group
J. Sellwood
Twilio
A. Shaw
UK National Cyber Security Centre
12 January 2022
Indicators of Compromise (IoCs) and Their Role in Attack Defence
draft-paine-smart-indicators-of-compromise-04
Abstract
Cyber defenders frequently rely on Indicators of Compromise (IoCs) to
identify, trace, and block malicious activity in networks or on
endpoints. This draft reviews the fundamentals, opportunities,
operational limitations, and best practices of IoC use. It
highlights the need for IoCs to be detectable in implementations of
Internet protocols, tools, and technologies - both for the IoCs'
initial discovery and their use in detection - and provides a
foundation for new approaches to operational challenges in network
security.
Status of This Memo
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Copyright Notice
Copyright (c) 2022 IETF Trust and the persons identified as the
document authors. All rights reserved.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. Requirements Language . . . . . . . . . . . . . . . . . . 3
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
3. IoC Fundamentals . . . . . . . . . . . . . . . . . . . . . . 4
3.1. IoC Types and the Pyramid of Pain . . . . . . . . . . . . 4
3.2. IoC Lifecycle . . . . . . . . . . . . . . . . . . . . . . 8
3.2.1. Discovery . . . . . . . . . . . . . . . . . . . . . . 8
3.2.2. Assessment . . . . . . . . . . . . . . . . . . . . . 9
3.2.3. Sharing . . . . . . . . . . . . . . . . . . . . . . . 9
3.2.4. Deployment . . . . . . . . . . . . . . . . . . . . . 10
3.2.5. Detection . . . . . . . . . . . . . . . . . . . . . . 10
3.2.6. Reaction . . . . . . . . . . . . . . . . . . . . . . 10
3.2.7. End of Life . . . . . . . . . . . . . . . . . . . . . 10
4. Using IoCs Effectively . . . . . . . . . . . . . . . . . . . 10
4.1. Opportunities . . . . . . . . . . . . . . . . . . . . . . 11
4.1.1. IoCs underpin and enable multiple layers of the modern
defence-in-depth strategy . . . . . . . . . . . . . . 11
4.1.2. IoCs can be used even with limited resources . . . . 12
4.1.3. IoCs have a multiplier effect on attack defence
effort . . . . . . . . . . . . . . . . . . . . . . . 12
4.1.4. IoCs are easily shared . . . . . . . . . . . . . . . 13
4.1.5. IoCs can provide significant time savings . . . . . . 13
4.1.6. IoCs allow for discovery of historic attacks . . . . 14
4.1.7. IoCs can be attributed to specific threats . . . . . 14
4.2. Case Studies . . . . . . . . . . . . . . . . . . . . . . 14
4.2.1. Introduction . . . . . . . . . . . . . . . . . . . . 14
4.2.2. Cobalt Strike . . . . . . . . . . . . . . . . . . . . 15
4.2.2.1. Overall TTP . . . . . . . . . . . . . . . . . . . 15
4.2.2.2. IoCs . . . . . . . . . . . . . . . . . . . . . . 15
4.2.3. APT33 . . . . . . . . . . . . . . . . . . . . . . . . 16
4.2.3.1. Overall TTP . . . . . . . . . . . . . . . . . . . 16
4.2.3.2. IoCs . . . . . . . . . . . . . . . . . . . . . . 17
5. Operational Limitations . . . . . . . . . . . . . . . . . . . 17
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5.1. Time and Effort . . . . . . . . . . . . . . . . . . . . . 17
5.1.1. Fragility . . . . . . . . . . . . . . . . . . . . . . 17
5.1.2. Discoverability . . . . . . . . . . . . . . . . . . . 18
5.2. Precision . . . . . . . . . . . . . . . . . . . . . . . . 19
5.2.1. Specificity . . . . . . . . . . . . . . . . . . . . . 19
5.2.2. Dual and Compromised Use . . . . . . . . . . . . . . 20
5.3. Privacy . . . . . . . . . . . . . . . . . . . . . . . . . 20
5.4. Automation . . . . . . . . . . . . . . . . . . . . . . . 21
6. Best Practice . . . . . . . . . . . . . . . . . . . . . . . . 22
6.1. Comprehensive Coverage and Defence-in-Depth . . . . . . . 22
6.2. Security Considerations . . . . . . . . . . . . . . . . . 24
7. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . 24
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 25
9. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 25
10. Informative References . . . . . . . . . . . . . . . . . . . 25
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 27
1. Introduction
This draft describes the various types of Indicator of Compromise
(IoC) and how they are used effectively in attack defence (often
called cyber defence). It introduces concepts such as the Pyramid of
Pain [PoP] and the IoC lifecycle to highlight how IoCs may be used to
provide a broad range of defences. This draft provides best practice
for implementers of controls based on IoCs, as well as potential
operational limitations. Two case studies which demonstrate the
usefulness of IoCs for detecting and defending against real world
attacks are included. One case study involves an intrusion set (a
collection of indicators for a specific attack) known as APT33 and
the other an attack tool called Cobalt Strike. This document is not
a comprehensive report of APT33 or Cobalt Strike and is intended to
be read alongside publicly published reports (referred to as open
source material among intelligence practitioners) on these threats
(for example, [Symantec] and [NCCGroup], respectively).
1.1. Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119].
2. Terminology
Attack defence: the activity of providing cyber security to an
environment through the prevention, detection and response to
attempted and successful cyber intrusions. Successful defence is
achieved through the blocking, monitoring and response to adversarial
activity at a network, endpoint or application levels.
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Command and control (C2) server: an attacker-controlled server used
to communicate with, send commands to and receive data from
compromised machines. Communication between a C2 server and
compromised hosts is called command and control traffic.
Domain Generation Algorithm (DGA): used in malware strains to
generate domain names periodically. Adversaries may use DGAs to
dynamically identify a destination for C2 traffic, rather than
relying on a list of static IP addresses or domains that can be
blocked more easily.
Kill chain: a model for conceptually breaking down a cyber intrusion
to allow defenders to think about, discuss, plan for, and implement
controls to defend discrete phases of an attacker's activity
[KillChain].
Tactics, Techniques, and Procedures (TTPs): the way an adversary
undertakes activities in the kill chain - the choices made, methods
followed, tools and infrastructure used, protocols employed, and
commands executed. If they are distinct enough, aspects of an
attacker's TTPs can form specific Indicators of Compromise (IoCs), as
if they were a fingerprint.
3. IoC Fundamentals
3.1. IoC Types and the Pyramid of Pain
Indicators of Compromise (IoCs) are observable artefacts relating to
an attacker or their activities, such as their tactics, techniques,
procedures, and associated tooling and infrastructure. These
indicators can be observed at network or endpoint (host) levels and
can, with varying degrees of confidence, help network defenders (blue
teams) to pro-actively block malicious traffic or code execution,
determine a cyber intrusion occurred, or associate discovered
activity to a known intrusion set and thereby potentially identify
additional avenues for investigation. Examples of protocol-related
IoCs can include:
* IPv4 and IPv6 addresses in network traffic.
* DNS domain names in network traffic, resolver caches or logs.
* TLS Server Name Indication values in network traffic.
* Code signing certificates in binaries or TLS certificate
information (such as SHA256 hashes) in network traffic.
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* Cryptographic hashes (e.g. MD5, SHA1 or SHA256) of malicious
binaries or scripts when calculated from network traffic or file
system artefacts.
* Attack tools (such as Mimikatz [Mimikatz]) and their code
structure and execution characteristics.
* Attack techniques, such as Kerberos golden tickets [GoldenTicket]
which can be observed in network traffic or system artefacts.
The common types of IoC form a 'Pyramid of Pain' [PoP] that informs
prevention, detection, and mitigation strategies. Each IoC type's
place in the pyramid represents how much 'pain' a typical adversary
experiences as part of changing the activity that produces that
artefact. The greater pain an adversary experiences (towards the
top) the less likely they are to change those aspects of their
activity and the longer the IoC is likely to reflect the attacker's
intrusion set - i.e., the less fragile those IoCs will be from a
defender's perspective. The layers of the PoP commonly range from
hashes up to TTPs, with the pain ranging from simply recompiling code
to creating a whole new attack strategy. Other types of IoC do exist
and could be included in an extended version of the PoP should that
assist the defender to understand and discuss intrusion sets most
relevant to them.
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/\
/ \ MORE PAIN
/ \ LESS FRAGILE
/ \ LESS PRECISE
/ TTPs \
/ \ / \
============== |
/ \ |
/ Tools \ |
/ \ |
====================== |
/ \ |
/ network/host artefacts \ |
/ \ |
============================== |
/ \ |
/ domain names \ |
/ \ |
====================================== |
/ \ |
/ IP addresses \ |
/ \ \ /
==============================================
/ \ LESS PAIN
/ Hash values \ MORE FRAGILE
/ \ MORE PRECISE
======================================================
Figure 1
On the lowest (and least painful) level are hashes of malicious
files. These are easy for a defender to gather and can be deployed
to firewalls or endpoint protection to block malicious downloads or
prevent code execution. While IoCs aren't the only way for defenders
to do this kind of blocking, they are a quick, convenient, and
unintrusive method. Hashes are precise detections for individual
files based on their binary content. To subvert this defence,
however, an adversary need only recompile code, or otherwise modify
the file content with some trivial changes, to modify the hash value.
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The next two levels are IP addresses and domain names. Interactions
with these may be blocked, with varying false positive rates
(misidentifying non-malicious traffic as malicious, see Section 5),
and often cause more pain to an adversary to subvert than file
hashes. The adversary may have to change IP ranges, find a new
provider, and change their code (e.g., if the IP address is hard-
coded, rather than resolved). Domain names are more specific than IP
addresses (as multiple domain names may be associated with a single
IP address) and are more painful for an adversary to change.
Network and endpoint artefacts, such as a malware's beaconing pattern
on the network or the modified timestamps of files touched on an
endpoint, are harder still to change as they relate specifically to
the attack taking place and, in some cases, may not be under the
direct control of the attacker. However, more sophisticated
attackers use TTPs or tooling that provide flexibility at this level
(such as Cobalt Strike's malleable command and control [COBALT]) or a
means by which some artefacts can be masked (see [Timestomp]).
Tools and TTPs form the top two levels of the pyramid; these levels
describe a threat actor's methodology - the way they perform the
attack. The tools level refers specifically to the software (and
less frequently hardware) used to conduct the attack, whereas the
TTPs level picks up on all the other aspects of the attack strategy.
IoCs at these levels are more complicated and complex - for example
they can include the details of how an attacker deploys malicious
code to perform reconnaissance of a victim's network, that pivots
laterally to a valuable endpoint, and then downloads a ransomware
payload. TTPs and tools take intensive effort to diagnose on the
part of the defender, but they are fundamental to the attacker and
campaign and hence incredibly painful for the adversary to change.
The variation in discoverability of IoCs is indicated by the numbers
of IoCs in the open threat intelligence community Alienvault
[ALIENVAULT]. As of June 2021, Alienvault contained:
* Groups (i.e., combinations of TTPs): 441
* Malware families (i.e., tools): ~24,000
* URL: 1,976,224
* Domain names: 34,959,787
* IPv4 addresses: 4,305,036
* SHA256 hash values: 4,767,891
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The number of domain names appears out of sync with the other counts,
which reduce on the way up the PoP. This discrepancy warrants
further research; however, a contributing factor may be the fact that
threat actors use domain names to masquerade as legitimate
organisations and so have added incentive for creating new domain
names as they are identified and confiscated.
3.2. IoC Lifecycle
To be of use to defenders, IoCs must first be discovered, assessed,
shared, and deployed. When a logged activity is identified and
correlated to an IoC this detection triggers a reaction by the
defender which may include an investigation, potentially leading to
more IoCs being discovered, assessed, shared, and deployed. This
cycle continues until such time that the IoC is determined to no
longer be relevant, at which point it is removed from the control
space.
3.2.1. Discovery
IoCs are often discovered initially through manual investigation or
automated analysis. They can be discovered in a range of sources,
including in networks and at endpoints. They must either be
extracted from logs monitoring protocol runs, code execution or
system operations (in the case of hashes, IP addresses, domain names,
and network or endpoint artefacts), or be determined through analysis
of attack activity or tooling. In some cases, discovery may be a
reactive process, where IoCs from past or current attacks are
identified from the traces left behind. However, discovery may also
result from proactive hunting for potential future IoCs extrapolated
from knowledge of past events (such as from identifying attacker
infrastructure by monitoring domain name registration patterns).
Crucially, for an IoC to be discovered, the indicator must be
extractable from the internet protocol, tool, or technology it is
associated with. Identifying a particular protocol run related to an
attack is of limited benefit if indicators cannot be extracted and
subsequently associated with a later related run of the same, or a
different, protocol. If it is not possible to tell the source or
destination of malicious attack traffic, it will not be possible to
identify and block subsequent attack traffic either.
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3.2.2. Assessment
Defenders may treat different IoCs differently, depending on the
IoCs' quality and the defender's needs and capabilities. Defenders
may, for example, place differing trust in IoCs depending on their
source, freshness, confidence level, or the associated threat. These
decisions rely on associated contextual information recovered at the
point of discovery or provided when the IoC was shared.
An IoC without context is not much use for network defence. On the
other hand, an IoC delivered with context (for example the threat
actor it relates to, its role in an attack, the last time it was seen
in use, its expected lifetime, or other related IoCs) allows a
network defender to make an informed choice on how to use it to
protect their network - for example, whether to simply log it,
actively monitor it, or out-right block it.
3.2.3. Sharing
Once discovered and assessed, IoCs are most helpful when then shared
at scale so many individuals and organisations can defend themselves.
An IoC may be shared individually (with appropriate context) in an
unstructured manner or may be packaged alongside many other IoCs in a
standardised format, such as Structured Threat Information Expression
[STIX], for distribution via a structured feed, such as one
implementing Trusted Automated Exchange of Intelligence Information
[TAXII], or through a Malware Information Sharing Platform [MISP].
While some security companies and some membership-based groups (often
dubbed Information Sharing and Analysis Centres (ISACs)) provide paid
intel feeds containing IoCs, there are various free IoC sources
available from individual security researchers up through small trust
groups to national governmental cyber security organisations and
international Computer Emergency Response Teams (CERTs). Whomever
they are, sharers commonly indicate the extent to which receivers may
further distribute IoCs using the Traffic Light Protocol [TLP]. At
its simplest, this indicates that the receiver may share with anyone
(TLP WHITE), share within the defined sharing community (TLP GREEN),
share within their organisation (TLP AMBER), or not share with anyone
outside the original specific IoC exchange (TLP RED).
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3.2.4. Deployment
For IoCs to provide defence-in-depth (see Section 6.1), which is one
of their key strengths, and so cope with different points of failure,
they should be deployed in controls monitoring networks and endpoints
through solutions that have sufficient privilege to act on them.
Wherever IoCs exist they need to be made available to security
controls and associated apparatus to ensure they can be deployed
quickly and widely. While IoCs may be manually assessed after
discovery or receipt, significant advantage may be gained by
automatically ingesting, processing, assessing, and deploying IoCs
from logs or intel feeds to the appropriate security controls.
3.2.5. Detection
Security controls with deployed IoCs monitor their relevant control
space and trigger a generic or specific reaction upon detection of
the IoC in monitored logs.
3.2.6. Reaction
The reaction to an IoC's detection may differ depending on factors
such as the capabilities and configuration of the control it is
deployed in, the assessment of the IoC, and the properties of the log
source in which it was detected. For example, a connection to a
known botnet C2 server may indicate a problem but does not guarantee
it, particularly if the server is a compromised host still performing
some other legitimate functions. Common reactions include event
logging, triggering alerts, and blocking or terminating the source of
the activity.
3.2.7. End of Life
How long an IoC remains useful varies and is dependent on factors
including initial confidence level, fragility, and precision of the
IoC (discussed further in Section 5). In some cases, IoCs may be
automatically 'aged' based on their initial characteristics and so
will reach end of life at a predetermined time. In other cases, IoCs
may become invalidated due to a shift in the threat actor's TTPs
(e.g., resulting from a new development or their discovery) or due to
remediation action taken by a defender. End of life may also come
about due to an activity unrelated to attack or defence, such as when
a third-party service used by the attacker changes or goes offline.
Whatever the cause, IoCs should be removed from detection at the end
of their life to reduce the likelihood of false positives.
4. Using IoCs Effectively
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4.1. Opportunities
IoCs offer a variety of opportunities to cyber defenders as part of a
modern defence-in-depth strategy. No matter the size of an
organisation, IoCs can provide an effective, scalable, and efficient
defence mechanism against classes of attack from the latest threats
or specific intrusion sets which may have struck in the past.
4.1.1. IoCs underpin and enable multiple layers of the modern defence-
in-depth strategy
Firewalls, Intrusion Detection Systems (IDS), and Intrusion
Prevention Systems (IPS) all employ IoCs to identify and mitigate
threats across networks. Anti-Virus (AV) and Endpoint Detection and
Response (EDR) products deploy IoCs via catalogues or libraries to
all supported client endpoints. Security Incident Event Management
(SIEM) platforms compare IoCs against aggregated logs from various
sources - network, endpoint, and application. Of course, IoCs do not
address all attack defence challenges - but they form a vital tier of
any organisation's layered defence. Some types of IoC may be present
across all those controls while others may be deployed only in
certain layers. Further, IoCs relevant to a specific kill chain may
only reflect activity performed during a certain phase and so need to
be combined with other IoCs or mechanisms for complete coverage of
the kill chain as part of an intrusion set.
As an example, open source malware can be deployed by many different
actors, each using their own TTPs and infrastructure. However, if
the actors use the same executable, the hash remains the same and
this IoC can be deployed in endpoint protection to block execution
regardless of individual actor, infrastructure, or other TTPs.
Should this defence fail in a specific case, for example if an actor
recompiles the executable binary producing a unique hash, other
defences can prevent them progressing further through their attack -
for instance, by blocking known malicious domain name look-ups and
thereby preventing the malware calling out to its C2 infrastructure.
Alternatively, another malicious actor may regularly change their
tools and infrastructure (and thus the indicator intrusion set)
deployed across different campaigns, but their access vectors may
remain consistent and well-known. In this case, this access TTP can
be recognised and proactively defended against even while there is
uncertainty of the intended subsequent activity. For example, if
their access vector consistently exploits a vulnerability in
software, regular and estate-wide patching can prevent the attack
from taking place. Should these pre-emptive measures fail however,
other IoCs observed across multiple campaigns may be able to prevent
the attack at later stages in the kill chain.
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4.1.2. IoCs can be used even with limited resources
IoCs are inexpensive, scalable, and easy to deploy, making their use
particularly beneficial for smaller entities, especially where they
are exposed to a significant threat. For example, a small
manufacturing subcontractor in a supply chain producing a critical,
highly specialised component may represent an attractive target
because there would be disproportionate impact on both the supply
chain and the prime contractor if it were compromised. It may be
reasonable to assume that this small manufacturer will have only
basic security (whether internal or outsourced) and while it is
likely to have comparatively less resources to manage the risks it
faces compared to larger partners, it can still leverage IoCs to
great effect. Small entities like this can deploy IoCs to give a
baseline protection against known threats without having access to a
well-resourced, mature defensive team and the threat intelligence
relationships necessary to perform resource-intensive investigations.
One reason for this is that use of IoCs does not require the same
intensive training as needed for more subjective controls, such as
those based on manual analysis of tipped machine learning events. In
this way, a major part of the appeal of IoCs is that they can afford
some level of protection to organisations across spectrums of
resource capability, maturity, and sophistication.
4.1.3. IoCs have a multiplier effect on attack defence effort
Individual IoCs can provide widespread protection that scales
effectively for defenders. Within a single organisation, simply
blocking one IoC may protect thousands of users and that blocking may
be performed (depending on the IoC type) across multiple security
controls monitoring numerous different types of activity within
networks, endpoints, and applications. While discovering one IoC can
be intensive, once shared via well-established routes (as discussed
in Section 3.2.2) that individual IoC may, further, protect thousands
of organisations and so all of their users. The prime contractor
from our earlier example can supply IoCs to the small subcontractor
and so further uplift that smaller entity's defensive capability and
at the same time protect itself and its interests.
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Not only may multiple organisations benefit through directly
receiving shared IoCs, but they may also benefit through the IoCs'
application in services they utilise. In the case of an ongoing
email phishing campaign, IoCs can be monitored, discovered, and
deployed quickly and easily by individual organisations. However, if
they are deployed quickly via a mechanism such as a protective DNS
filtering service, they can be more effective still - an email
campaign may be mitigated before some organisations' recipients ever
click the link or before some malicious payloads can call out for
instructions. Through such approaches other parties can be protected
without additional effort.
4.1.4. IoCs are easily shared
There is significant benefit to be had from the sharing of IoCs and
they can be easily shared for two main reasons: firstly, indicators
are easy to distribute as they are textual and so in small numbers
are frequently exchanged in emails, blog posts, or technical reports;
secondly, standards such as MISP Core [MISPCORE], OpenIOC [OPENIOC],
and STIX [STIX] provide well-defined formats for sharing large
collections or regular sets of IoC along with all the associated
context. Quick and easy sharing of IoCs gives blanket coverage for
organisations and allows widespread mitigation in a timely fashion -
they can be shared with systems administrators, from small to large
organisations and from large teams to single individuals, allowing
them all to implement defences on their networks.
4.1.5. IoCs can provide significant time savings
Not only are there time savings from sharing IoCs, saving duplication
of investigation effort, but deploying them automatically at scale is
seamless for many enterprises. Where automatic deployment of IoCs is
working well, organisations and users get blanket protection with
minimal human intervention and minimal effort, a key goal of attack
defence. The ability to do this at scale and at pace is often vital
when responding to agile threat actors that may change their
intrusion set frequently and so the relevant IoCs also change.
Conversely, protecting a complex network without automatic deployment
of IoCs could mean manually updating every single endpoint or network
device consistently and reliably to the same security state. The
work this entails (including locating assets and devices, polling for
logs and system information, and manually checking patch levels)
introduces complexity and a need for skilled analysts and engineers.
While it is still necessary to invest effort to eliminate false
positives when widely deploying IoCs, the cost and effort involved
can be far smaller than the work entailed in reliably manually
updating all endpoint and network devices - for example, particularly
on legacy systems that may be particularly complicated, or even
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impossible, to update.
4.1.6. IoCs allow for discovery of historic attacks
A network defender can use recently acquired IoCs in conjunction with
historic data, such as logged DNS queries or email attachment hashes,
to hunt for signs of past compromise. Not only can this technique
help to build up a clear picture of past attacks, but it also allows
for retrospective mitigation of the effects of any previous
intrusion. This opportunity is reliant on historic data not having
been compromised itself, by a technique such as Timestomp
[Timestomp], and not being incomplete due to data retention policies,
but is nonetheless valuable for detecting and remediating past
attacks.
4.1.7. IoCs can be attributed to specific threats
Deployment of various modern security controls, such as firewall
filtering or EDR, come with an inherent trade-off between breadth of
protection and various costs, including the risk of false positives
(see Section 5.2 ), staff time, and pure financial costs.
Organisations can use threat modelling and information assurance to
assess and prioritise risk from identified threats and to determine
how they will mitigate or accept each of them. Contextual
information tying IoCs to specific threats or actors and shared
alongside the IoCs enables organisations to focus their defences
against particular risks and so allows them the technical freedom and
capability to choose their risk posture and defence methods.
Producing this contextual information before sharing IoCs can take
intensive analytical effort as well as specialist tools and training.
At its simplest it can involve documenting sets of IoCs from multiple
instances of the same attack campaign, say from multiple unique
payloads (and therefore with distinct file hashes) from the same
source and connecting to the same C2 server. A more complicated
approach is to cluster similar combinations of TTPs seen across
multiple campaigns over a period of time. This can be used alongside
detailed malware reverse engineering and target profiling, overlaid
on a geopolitical and criminal backdrop, to infer attribution to a
single threat actor.
4.2. Case Studies
4.2.1. Introduction
The following two case studies illustrate how IoCs may be identified
in relation to threat actor tooling (in the first) and a threat actor
campaign (in the second). The case studies further highlight how
these IoCs may be used by cyber defenders.
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4.2.2. Cobalt Strike
Cobalt Strike [COBALT] is a commercial attack framework that consists
of an implant framework (beacon), network protocol, and a C2 server.
The beacon and network protocol are highly malleable, meaning the
protocol representation 'on the wire' can be easily changed by an
attacker to blend in with legitimate traffic. The proprietary beacon
supports TLS encryption overlaid with a custom encryption scheme
based on a public-private keypair. The product also supports other
techniques, such as domain fronting [DFRONT], in attempt to avoid
obvious passive detection by static network signatures.
4.2.2.1. Overall TTP
A beacon configuration describes how the implant should operate and
communicate with its C2 server. This configuration also provides
ancillary information such as the Cobalt Strike user's licence
watermark.
4.2.2.2. IoCs
Tradecraft has been developed that allows the fingerprinting of C2
servers based on their responses to specific requests. This allows
the servers to be identified and then their beacon configurations to
be downloaded and the associated infrastructure addresses extracted
as IoCs.
The resulting mass IoCs for Cobalt Strike are:
* IP addresses of the C2 servers
* domain names used
Whilst these IoCs need to be refreshed regularly (due to the ease of
which they can be changed), the authors' experience of protecting
public sector organisations show these IoCs are effective for
disrupting threat actor operations that use Cobalt Strike.
These IoCs can be used to check historical data for evidence of past
compromise, as well as deployed to detect or block future infection
in a timely manner, thereby contributing to preventing the loss of
user and system data.
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4.2.3. APT33
In contrast to the first case study, this describes a current
campaign by the threat actor APT33, also known as Elfin and Refined
Kitten (see [Symantec]). APT33 has been assessed by industry to be a
state-sponsored group [FireEye2], yet in this case study, IoCs still
gave defenders an effective tool against such a powerful adversary.
The group has been active since at least 2015 and is known to target
a range of sectors including petrochemical, government, engineering,
and manufacturing. Activity has been seen in countries across the
globe, but predominantly in the USA and Saudi Arabia.
4.2.3.1. Overall TTP
The techniques employed by this actor exhibit a relatively low level
of sophistication considering it is a state-sponsored group;
typically, APT33 performs spear phishing (sending targeted malicious
emails to a limited number of pre-selected recipients) with document
lures that imitate legitimate publications. User interaction with
these lures executes the initial payload and enables APT33 to gain
initial access. Once inside a target network, APT33 attempts to
pivot to other machines to gather documents and gain access to
administrative credentials. In some cases, users are tricked into
providing credentials that are then used with RULER, a freely
available tool that allows exploitation of an email client. The
attacker, in possession of a target's password, uses RULER to access
the target's mail account and embeds a malicious script which will be
triggered when the mail client is next opened, resulting in the
execution of malicious code (often additional malware retrieved from
the Internet) (see [FireEye]).
APT33 sometimes deploys a destructive tool which overwrites the
master boot record (MBR) of the hard drives in as many PCs as
possible. This type of tool, known as a wiper, results in data loss
and renders devices unusable until the operating system is
reinstalled. In some cases, the actor uses administrator credentials
to invoke execution across a large swathe of a company's IT estate at
once; where this isn't possible the actor may attempt to spread the
wiper first manually or by using worm-like capabilities against
unpatched vulnerabilities on the networked computers.
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4.2.3.2. IoCs
As a result of investigations by a partnership of industry and the
UK's National Cyber Security Centre (NCSC), a set of IoCs were
compiled and shared with both public and private sector organisations
so network defenders could search for them in their networks.
Detection of these IoCs is likely indicative of APT33 targeting and
could indicate potential compromise and subsequent use of destructive
malware. Network defenders could also initiate processes to block
these IoCs to foil future attacks. This set of IoCs comprised:
* 9 hashes and email subject lines
* 5 IP addresses
* 7 domain names
5. Operational Limitations
The different IoC types inherently embody a set of trade-offs for
defenders between the risk of false positives (misidentifying non-
malicious traffic as malicious) and the risk of failing to identify
attacks. The attacker's relative pain of modifying attacks to
subvert known IoCs, as discussed using the Pyramid of Pain (PoP) in
Section 3.1, inversely correlates with the fragility of the IoC and
with the precision with which the IoC identifies an attack. Research
is needed to elucidate the exact nature of these trade-offs between
pain, fragility, and precision.
5.1. Time and Effort
5.1.1. Fragility
As alluded to in Section 3.1, the Pyramid of Pain can be thought of
in terms of fragility for the defender as well as pain for the
attacker. The less painful it is for the attacker to change an IoC,
the more fragile that IoC is as a defence tool. It is relatively
simple to determine the hash value for various malicious file
attachments observed as lures in a phishing campaign and to deploy
these through AV or an email gateway security control. However,
those hashes are fragile and can (and often will) be changed between
campaigns. Malicious IP addresses and domain names can also be
changed between campaigns, but this happens less frequently due to
the greater pain of managing infrastructure compared to altering
files, and so IP addresses and domain names provide a less fragile
detection capability.
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This does not mean the more fragile IoC types are worthless.
Firstly, there is no guarantee a fragile IoC will change, and if a
known IoC isn't changed by the attacker but wasn't blocked then the
defender missed an opportunity to halt an attack in its tracks.
Secondly, even within one IoC type, there is variation in the
fragility depending on the context of the IoC. The file hash of a
phishing lure document (with a particular theme and containing a
specific staging server link) may be more fragile than the file hash
of a remote access trojan payload the attacker uses after initial
access. That in turn may be more fragile than the file hash of an
attacker-controlled post-exploitation reconnaissance tool that
doesn't connect directly to the attacker's infrastructure. Thirdly,
some threats and actors are more capable or inclined to change than
others, and so the fragility of an IoC for one may be very different
to an IoC of the same type for another actor.
Ultimately, fragility is a defender's concern that impacts the
ongoing efficacy of each IoC and will factor into decisions about end
of life. However, it should not prevent adoption of individual IoCs
unless there are significantly strict resource constraints that
demand down-selection of IoCs for deployment. More usually,
defenders researching threats will attempt to identify IoCs of
varying fragilities for a particular kill chain to provide the
greatest chances of ongoing detection given available investigative
effort (see Section 5.1.2) and while still maintaining precision (see
Section 5.2).
Finally, it is worth noting that fragility can apply to an entire
class of IoCs for a range of reasons; for example, IPv4 addresses are
becoming increasingly fragile due to addresses growing scarce,
widespread use of cloud services, and the ease with which domain
names can be moved from one hosting provider to another (thus
changing IP range).
5.1.2. Discoverability
To be used in attack defence, IoCs must first be discovered through
proactive hunting or reactive investigation. As noted in
Section 3.1, IoCs in the tools and TTPs levels of the PoP require
intensive effort and research to discover. However, it is not just
an IoC's type that impacts its discoverability. The sophistication
of the actor, their TTPs, and their tooling play a significant role,
as does whether the IoC is retrieved from logs after the attack or
extracted from samples or infected systems earlier.
For example, on an infected endpoint it may be possible to identify a
malicious payload and then extract relevant IoCs, such as the file
hash and its C2 server address. If the attacker used the same static
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payload throughout the attack this single file hash value will cover
all instances. If, however, the attacker diversified their payloads,
that hash can be more fragile and other hashes may need to be
discovered from other samples used on other infected endpoints.
Concurrently, the attacker may have simply hard-coded configuration
data into the payload, in which case the C2 server address can be
easy to recover. Alternatively, the address can be stored in an
obfuscated persistent configuration either within the payload (e.g.,
within its source code or associated resource) or the infected
endpoint's filesystem (e.g., using alternative data streams [ADS])
and thus requiring more effort to discover. Further, the attacker
may be storing the configuration in memory only or relying on a
domain generation algorithm (DGA) to generate C2 server addresses on
demand. In this case, extracting the C2 server address can require a
memory dump or the execution or reverse engineering of the DGA, all
of which increase the effort still further.
If the malicious payload has already communicated with its C2 server,
then it may be possible to discover that C2 server address IoC from
network traffic logs more easily. However, once again multiple
factors can make discoverability more challenging, such as the
increasing adoption of HTTPS for malicious traffic - meaning C2
communications blend in with legitimate traffic, and can be
complicated to identify. Further, some malwares obfuscate their
intended destinations by using alternative DNS resolution services
(e.g., OpenNIC [OPENNIC]) or by performing transformation operations
on resolved IP addresses to determine the real C2 server address
encoded in the DNS response [LAZARUS].
5.2. Precision
5.2.1. Specificity
Alongside pain and fragility, the PoP's levels can also be considered
in terms of how precise the defence can be, with the false positive
rate usually increasing as we move up the pyramid to less specific
IoCs. A hash value identifies a particular file, such as an
executable binary, and given a suitable cryptographic hash function
the false positives are effectively nil; by suitable we mean one with
preimage resistance and strong collision resistance. In comparison,
IoCs in the upper levels (such as some network artefacts or tool
fingerprints) may apply to various malicious binaries, and even
benign software may share the same identifying characteristics. For
example, threat actor tools making web requests may be identified by
the user-agent string specified in the request header. However, this
value may be the same as used by legitimate software, either by the
attacker's choice or through use of a common library.
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It should come as no surprise that the more specific an IoC the more
fragile it is - as things change, they move outside of that specific
focus. While less fragile IoCs may be desirable for their robustness
and longevity, this must be balanced with the increased chance of
false positives from their broadness. One way in which this balance
is achieved is by grouping indicators and using them in combination.
While two low-specificity IoCs for a particular attack may each have
chances of false positives, when observed together they may provide
greater confidence of an accurate detection of the relevant kill
chain.
5.2.2. Dual and Compromised Use
As noted in Section 3.2.2, the context of an IoC, such as the way in
which the attacker uses it, may equally impact the precision with
which that IoC detects an attack. An IP address representing an
attacker's staging server, from which their attack chain downloads
subsequent payloads, offers a precise IP address for attacker-owned
infrastructure. However, it will be less precise if that IP address
is associated with a cloud hosting provider and it is regularly
reassigned from one user to another; and it will be less precise
still if the attacker compromised a legitimate web server and is
abusing the IP address alongside the ongoing legitimate use.
In a similar manner, a file hash representing an attacker's custom
remote access trojan will be very precise; however, a file hash
representing a common enterprise remote administration tool will be
less precise depending on whether the defender organisation usually
uses that tool for legitimate systems administration or not.
Notably, such dual use indicators are context specific both in
whether they are usually used legitimately and in the way they are
used in a particular circumstance. Use of the remote administration
tool may be legitimate for support staff during working hours, but
not generally by non-support staff, particularly if observed outside
of that employee's usual working hours.
It is reasons such as these that context is so important when sharing
and using IoCs.
5.3. Privacy
As noted in Section 3.2.2, context is critical to effective detection
using IoCs. However, at times, defenders may feel there are privacy
concerns with how much to share about a cyber intrusion, and with
whom. For example, defenders may generalise the IoCs' description of
the attack, by removing context to facilitate sharing. This
generalisation can result in an incomplete set of IoCs being shared
or IoCs being shared without clear indication of what they represent
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and how they are involved in an attack. The sharer will consider the
privacy trade-off when generalising the IoC, and should bear in mind
that the loss of context can greatly reduce the utility of the IoC
for those they share with.
Self-censoring by sharers appears more prevalent and more extensive
when sharing IoCs into groups with more members, into groups with a
broader range of perceived member expertise (particularly the further
the lower bound extends below the sharer's perceived own expertise),
and into groups that do not maintain strong intermember trust. Trust
within such groups appears often strongest where members: interact
regularly; have common backgrounds, expertise, or challenges; conform
to behavioural expectations (such as by following defined handling
requirements and not misrepresenting material they share); and
reciprocate the sharing and support they receive. Research
opportunities exist to determine how IoC sharing groups' requirements
for trust and members' interaction strategies vary and whether
sharing can be optimised or incentivised, such as by using game
theoretic approaches.
5.4. Automation
While IoCs can be effectively utilised by organisations of various
sizes and resource constraints, as discussed in Section 4.1.2,
automation of IoC ingestion, processing, assessment, and deployment
is critical for managing them at scale. Manual oversight and
investigation may be necessary intermittently, but a reliance on
manual processing and searching only works at small scale or for
occasional cases.
The adoption of automation can also enable faster and easier
correlation of IoC detections across log sources, time, and space.
Thereby, the response can be tailored to reflect the number and
overlap of detections from a particular intrusion set, and the
necessary context can be presented alongside the detection when
generating any alerts for defender review. While manual processing
and searching may be no less accurate (although IoC transcription
errors are a common problem during busy incidents), the correlation
and cross-referencing necessary to provide the same degree of
situational awareness is much more time consuming.
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A third important consideration when performing manual processing is
the longer phase monitoring and adjustment necessary to effectively
age out IoCs as they become irrelevant or, more crucially,
inaccurate. Manual implementations must often simply include or
exclude an IoC, as anything more granular is time consuming and
complicated to manage. In contrast, automations can support a
gradual reduction in confidence scoring enabling IoCs to contribute
but not individually disrupt a detection as their specificity
reduces.
6. Best Practice
6.1. Comprehensive Coverage and Defence-in-Depth
IoCs provide the defender with a range of options across the Pyramid
of Pain's (PoP) layers, enabling them to balance precision and
fragility to give high confidence detections that are practical and
useful. Broad coverage of the PoP is important as it allows the
defender to cycle between high precision but high fragility options
and more robust but less precise indicators. As fragile indicators
are changed, the more robust IoCs allow for continued detection and
faster rediscovery. For this reason, it's important to collect as
many IoCs as possible across the whole PoP.
At the top of the PoP, TTPs identified through anomaly detection and
machine learning are more likely to have false positives, which gives
lower confidence and, vitally, requires better trained analysts to
understand and implement the defences. However, these are very
painful for attackers to change and so when tuned appropriately
provide a robust detection. Hashes, at the bottom, are precise and
easy to deploy but are fragile and easily changed within and across
campaigns by malicious actors.
Endpoint Detection and Response (EDR) or Anti-Virus (AV) are often
the first port of call for protection from intrusion but endpoint
solutions aren't a panacea. One issue is that there are many
environments where it is not possible to keep them updated, or in
some cases, deploy them at all. For example, the Owari botnet, a
Mirai variant [Owari], exploited Internet of Things (IoT) devices
where such solutions could not be deployed. It is because of such
gaps, where endpoint solutions can't be relied on (see [EVOLVE]),
that a defence-in-depth approach is commonly advocated, using a
blended approach that includes both network and endpoint defences.
If an attack happens, then you hope an endpoint solution will pick it
up. If it doesn't, it could be for many good reasons: the endpoint
solution could be quite conservative and aim for a low false-positive
rate; it might not have ubiquitous coverage; or it might only be able
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to defend the initial step of the kill chain [KillChain]. In the
worst cases, the attack specifically disables the endpoint solution
or the malware is brand new and so won't be recognised.
In the middle of the pyramid, IoCs related to network information
(such as domains and IP addresses) can be particularly useful. They
allow for broad coverage, without requiring each and every endpoint
security solution to be updated, as they may be detected and enforced
in a more centralised manner at network choke points (such as proxies
and gateways). This makes them particular useful in contexts where
ensuring endpoint security isn't possible such as "Bring Your Own
Device" (BYOD), Internet of Things (IoT) and legacy environments.
It's important to note that these network-level IoCs can also protect
against compromised endpoints when these IoCs used to detect the
attack in network traffic, even if the compromise passes unnoticed.
For example, in a BYOD environment, enforcing security policies on
the device can be difficult, so non-endpoint IoCs and solutions are
needed to allow detection of compromise even with no endpoint
coverage.
One example of how IoCs provide a layer of a defence-in-depth
solution is Protective DNS (PDNS), a free and voluntary DNS filtering
service provided by the UK NCSC for UK public sector organisations
[PDNS]. In 2018, this service blocked access to 57.4 million DNS
queries for 118,527 unique reasons (out of 68.7 billion total
queries) for the organisations signed up to the service [ACD2019]. 28
million of them were for domain generation algorithms (DGAs) [DGAs],
including 15 known DGAs which are a type of TTP.
IoCs such as malicious domains can be put on PDNS straight away and
can then be used to prevent access to those known malicious domains
across the entire estate of over 460 separate public sector entities
that use NCSC's PDNS [Annual2019]. Coverage can be patchy with
endpoints, as the roll-out of protections isn't uniform or
necessarily fast - but if the IoC is on PDNS, a consistent defence is
maintained. This offers protection, regardless of whether the
context is a BYOD environment or a managed enterprise system. Other
IoCs, like Server Name Indicator values in TLS or the server
certificate information, also provide IoC protections.
Similar to the AV scenario, large scale services face risk decisions
around balancing threat against business impact from false positives.
Organisations need to be able to retain the ability to be more
conservative with their own defences, while still benefiting from
them. For instance, a commercial DNS filtering service is intended
for broad deployment, so will have a risk tolerance similar to AV
products; whereas DNS filtering intended for government users (e.g.
PDNS) can be more conservative, but will still have a relatively
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broad deployment if intended for the whole of government. A
government department or specific company, on the other hand, might
accept the risk of disruption and arrange firewalls or other network
protection devices to completely block anything related to particular
threats, regardless of the confidence, but rely on a DNS filtering
service for everything else.
Other network defences can make use of this blanket coverage from
IoCs, like middlebox mitigation, proxy defences, and application
layer firewalls, but are out of scope for this draft. Note too that
DNS goes through firewalls, proxies and possibly to a DNS filtering
service; it doesn't have to be unencrypted, but these appliances must
be able to decrypt it to do anything useful with it, like blocking
queries for known bad URIs.
Covering a broad range of IoCs gives defenders a wide range of
benefits: they are easy to deploy; they provide a high enough
confidence to be effective; at least some will be painful for
attackers to change; their distribution around the infrastructure
allows for different points of failure, and so overall they enable
the defenders to disrupt bad actors. The combination of these
factors cements IoCs as a particularly valuable tool for defenders
with limited resources.
6.2. Security Considerations
This draft is all about system security. However, when poorly
deployed, IoCs can lead to over-blocking which may present an
availability concern for some systems. While IoCs preserve privacy
on a macro scale (by preventing data breaches), research could be
done to investigate the impact on privacy from sharing IoCs, and
improvements could be made to minimise any impact found. The
creation of a privacy-preserving IoC sharing method, that still
allows both network and endpoint defences to provide security and
layered defences, would be an interesting proposal.
7. Conclusions
IoCs are versatile and powerful. IoCs underpin and enable multiple
layers of the modern defence-in-depth strategy. IoCs are easy to
share, providing a multiplier effect on attack defence effort and
they save vital time. Network-level IoCs offer protection,
especially valuable when an endpoint-only solution isn't sufficient.
These properties, along with their ease of use, make IoCs a key
component of any attack defence strategy and particularly valuable
for defenders with limited resources.
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For IoCs to be useful, they don't have to be unencrypted or visible
in networks - but crucially they do need to be made available, along
with their context, to entities that need them. It is also important
that this availability and eventual usage copes with multiple points
of failure, as per the defence-in-depth strategy, of which IoCs are a
key part.
8. IANA Considerations
This draft does not require any IANA action.
9. Acknowledgements
Thanks to all those who have been involved with improving cyber
defence in the IETF and IRTF communities.
10. Informative References
[ACD2019] Levy, I. and M. S, "Active Cyber Defence - The Second
Year", 2019, <https://www.ncsc.gov.uk/report/active-cyber-
defence-report-2019>.
[ADS] Microsoft, "File Streams (Local File Systems)", 2018,
<https://docs.microsoft.com/en-us/windows/win32/fileio/
file-streams>.
[ALIENVAULT]
AlienVault, "AlienVault", 2021,
<https://otx.alienvault.com/>.
[Annual2019]
NCSC, "Annual Review 2019", 2019,
<https://www.ncsc.gov.uk/annual-review/2019/ncsc/docs/
ncsc_2019-annual-review.pdf>.
[COBALT] Cobalt Strike, "OVERRULED: Containing a Potentially
Destructive Adversary", 2021,
<https://www.cobaltstrike.com/>.
[DFRONT] InfoSec Resources, "Domain Fronting", 2017,
<https://resources.infosecinstitute.com/topic/domain-
fronting/>.
[DGAs] MITRE, "Dynamic Resolution: Domain Generation Algorithms",
2020, <https://attack.mitre.org/techniques/T1483/>.
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[EVOLVE] McFadden, M., "Evolution of Endpoint Security - An
Operational Perspective", 2021,
<https://datatracker.ietf.org/doc/draft-mcfadden-opsec-
endp-evolve/>.
[FireEye] O'Leary, J., Kimble, J., Vanderlee, K., and N. Fraser,
"Insights into Iranian Cyber Espionage: APT33 Targets
Aerospace and Energy Sectors and has Ties to Destructive
Malware", 2017, <https://www.fireeye.com/blog/threat-
research/2017/09/apt33-insights-into-iranian-cyber-
espionage.html>.
[FireEye2] FireEye, "OVERRULED: Containing a Potentially Destructive
Adversary", 2018, <https://www.fireeye.com/blog/threat-
research/2018/12/overruled-containing-a-potentially-
destructive-adversary.html>.
[GoldenTicket]
Soria-Machado, M., Abolins, D., Boldea, C., and K. Socha,
"Kerberos Golden Ticket Protection", 2014,
<https://cert.europa.eu/static/WhitePapers/UPDATED - CERT-
EU_Security_Whitepaper_2014-007_Kerberos_Golden_Ticket_Pro
tection_v1_4.pdf>.
[KillChain]
Lockheed Martin, "The Cyber Kill Chain", 2020,
<https://www.lockheedmartin.com/en-us/capabilities/cyber/
cyber-kill-chain.html>.
[LAZARUS] Kaspersky Lab, "Lazarus Under The Hood", 2018,
<https://media.kasperskycontenthub.com/wp-
content/uploads/sites/43/2018/03/07180244/
Lazarus_Under_The_Hood_PDF_final.pdf>.
[Mimikatz] Mulder, J., "Mimikatz Overview, Defenses and Detection",
2016, <https://www.sans.org/reading-
room/whitepapers/detection/mimikatz-overview-defenses-
detection-36780>.
[MISP] MISP, "MISP", 2019, <https://www.misp-project.org/>.
[MISPCORE] MISP, "MISP Core", 2020, <https://github.com/MISP/misp-
rfc/blob/master/misp-core-format/raw.md.txt>.
[NCCGroup] Jansen, W., "Abusing cloud services to fly under the
radar", 2021, <https://research.nccgroup.com/2021/01/12/
abusing-cloud-services-to-fly-under-the-radar/>.
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[OPENIOC] Gibb, W., "OpenIOC: Back to the Basics", 2013,
<https://www.fireeye.com/blog/threat-research/2013/10/
openioc-basics.html>.
[OPENNIC] OpenNIC Project, "OpenNIC Project", 2021,
<https://www.opennic.org/>.
[Owari] NCSC, "Owari botnet own-goal takeover", 2018,
<https://www.ncsc.gov.uk/report/weekly-threat-report-8th-
june-2018>.
[PDNS] NCSC, "Protective DNS", 2019,
<https://www.ncsc.gov.uk/information/pdns>.
[PoP] Bianco, D.J., "The Pyramid of Pain", 2014,
<https://detect-respond.blogspot.com/2013/03/the-pyramid-
of-pain.html>.
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[STIX] OASIS Cyber Threat Intelligence, "STIX", 2019,
<https://oasis-open.github.io/cti-documentation/stix/
intro>.
[Symantec] Symantec, "Elfin: Relentless", 2019,
<https://www.symantec.com/blogs/threat-intelligence/elfin-
apt33-espionage>.
[TAXII] OASIS Cyber Threat Intelligence, "TAXII", 2021,
<https://oasis-open.github.io/cti-documentation/taxii/
intro.html>.
[Timestomp]
OASIS Cyber Threat Intelligence, "Timestomp", 2019,
<https://attack.mitre.org/techniques/T1099/>.
[TLP] FIRST, "Traffic Light Protocol", 2021,
<https://www.first.org/tlp/>.
Authors' Addresses
Kirsty Paine
Splunk Inc.
Email: kirsty.ietf@gmail.com
Paine, et al. Expires 16 July 2022 [Page 27]
Internet-Draft Indicators of Compromise January 2022
Ollie Whitehouse
NCC Group
Email: ollie.whitehouse@nccgroup.com
James Sellwood
Twilio
Email: jsellwood@twilio.com
Andrew Shaw
UK National Cyber Security Centre
Email: andrew.s2@ncsc.gov.uk
Paine, et al. Expires 16 July 2022 [Page 28]