| Network Working Group | J. Hall |
| Internet-Draft | Internet Society |
| Intended status: Informational | M. Aaron |
| Expires: September 5, 2020 | CU Boulder |
| S. Adams | |
| CDT | |
| B. Jones | |
| N. Feamster | |
| Princeton | |
| March 04, 2020 |
A Survey of Worldwide Censorship Techniques
draft-irtf-pearg-censorship-01
This document describes the technical mechanisms used by censorship regimes around the world to block or impair Internet traffic. It aims to make designers, implementers, and users of Internet protocols aware of the properties being exploited and mechanisms used to censor end-user access to information. This document makes no suggestions on individual protocol considerations, and is purely informational, intended to be a reference.
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Censorship is where an entity in a position of power – such as a government, organization, or individual – suppresses communication that it considers objectionable, harmful, sensitive, politically incorrect or inconvenient. (Although censors that engage in censorship must do so through legal, military, or other means, this document focuses largely on technical mechanisms used to achieve network censorship.)
This document describes the technical mechanisms that censorship regimes around the world use to block or degrade Internet traffic (see [RFC7754] for a discussion of Internet blocking and filtering in terms of implications for Internet architecture, rather than end-user access to content and services).
We describe three elements of Internet censorship – prescription, identification, and interference – and structure the document in three major sections corresponding to each. Prescription is the process by which censors determine what types of material they should block, i.e. they decide to block a list of pornographic websites. Identification is the process by which censors classify specific traffic to be blocked or impaired, i.e. the censor blocks or impairs all webpages containing “sex” in the title or traffic to www.sex.example. Interference is the process by which the censor intercedes in communication and prevents access to censored materials by blocking access or impairing the connection.
Prescription is the process of figuring out what censors would like to block [Glanville-2008]. Generally, censors aggregate information “to block” in blocklists or using real-time heuristic assessment of content [Ding-1999]. There are indications that online censors are starting to use machine learning techniques as well [Tang-2016].
There are typically three types of blocklists: Keyword, domain name, or Internet Protocol (IP) address. Keyword and domain name blocking take place at the application level (e.g. HTTP), whereas IP blocking tends to take place using routing data in IPv4/IPv6 headers. The mechanisms for building up these blocklists are varied. Censors can purchase from private industry “content control” software, such as SmartFilter, which allows filtering from broad categories that they would like to block, such as gambling or pornography. In these cases, these private services attempt to categorize every semi-questionable website as to allow for meta-tag blocking (similarly, they tune real-time content heuristic systems to map their assessments onto categories of objectionable content).
Countries that are more interested in retaining specific political control, a desire which requires swift and decisive action, often have ministries or organizations, such as the Ministry of Industry and Information Technology in China or the Ministry of Culture and Islamic Guidance in Iran, which maintain their own blocklists.
Internet censorship, necessarily, takes place over a network. Network design gives censors a number of different points-of-control where they can identify the content they are interested in filtering. An important aspect of pervasive technical interception is the necessity to rely on software or hardware to intercept the content the censor is interested in. This requirement, the need to have the interception mechanism located somewhere, logically or physically, implicates various general points-of-control:
At all levels of the network hierarchy, the filtration mechanisms used to detect undesirable traffic are essentially the same: a censor either directly sniffs transmitting packets and identifies undesirable content, and then uses a blocking or shaping mechanism to prevent or impair access, or requests that an actor ancillary to the censor, such as a private entity, performs these functions. Identification of undesirable traffic can occur at the application, transport, or network layer of the IP stack. Censors are almost always concerned with web traffic, so the relevant protocols tend to be filtered in predictable ways. For example, a subversive image would always make it past a keyword filter, but the IP address of the site serving the image may be blocklisted when identified as a provider of undesirable content.
An HTTP header contains a lot of useful information for traffic identification; although “host” is the only required field in an HTTP request header (for HTTP/1.1 and later), an HTTP method field is necessary to do anything useful. As such, “method” and “host” are the two fields used most often for ubiquitous censorship. A censor can sniff traffic and identify a specific domain name (host) and usually a page name (GET /page) as well. This identification technique is usually paired with TCP/IP header identification (see Section 3.3.1) for a more robust method.
Tradeoffs: Request Identification is a technically straight-forward identification method that can be easily implemented at the Backbone or ISP level. The hardware needed for this sort of identification is cheap and easy-to-acquire, making it desirable when budget and scope are a concern. HTTPS will encrypt the relevant request and response fields, so pairing with TCP/IP identification (see Section 3.3.1) is necessary for filtering of HTTPS. However, some countermeasures such as URL obfuscation can trivially defeat simple forms of HTTP Request Header Identification (for example, using a simple character rotation technique, ROT-13, where the current letter is replaced by the one 13 characters ahead of it in the alphabet, a URL such as http://ice.citizenlab.org becomes uggc://vpr.pvgvmrayno.bet).
Empirical Examples: Studies exploring censorship mechanisms have found evidence of HTTP header/ URL filtering in many countries, including Bangladesh, Bahrain, China, India, Iran, Malaysia, Pakistan, Russia, Saudi Arabia, South Korea, Thailand, and Turkey [Verkamp-2012] [Nabi-2013] [Aryan-2012]. Commercial technologies such as the McAfee SmartFilter and NetSweeper are often purchased by censors [Dalek-2013]. These commercial technologies use a combination of HTTP Request Identification and TCP/IP Header Identification to filter specific URLs. Dalek et al. and Jones et al. identified the use of these products in the wild [Dalek-2013] [Jones-2014].
While HTTP Request Header Identification relies on the information contained in the HTTP request from client to server, response identification uses information sent in response by the server to client to identify undesirable content.
Tradeoffs: As with HTTP Request Header Identification, the techniques used to identify HTTP traffic are well-known, cheap, and relatively easy to implement, but is made useless by HTTPS, because the response in HTTPS is encrypted, including headers.
The response fields are also less helpful for identifying content than request fields, as “Server” could easily be identified using HTTP Request Header identification, and “Via” is rarely relevant. HTTP Response censorship mechanisms normally let the first n packets through while the mirrored traffic is being processed; this may allow some content through and the user may be able to detect that the censor is actively interfering with undesirable content.
Empirical Examples: In 2009, Jong Park et al. at the University of New Mexico demonstrated that the Great Firewall of China (GFW) has used this technique [Crandall-2010]. However, Jong Park et al. found that the GFW discontinued this practice during the course of the study. Due to the overlap in HTTP response filtering and keyword filtering (see Section 3.2.3), it is likely that most censors rely on keyword filtering over TCP streams instead of HTTP response filtering.
Many governments pressure content providers to censor themselves, or provide the legal framework within which content distributors are incentivised to follow the content restriction preferences of agents external to the content distributor [Boyle-1997]. Due to the extensive reach of such censorship, we need to define content distributor as any service that provides utility to users, including everything from web sites to locally installed programs. A commonly used method of instrumenting content distributors consists of keyword identification to detect restricted terms on their platform. The terms on such keyword lists may be provided by the government or the content provider may be expected to come up with their own list. A different method of instrumenting content distributors consists in requiring a distributor to disassociate with some categories of users. (See also Section 6.4.)
Tradeoffs: By instrumenting content distributors to identify restricted content or content providers, the censor can gain new information at the cost of political capital with the companies it forces or encourages to participate in censorship. For example, the censor can gain insight about the content of encrypted traffic by coercing web sites to identify restricted content. Coercing content distributors to regulate users, categories of users, content and content providers may encourage users and content providers to exhibit self-censorship, an additional advantage for censors. The tradeoffs for instrumenting content distributors are highly dependent on the content provider and the requested assistance. The typical concern is that the targeted keywords or categories of users are too broad, risk being too broadly applied or are not subjected to a sufficiently robust legal process prior to their mandatory application (see p. 8 of [EC-2012]).
Empirical Examples: Researchers have discovered keyword identification by content providers on platforms ranging from instant messaging applications [Senft-2013] to search engines [Rushe-2015] [Cheng-2010] [Whittaker-2013] [BBC-2013] [Condliffe-2013]. To demonstrate the prevalence of this type of keyword identification, we look to search engine censorship.
Search engine censorship demonstrates keyword identification by content providers and can be regional or worldwide. Implementation is occasionally voluntary, but normally is based on laws and regulations of the country a search engine is operating in. The keyword blocklists are most likely maintained by the search engine provider. China is known to require search engine providers to “voluntarily” maintain search term blocklists to acquire/keep an Internet content provider (ICP) license [Cheng-2010]. It is clear these blocklists are maintained by each search engine provider based on the slight variations in the intercepted searches [Zhu-2011] [Whittaker-2013]. The United Kingdom has been pushing search engines to self-censor with the threat of litigation if they don’t do it themselves: Google and Microsoft have agreed to block more than 100,000 queries in U.K. to help combat abuse [BBC-2013] [Condliffe-2013]. European Union law, as well as US law, requires modification of search engine results in response to either copyright, trademark, data protection or defamation concerns [EC-2012].
Depending on the output, search engine keyword identification may be difficult or easy to detect. In some cases specialized or blank results provide a trivial enumeration mechanism, but more subtle censorship can be difficult to detect. In February 2015, Microsoft’s search engine, Bing, was accused of censoring Chinese content outside of China [Rushe-2015] because Bing returned different results for censored terms in Chinese and English. However, it is possible that censorship of the largest base of Chinese search users, China, biased Bing’s results so that the more popular results in China (the uncensored results) were also more popular for Chinese speakers outside of China.
Disassociation by content distributors from certain categories of users has happened for instance in Spain, as a result of the conflict between the Catalunyan independence movement and the Spanish legal presumption of a unitary state [Lomas-2019]. E-sport event organizers have also disassociated themselves from top players who expressed political opinions in relation to the 2019 Hong Kong protests [Victor-2019]. See also Section 4.3.2.
Deep Packet Inspection has become computationally feasible as a censorship mechanism in recent years [Wagner-2009]. Unlike other techniques, DPI reassembles network flows to examine the application “data” section, as opposed to only the header, and is therefore often used for keyword identification. DPI also differs from other identification technologies because it can leverage additional packet and flow characteristics, i.e. packet sizes and timings, to identify content. To prevent substantial quality of service (QoS) impacts, DPI normally analyzes a copy of data while the original packets continue to be routed. Typically, the traffic is split using either a mirror switch or fiber splitter, and analyzed on a cluster of machines running Intrusion Detection Systems (IDS) configured for censorship.
Tradeoffs: DPI is one of the most expensive identification mechanisms and can have a large QoS impact [Porter-2010]. When used as a keyword filter for TCP flows, DPI systems can cause also major overblocking problems. Like other techniques, DPI is less useful against encrypted data, though DPI can leverage unencrypted elements of an encrypted data flow (e.g., the Server Name Indicator (SNI) sent in the clear for TLS) or statistical information about an encrypted flow (e.g., video takes more bandwidth than audio or textual forms of communication) to identify traffic.
Other kinds of information can be inferred by comparing certain unencrypted elements exchanged during TLS handshakes to similar data points from known sources. This practice, called TLS fingerprinting, allows a probabilistic identification of a party’s operating system, browser, or application based on a comparison of the specific combinations of TLS version, ciphersuites, compression options, etc. sent in the ClientHello message to similar signatures found in unencrypted traffic [Husak-2016].
Despite these problems, DPI is the most powerful identification method and is widely used in practice. The Great Firewall of China (GFW), the largest censorship system in the world, has used DPI to identify restricted content over HTTP and DNS and inject TCP RSTs and bad DNS responses, respectively, into connections [Crandall-2010] [Clayton-2006] [Anonymous-2014].
Empirical Examples: Several studies have found evidence of DPI being used to censor content and tools. Clayton et al. Crandal et al., Anonymous, and Khattak et al., all explored the GFW and Khattak et al. even probed the firewall to discover implementation details like how much state it stores [Crandall-2010] [Clayton-2006] [Anonymous-2014] [Khattak-2013]. The Tor project claims that China, Iran, Ethiopia, and others must have used DPI to block the obfs2 protocol [Wilde-2012]. Malaysia has been accused of using targeted DPI, paired with DDoS, to identify and subsequently knockout pro-opposition material [Wagstaff-2013]. It also seems likely that organizations not so worried about blocking content in real-time could use DPI to sort and categorically search gathered traffic using technologies such as NarusInsight [Hepting-2011].
In encrypted connections using Transport Layer Security (TLS), there may be servers that host multiple “virtual servers” at a given network address, and the client will need to specify in the (unencrypted) Client Hello message which domain name it seeks to connect to (so that the server can respond with the appropriate TLS certificate) using the Server Name Indication (SNI) TLS extension [RFC6066]. Since SNI is sent in the clear, censors and filtering software can use it as a basis for blocking, filtering, or impairment by dropping connections to domains that match prohibited content (e.g., bad.foo.example may be censored while good.foo.example is not) [Shbair-2015].
Domain fronting has been one popular way to avoid identification by censors [Fifield-2015]. To avoid identification by censors, applications using domain fronting put a different domain name in the SNI extension than the one encrypted by HTTPS. The visible SNI would indicate an unblocked domain, while the blocked domain remains hidden in the encrypted application header. Some encrypted messaging services relied on domain fronting to enable their provision in countries employing SNI-based filtering. These services used the cover provided by domains for which blocking at the domain level would be undesirable to hide their true domain names. However, the companies holding the most popular domains have since reconfigured their software to prevent this practice. It may be possible to achieve similar results using potential future options to encrypt SNI in TLS 1.3.
Tradeoffs: Some clients do not send the SNI extension (e.g., clients that only support versions of SSL and not TLS) or will fall back to SSL if a TLS connection fails, rendering this method ineffective. In addition, this technique requires deep packet inspection techniques that can be computationally and infrastructurally expensive and improper configuration of an SNI-based block can result in significant overblocking, e.g., when a second-level domain like populardomain.example is inadvertently blocked. In the case of encrypted SNI, pressure to censor may transfer to other points of intervention, such as content and application providers.
Empirical Examples: There are many examples of security firms that offer SNI-based filtering products [Trustwave-2015] [Sophos-2015] [Shbair-2015], and the governments of China, Egypt, Iran, Qatar, South Korea, Turkey, Turkmenistan, and the UAE all do widespread SNI filtering or blocking [OONI-2018] [OONI-2019] [NA-SK-2019] [CitizenLab-2018] [Gatlan-2019].
Of the various shallow packet inspection methods, TCP/IP Header Identification is the most pervasive, reliable, and predictable type of identification. TCP/IP headers contain a few invaluable pieces of information that must be transparent for traffic to be successfully routed: destination and source IP address and port. Destination and Source IP are doubly useful, as not only does it allow a censor to block undesirable content via IP blocklisting, but also allows a censor to identify the IP of the user making the request. Port is useful for allowlisting certain applications.
Trade-offs: TCP/IP identification is popular due to its simplicity, availability, and robustness.
TCP/IP identification is trivial to implement, but is difficult to implement in backbone or ISP routers at scale, and is therefore typically implemented with DPI. Blocklisting an IP is equivalent to installing a /32 route on a router and due to limited flow table space, this cannot scale beyond a few thousand IPs at most. IP blocking is also relatively crude, leading to overblocking, and cannot deal with some services like Content Distribution Networks (CDN), that host content at hundreds or thousands of IP addresses. Despite these limitations, IP blocking is extremely effective because the user needs to proxy their traffic through another destination to circumvent this type of identification.
Port-blocking is generally not useful because many types of content share the same port and it is possible for censored applications to change their port. For example, most HTTP traffic goes over port 80, so the censor cannot differentiate between restricted and allowed content solely on the basis of port. Port allowlisting is occasionally used, where a censor limits communication to approved ports, such as 80 for HTTP traffic and is most effective when used in conjunction with other identification mechanisms. For example, a censor could block the default HTTPS port, port 443, thereby forcing most users to fall back to HTTP.
Censors sometimes identify entire protocols to be blocked using a variety of traffic characteristics. For example, Iran impairs the performance of HTTPS traffic, a protocol that prevents further analysis, to encourage users to switch to HTTP, a protocol that they can analyze [Aryan-2012]. A simple protocol identification would be to recognize all TCP traffic over port 443 as HTTPS, but more sophisticated analysis of the statistical properties of payload data and flow behavior, would be more effective, even when port 443 is not used [Hjelmvik-2010] [Sandvine-2014].
If censors can detect circumvention tools, they can block them, so censors like China are extremely interested in identifying the protocols for censorship circumvention tools. In recent years, this has devolved into an arms race between censors and circumvention tool developers. As part of this arms race, China developed an extremely effective protocol identification technique that researchers call active probing or active scanning.
In active probing, the censor determines whether hosts are running a circumvention protocol by trying to initiate communication using the circumvention protocol. If the host and the censor successfully negotiate a connection, then the censor conclusively knows that host is running a circumvention tool. China has used active scanning to great effect to block Tor [Winter-2012].
Trade-offs: Protocol Identification necessarily only provides insight into the way information is traveling, and not the information itself.
Protocol identification is useful for detecting and blocking circumvention tools, like Tor, or traffic that is difficult to analyze, like VoIP or SSL, because the censor can assume that this traffic should be blocked. However, this can lead to over-blocking problems when used with popular protocols. These methods are expensive, both computationally and financially, due to the use of statistical analysis, and can be ineffective due to their imprecise nature.
Empirical Examples: Protocol identification can be easy to detect if it is conducted in real time and only a particular protocol is blocked, but some types of protocol identification, like active scanning, are much more difficult to detect. Protocol identification has been used by Iran to identify and throttle SSH traffic to make it unusable [Anonymous-2007] and by China to identify and block Tor relays [Winter-2012]. Protocol Identification has also been used for traffic management, such as the 2007 case where Comcast in the United States used RST injection to interrupt BitTorrent Traffic [Winter-2012].
There are a variety of mechanisms that censors can use to block or filter access to content by altering responses from the DNS [AFNIC-2013] [ICANN-SSAC-2012], including blocking the response, replying with an error message, or responding with an incorrect address.
“DNS mangling” is a network-level technique where an incorrect IP address is returned in response to a DNS query to a censored destination. An example of this is what some Chinese networks do (we are not aware of any other wide-scale uses of mangling). On those Chinese networks, every DNS request in transit is examined (presumably by network inspection technologies such as DPI) and, if it matches a censored domain, a false response is injected. End users can see this technique in action by simply sending DNS requests to any unused IP address in China (see example below). If it is not a censored name, there will be no response. If it is censored, an erroneous response will be returned. For example, using the command-line dig utility to query an unused IP address in China of 192.0.2.2 for the name “www.uncensored.example” compared with “www.censored.example” (censored at the time of writing), we get an erroneous IP address “198.51.100.0” as a response:
% dig +short +nodnssec @192.0.2.2 A www.uncensored.example ;; connection timed out; no servers could be reached % dig +short +nodnssec @192.0.2.2 A www.censored.example 198.51.100.0
There are also cases of what is colloquially called “DNS lying”, where a censor mandates that the DNS responses provided – by an operator of a recursive resolver such as an Internet access provider – be different than what authoritative resolvers would provide [Bortzmayer-2015].
DNS cache poisoning refers to a mechanism where a censor interferes with the response sent by an authoritative DNS resolver to a recursive resolver by responding more quickly than the authoritative resolver can respond with an alternative IP address [Halley-2008]. Cache poisoning occurs after the requested site’s name servers resolve the request and attempt to forward the true IP back to the requesting device; on the return route the resolved IP is recursively cached by each DNS server that initially forwarded the request. During this caching process if an undesirable keyword is recognized, the resolved IP is “poisoned” and an alternative IP (or NXDOMAIN error) is returned more quickly than the upstream resolver can respond, causing an erroneous IP address to be cached (and potentially recursively so). The alternative IPs usually direct to a nonsense domain or a warning page. Alternatively, Iranian censorship appears to prevent the communication en-route, preventing a response from ever being sent [Aryan-2012].
Trade-offs: These forms of DNS interference require the censor to force a user to traverse a controlled DNS hierarchy (or intervening network on which the censor serves as a Active Pervasive Attacker [RFC7624] to rewrite DNS responses) for the mechanism to be effective. It can be circumvented by using alternative DNS resolvers (such as the public DNS resolvers provided by Google, OpenDNS, Telcomix, or FDN) that may fall outside of the jurisdictional control of the censor, or Virtual Private Network (VPN) technology. DNS mangling and cache poisoning also imply returning an incorrect IP to those attempting to resolve a domain name, but in some cases the destination may be technically accessible; over HTTP, for example, the user may have another method of obtaining the IP address of the desired site and may be able to access it if the site is configured to be the default server listening at this IP address. Target blocking has also been a problem, as occasionally users outside of the censors region will be directed through DNS servers or DNS-rewriting network equipment controlled by a censor, causing the request to fail. The ease of circumvention paired with the large risk of content blocking and target blocking make DNS interference a partial, difficult, and less than ideal censorship mechanism. Additionally, the above mechanisms rely on DNSSEC not being deployed or DNSSEC validation not being active on the client or recursive resolver.
Empirical Examples: DNS interference, when properly implemented, is easy to identify based on the shortcomings identified above. Turkey relied on DNS interference for its country-wide block of websites such Twitter and YouTube for almost week in March of 2014 but the ease of circumvention resulted in an increase in the popularity of Twitter until Turkish ISPs implementing an IP blocklist to achieve the governmental mandate [Zmijewki-2014]. Ultimately, Turkish ISPs started hijacking all requests to Google and Level 3’s international DNS resolvers [Zmijewki-2014]. DNS interference, when incorrectly implemented, has resulted in some of the largest “censorship disasters”. In January 2014, China started directing all requests passing through the Great Fire Wall to a single domain, dongtaiwang.com, due to an improperly configured DNS poisoning attempt; this incident is thought to be the largest Internet-service outage in history [AFP-2014] [Anon-SIGCOMM12]. Countries such as China, Iran, Turkey, and the United States have discussed blocking entire TLDs as well, but only Iran has acted by blocking all Israeli (.il) domains [Albert-2011]. DNS-blocking is commonly deployed in European countries to deal with undesirable content, such as child abuse content (Norway, United Kingdom, Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Malta, the Netherlands, Poland, Spain and Sweden [Wright-2013] [Eneman-2010]), online gambling (Belgium, Bulgaria, Czech Republic, Cyprus, Denmark, Estonia, France, Greece, Hungary, Italy, Latvia, Lithuania, Poland, Portugal, Romania, Slovakia, Slovenia, Spain (see Section 6.3.2 of: [EC-gambling-2012], [EC-gambling-2019])), copyright infringement (all EEA countries), hate-speech and extremism (France [Hertel-2015]) and terrorism content (France [Hertel-2015]). Many European DNS filtering systems function as described in section 6.2.
While other interference techniques outlined in this section mostly focus on blocking or preventing access to content, it can be an effective censorship strategy in some cases to not entirely block access to a given destination, or service but instead degrade the performance of the relevant network connection. The resulting user experience for a site or service under performance degradation can be so bad that users opt to use a different site, service, or method of communication, or may not engage in communication at all if there are no alternatives. Traffic shaping techniques that rate-limit the bandwidth available to certain types of traffic is one example of a performance degradation.
Trade offs: While implementing a performance degradation will not always eliminate the ability of people to access a desire resource, it may force them to use other means of communication where censorship (or surveillance) is more easily accomplished.
Empirical Examples: Iran has been known to shape the bandwidth available to HTTPS traffic to encourage unencrypted HTTP traffic [Aryan-2012].
Packet dropping is a simple mechanism to prevent undesirable traffic. The censor identifies undesirable traffic and chooses to not properly forward any packets it sees associated with the traversing undesirable traffic instead of following a normal routing protocol. This can be paired with any of the previously described mechanisms so long as the censor knows the user must route traffic through a controlled router.
Trade offs: Packet Dropping is most successful when every traversing packet has transparent information linked to undesirable content, such as a Destination IP. One downside Packet Dropping suffers from is the necessity of blocking all content from otherwise allowable IPs based on a single subversive sub-domain; blogging services and github repositories are good examples. China famously dropped all github packets for three days based on a single repository hosting undesirable content [Anonymous-2013]. The need to inspect every traversing packet in close to real time also makes Packet Dropping somewhat challenging from a QoS perspective.
Empirical Examples: Packet Dropping is a very common form of technical interference and lends itself to accurate detection given the unique nature of the time-out requests it leaves in its wake. The Great Firewall of China has been observed using packet dropping as one of its primary mechanisms of technical censorship [Ensafi-2013]. Iran has also used Packet Dropping as the mechanisms for throttling SSH [Aryan-2012]. These are but two examples of a ubiquitous censorship practice.
Packet injection, generally, refers to a man-in-the-middle (MITM) network interference technique that spoofs packets in an established traffic stream. RST packets are normally used to let one side of TCP connection know the other side has stopped sending information, and thus the receiver should close the connection. RST Packet Injection is a specific type of packet injection attack that is used to interrupt an established stream by sending RST packets to both sides of a TCP connection; as each receiver thinks the other has dropped the connection, the session is terminated.
Trade-offs: Although ineffective against non-TCP protocols (QUIC, IPSec), RST Packet Injection has a few advantages that make it extremely popular as a censorship technique. RST Packet Injection is an out-of-band interference mechanism, allowing the avoidance of the the QoS bottleneck one can encounter with inline techniques such as Packet Dropping. This out-of-band property allows a censor to inspect a copy of the information, usually mirrored by an optical splitter, making it an ideal pairing for DPI and Protocol Identification [Weaver-2009] (this asynchronous version of a MITM is often called a Man-on-the-Side (MOTS)). RST Packet Injection also has the advantage of only requiring one of the two endpoints to accept the spoofed packet for the connection to be interrupted.
The difficult part of RST Packet Injection is spoofing “enough” correct information to ensure one end-point accepts a RST packet as legitimate; this generally implies a correct IP, port, and (TCP) sequence number. Sequence number is the hardest to get correct, as [RFC0793] specifies an RST Packet should be in-sequence to be accepted, although the RFC also recommends allowing in-window packets as “good enough”. This in-window recommendation is important, as if it is implemented it allows for successful Blind RST Injection attacks [Netsec-2011]. When in-window sequencing is allowed, It is trivial to conduct a Blind RST Injection, a blind injection implies the censor doesn’t know any sensitive (encrypted) sequencing information about the TCP stream they are injecting into, they can simply enumerate the ~70000 possible windows; this is particularly useful for interrupting encrypted/obfuscated protocols such as SSH or Tor. RST Packet Injection relies on a stateful network, making it useless against UDP connections. RST Packet Injection is among the most popular censorship techniques used today given its versatile nature and effectiveness against all types of TCP traffic.
Empirical Examples: RST Packet Injection, as mentioned above, is most often paired with identification techniques that require splitting, such as DPI or Protocol Identification. In 2007, Comcast was accused of using RST Packet Injection to interrupt traffic it identified as BitTorrent [Schoen-2007], this later led to a US Federal Communications Commission ruling against Comcast [VonLohmann-2008]. China has also been known to use RST Packet Injection for censorship purposes. This interference is especially evident in the interruption of encrypted/obfuscated protocols, such as those used by Tor [Winter-2012].
Distributed Denial of Service attacks are a common attack mechanism used by “hacktivists” and malicious hackers, but censors have used DDoS in the past for a variety of reasons. There is a huge variety of DDoS attacks [Wikip-DoS], but on a high level two possible impacts tend to occur; a flood attack results in the service being unusable while resources are being spent to flood the service, a crash attack aims to crash the service so resources can be reallocated elsewhere without “releasing” the service.
Trade-offs: DDoS is an appealing mechanism when a censor would like to prevent all access to undesirable content, instead of only access in their region for a limited period of time, but this is really the only uniquely beneficial feature for DDoS as a censorship technique. The resources required to carry out a successful DDoS against major targets are computationally expensive, usually requiring renting or owning a malicious distributed platform such as a botnet, and imprecise. DDoS is an incredibly crude censorship technique, and appears to largely be used as a timely, easy-to-access mechanism for blocking undesirable content for a limited period of time.
Empirical Examples: In 2012 the U.K.’s GCHQ used DDoS to temporarily shutdown IRC chat rooms frequented by members of Anonymous using the Syn Flood DDoS method; Syn Flood exploits the handshake used by TCP to overload the victim server with so many requests that legitimate traffic becomes slow or impossible [Schone-2014] [CERT-2000]. Dissenting opinion websites are frequently victims of DDoS around politically sensitive events in Burma [Villeneuve-2011]. Controlling parties in Russia [Kravtsova-2012], Zimbabwe [Orion-2013], and Malaysia [Muncaster-2013] have been accused of using DDoS to interrupt opposition support and access during elections. In 2015, China launched a DDoS attack using a true MITM system collocated with the Great Firewall, dubbed “Great Cannon”, that was able to inject JavaScript code into web visits to a Chinese search engine that commandeered those user agents to send DDoS traffic to various sites [Marczak-2015].
While it is perhaps the crudest of all censorship techniques, there is no more effective way of making sure undesirable information isn’t allowed to propagate on the web than by shutting off the network. The network can be logically cut off in a region when a censoring body withdraws all of the Boarder Gateway Protocol (BGP) prefixes routing through the censor’s country.
Trade-offs: The impact to a network disconnection in a region is huge and absolute; the censor pays for absolute control over digital information with all the benefits the Internet brings; this is never a long-term solution for any rational censor and is normally only used as a last resort in times of substantial unrest.
Empirical Examples: Network Disconnections tend to only happen in times of substantial unrest, largely due to the huge social, political, and economic impact such a move has. One of the first, highly covered occurrences was with the Junta in Myanmar employing Network Disconnection to help Junta forces quash a rebellion in 2007 [Dobie-2007]. China disconnected the network in the Xinjiang region during unrest in 2009 in an effort to prevent the protests from spreading to other regions [Heacock-2009]. The Arab Spring saw the the most frequent usage of Network Disconnection, with events in Egypt and Libya in 2011 [Cowie-2011] [Cowie-2011b], and Syria in 2012 [Thomson-2012]. Russia has indicated that it will attempt to disconnect all Russian networks from the global internet in April 2019 as part of a test of the nation’s network independence. Reports also indicate that, as part of the test disconnect, Russian telecom firms must now route all traffic to state-operated monitoring points [Cimpanu-2019]. India was the country that saw the largest number of internet shutdowns per year in 2016 and 2017 [Dada-2017].
As the name implies, sometimes manpower is the easiest way to figure out which content to block. Manual Filtering differs from the common tactic of building up blocklists in that it doesn’t necessarily target a specific IP or DNS, but instead removes or flags content. Given the imprecise nature of automatic filtering, manually sorting through content and flagging dissenting websites, blogs, articles and other media for filtration can be an effective technique. This filtration can occur on the Backbone/ISP level – China’s army of monitors is a good example [BBC-2013b] – but more commonly manual filtering occurs on an institutional level. Internet Content Providers such as Google or Weibo, require a business license to operate in China. One of the prerequisites for a business license is an agreement to sign a “voluntary pledge” known as the “Public Pledge on Self-discipline for the Chinese Internet Industry”. The failure to “energetically uphold” the pledged values can lead to the ICPs being held liable for the offending content by the Chinese government [BBC-2013b].
Self-censorship is one of the most interesting and effective types of censorship; a mix of Bentham’s Panopticon, cultural manipulation, intelligence gathering, and offline enforcement. Simply put, self-censorship is when a censor creates an atmosphere where users censor themselves. This can be achieved through controlling information, intimidating would-be dissidents, swaying public thought, and creating apathy. Self-censorship is difficult to document, as when it is implemented effectively the only noticeable tracing is a lack of undesirable content; instead one must look at the tools and techniques used by censors to encourage self-censorship. Controlling Information relies on traditional censorship techniques, or by forcing all users to connect through an intranet, such as in North Korea. Intimidation is often achieved through allowing Internet users to post “whatever they want,” but arresting those who post about dissenting views, this technique is incredibly common [Calamur-2013] [AP-2012] [Hopkins-2011] [Guardian-2014] [Johnson-2010]. A good example of swaying public thought is China’s “50-Cent Party,” reported to be composed of somewhere between 20,000 [Bristow-2013] and 300,000 [Fareed-2008] contributors who are paid to “guide public thought” on local and regional issues as directed by the Ministry of Culture. Creating apathy can be a side-effect of successfully controlling information over time and is ideal for a censorship regime [Gao-2014].
Because domain names are resolved recursively, if a root name server reassigns or delists a domain, all other DNS servers will be unable to properly forward and cache the site. Domain name registration is only really a risk where undesirable content is hosted on TLD controlled by the censoring country, such as .cn or .ru [Anderson-2011] or where legal processes in countries like the United States result in domain name seizures and/or DNS redirection by the government [Kopel-2013].
Servers must have a physical location somewhere in the world. If undesirable content is hosted in the censoring country the servers can be physically seized or the hosting provider can be required to prevent access [Anderson-2011].
In many countries, legal mechanisms exist where an individual or other content provider can issue a legal request to a content host that requires the host to take down content. Examples include the systems employed by companies like Google to comply with “Right to be Forgotten” policies in the European Union [Google-RTBF], intermediary liability rules for electronic platform providers [EC-2012], or the copyright-oriented notice and takedown regime of the United States Digital Millennium Copyright Act (DMCA) Section 512 [DMLP-512].
This document benefited from discussions with Amelia Andersdotter, Stephane Bortzmeyer, Vinicius Fortuna, Martin Nilsson, Michael Richardson, and Patrick Vacek.