Joshua Tan, Lujo Bauer, Nicolas Christin, L. Cranor
Multiple mechanisms exist to encourage users to create stronger passwords, including minimum-length and character-class requirements, prohibiting blocklisted passwords, and giving feedback on the strength of candidate passwords. Despite much research, there is little definitive, scientific guidance on how these mechanisms should be combined and configured to best effect. Through two online experiments, we evaluated combinations of minimum-length and character-class requirements, blocklists, and a minimum-strength requirement that requires passwords to exceed a strength threshold according to neural-network-driven password-strength estimates. Our results lead to concrete recommendations for policy configurations that produce a good balance of security and usability. In particular, for high-value user accounts we recommend policies that combine minimum-strength and minimum-length requirements. While we offer recommendations for organizations required to use blocklists, using blocklists does not provide further gains. Interestingly, we also find that against expert attackers, character-class requirements, traditionally associated with producing stronger passwords, in practice may provide very little improvement and may even reduce effective security.
{"title":"Practical Recommendations for Stronger, More Usable Passwords Combining Minimum-strength, Minimum-length, and Blocklist Requirements","authors":"Joshua Tan, Lujo Bauer, Nicolas Christin, L. Cranor","doi":"10.1145/3372297.3417882","DOIUrl":"https://doi.org/10.1145/3372297.3417882","url":null,"abstract":"Multiple mechanisms exist to encourage users to create stronger passwords, including minimum-length and character-class requirements, prohibiting blocklisted passwords, and giving feedback on the strength of candidate passwords. Despite much research, there is little definitive, scientific guidance on how these mechanisms should be combined and configured to best effect. Through two online experiments, we evaluated combinations of minimum-length and character-class requirements, blocklists, and a minimum-strength requirement that requires passwords to exceed a strength threshold according to neural-network-driven password-strength estimates. Our results lead to concrete recommendations for policy configurations that produce a good balance of security and usability. In particular, for high-value user accounts we recommend policies that combine minimum-strength and minimum-length requirements. While we offer recommendations for organizations required to use blocklists, using blocklists does not provide further gains. Interestingly, we also find that against expert attackers, character-class requirements, traditionally associated with producing stronger passwords, in practice may provide very little improvement and may even reduce effective security.","PeriodicalId":20481,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83331602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chenxiong Qian, Hyungjoon Koo, ChangSeok Oh, Taesoo Kim, Wenke Lee
Today, a web browser plays a crucial role in offering a broad spectrum of web experiences. The most popular browser, Chromium, has become an extremely complex application to meet ever-increasing user demands, exposing unavoidably large attack vectors due to its large code base. Code debloating attracts attention as a means of reducing such a potential attack surface by eliminating unused code. However, it is very challenging to perform sophisticated code removal without breaking needed functionalities because Chromium operates on a large number of closely connected and complex components, such as a renderer and JavaScript engine. In this paper, we present Slimium, a debloating framework for a browser (i.e., Chromium) that harnesses a hybrid approach for a fast and reliable binary instrumentation. The main idea behind Slimium is to determine a set of features as a debloating unit on top of a hybrid (i.e., static, dynamic, heuristic) analysis, and then leverage feature subsetting to code debloating. It aids in i) focusing on security-oriented features, ii) discarding unneeded code simply without complications, and iii)~reasonably addressing a non-deterministic path problem raised from code complexity. To this end, we generate a feature-code map with a relation vector technique and prompt webpage profiling results. Our experimental results demonstrate the practicality and feasibility of Slimium for 40 popular websites, as on average it removes 94 CVEs (61.4%) by cutting down 23.85 MB code (53.1%) from defined features (21.7% of the whole) in Chromium.
{"title":"Slimium: Debloating the Chromium Browser with Feature Subsetting","authors":"Chenxiong Qian, Hyungjoon Koo, ChangSeok Oh, Taesoo Kim, Wenke Lee","doi":"10.1145/3372297.3417866","DOIUrl":"https://doi.org/10.1145/3372297.3417866","url":null,"abstract":"Today, a web browser plays a crucial role in offering a broad spectrum of web experiences. The most popular browser, Chromium, has become an extremely complex application to meet ever-increasing user demands, exposing unavoidably large attack vectors due to its large code base. Code debloating attracts attention as a means of reducing such a potential attack surface by eliminating unused code. However, it is very challenging to perform sophisticated code removal without breaking needed functionalities because Chromium operates on a large number of closely connected and complex components, such as a renderer and JavaScript engine. In this paper, we present Slimium, a debloating framework for a browser (i.e., Chromium) that harnesses a hybrid approach for a fast and reliable binary instrumentation. The main idea behind Slimium is to determine a set of features as a debloating unit on top of a hybrid (i.e., static, dynamic, heuristic) analysis, and then leverage feature subsetting to code debloating. It aids in i) focusing on security-oriented features, ii) discarding unneeded code simply without complications, and iii)~reasonably addressing a non-deterministic path problem raised from code complexity. To this end, we generate a feature-code map with a relation vector technique and prompt webpage profiling results. Our experimental results demonstrate the practicality and feasibility of Slimium for 40 popular websites, as on average it removes 94 CVEs (61.4%) by cutting down 23.85 MB code (53.1%) from defined features (21.7% of the whole) in Chromium.","PeriodicalId":20481,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86022757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Melissa Chase, Trevor Perrin, Gregory M. Zaverucha
In this paper we present a system for maintaining a membership list of users in a group, designed for use in the Signal Messenger secure messaging app. The goal is to support private groups where membership information is readily available to all group members but hidden from the service provider or anyone outside the group. In the proposed solution, a central server stores the group membership in the form of encrypted entries. Members of the group authenticate to the server in a way that reveals only that they correspond to some encrypted entry, then read and write the encrypted entries. Authentication in our design uses a primitive called a keyed-verification anonymous credential~(KVAC), and we construct a new KVAC scheme based on an algebraic MAC, instantiated in a group G of prime order. The benefit of the new KVAC is that attributes may be elements in G, whereas previous schemes could only support attributes that were integers modulo the order of G. This enables us to encrypt group data using an efficient Elgamal-like encryption scheme, and to prove in zero-knowledge that the encrypted data is certified by a credential. Because encryption, authentication, and the associated proofs of knowledge are all instantiated in G the system is efficient, even for large groups.
{"title":"The Signal Private Group System and Anonymous Credentials Supporting Efficient Verifiable Encryption","authors":"Melissa Chase, Trevor Perrin, Gregory M. Zaverucha","doi":"10.1145/3372297.3417887","DOIUrl":"https://doi.org/10.1145/3372297.3417887","url":null,"abstract":"In this paper we present a system for maintaining a membership list of users in a group, designed for use in the Signal Messenger secure messaging app. The goal is to support private groups where membership information is readily available to all group members but hidden from the service provider or anyone outside the group. In the proposed solution, a central server stores the group membership in the form of encrypted entries. Members of the group authenticate to the server in a way that reveals only that they correspond to some encrypted entry, then read and write the encrypted entries. Authentication in our design uses a primitive called a keyed-verification anonymous credential~(KVAC), and we construct a new KVAC scheme based on an algebraic MAC, instantiated in a group G of prime order. The benefit of the new KVAC is that attributes may be elements in G, whereas previous schemes could only support attributes that were integers modulo the order of G. This enables us to encrypt group data using an efficient Elgamal-like encryption scheme, and to prove in zero-knowledge that the encrypted data is certified by a credential. Because encryption, authentication, and the associated proofs of knowledge are all instantiated in G the system is efficient, even for large groups.","PeriodicalId":20481,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84670865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shashank Agrawal, S. Badrinarayanan, Pratyay Mukherjee, Peter Rindal
We use biometrics like fingerprints and facial images to identify ourselves to our mobile devices and log on to applications everyday. Such authentication is internal-facing: we provide measurement on the same device where the template is stored. If our personal devices could participate in external-facing authentication too, where biometric measurement is captured by a nearby external sensor, then we could also enjoy a frictionless authentication experience in a variety of physical spaces like grocery stores, convention centers, ATMs, etc. The open setting of a physical space brings forth important privacy concerns though. We design a suite of secure protocols for external-facing authentication based on the cosine similarity metric which provide privacy for both user templates stored on their devices and the biometric measurement captured by external sensors in this open setting. The protocols provide different levels of security, ranging from passive security with some leakage to active security with no leakage at all. With the help of new packing techniques and zero-knowledge proofs for Paillier encryption -- and careful protocol design, our protocols achieve very practical performance numbers. For templates of length 256 with elements of size 16 bits each, our fastest protocol takes merely 0.024 seconds to compute a match, but even the slowest one takes no more than 0.12 seconds. The communication overhead of our protocols is very small too. The passive and actively secure protocols (with some leakage) need to exchange just 16.5KB and 27.8KB of data, respectively. The first message is designed to be reusable and, if sent in advance, would cut the overhead down to just 0.5KB and 0.8KB, respectively.
{"title":"Game-Set-MATCH: Using Mobile Devices for Seamless External-Facing Biometric Matching","authors":"Shashank Agrawal, S. Badrinarayanan, Pratyay Mukherjee, Peter Rindal","doi":"10.1145/3372297.3417287","DOIUrl":"https://doi.org/10.1145/3372297.3417287","url":null,"abstract":"We use biometrics like fingerprints and facial images to identify ourselves to our mobile devices and log on to applications everyday. Such authentication is internal-facing: we provide measurement on the same device where the template is stored. If our personal devices could participate in external-facing authentication too, where biometric measurement is captured by a nearby external sensor, then we could also enjoy a frictionless authentication experience in a variety of physical spaces like grocery stores, convention centers, ATMs, etc. The open setting of a physical space brings forth important privacy concerns though. We design a suite of secure protocols for external-facing authentication based on the cosine similarity metric which provide privacy for both user templates stored on their devices and the biometric measurement captured by external sensors in this open setting. The protocols provide different levels of security, ranging from passive security with some leakage to active security with no leakage at all. With the help of new packing techniques and zero-knowledge proofs for Paillier encryption -- and careful protocol design, our protocols achieve very practical performance numbers. For templates of length 256 with elements of size 16 bits each, our fastest protocol takes merely 0.024 seconds to compute a match, but even the slowest one takes no more than 0.12 seconds. The communication overhead of our protocols is very small too. The passive and actively secure protocols (with some leakage) need to exchange just 16.5KB and 27.8KB of data, respectively. The first message is designed to be reusable and, if sent in advance, would cut the overhead down to just 0.5KB and 0.8KB, respectively.","PeriodicalId":20481,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","volume":"121 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88259674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Symmetric password-authenticated key exchange (sPAKE) can be seen as an extension of traditional key exchange where two parties agree on a shared key if and only if they share a common secret (possibly low-entropy) password. We present the first sPAKE protocol to simultaneously achieve the following properties: only two exponentiations per party, the same as plain unauthenticated Diffie-Hellman key agreement (and likely optimal); optimal round complexity: a single flow (one message from each party that can be sent in parallel) to achieve implicit authentication, or two flows to achieve explicit mutual authentication; security in the random oracle model, rather than ideal cipher or generic group model; UC security, rather than game-based. Our protocol is a generalization of the seminal EKE protocol of Bellovin & Merritt (S&P 1992). We also present a UC-secure 1-out-of-N oblivious transfer (OT) protocol, for random payloads. Its communication complexity is independent of N, meaning that N can even be exponential in the security parameter. Such a protocol can also be considered a kind of oblivious PRF (OPRF). Our protocol improves over the leading UC-secure 1-out-of-N OT construction of Masny & Rindal (CCS 2019) for all N>2, and has essentially the same cost for N=2. The new technique underlying these results is a primitive we call programmable-once public function (POPF). Intuitively, a POPF is a function whose output can be programmed by one party on exactly one point. All other outputs of the function are outside of any party's control, in a provable sense.
{"title":"Minimal Symmetric PAKE and 1-out-of-N OT from Programmable-Once Public Functions","authors":"Ian McQuoid, Mike Rosulek, Lawrence Roy","doi":"10.1145/3372297.3417870","DOIUrl":"https://doi.org/10.1145/3372297.3417870","url":null,"abstract":"Symmetric password-authenticated key exchange (sPAKE) can be seen as an extension of traditional key exchange where two parties agree on a shared key if and only if they share a common secret (possibly low-entropy) password. We present the first sPAKE protocol to simultaneously achieve the following properties: only two exponentiations per party, the same as plain unauthenticated Diffie-Hellman key agreement (and likely optimal); optimal round complexity: a single flow (one message from each party that can be sent in parallel) to achieve implicit authentication, or two flows to achieve explicit mutual authentication; security in the random oracle model, rather than ideal cipher or generic group model; UC security, rather than game-based. Our protocol is a generalization of the seminal EKE protocol of Bellovin & Merritt (S&P 1992). We also present a UC-secure 1-out-of-N oblivious transfer (OT) protocol, for random payloads. Its communication complexity is independent of N, meaning that N can even be exponential in the security parameter. Such a protocol can also be considered a kind of oblivious PRF (OPRF). Our protocol improves over the leading UC-secure 1-out-of-N OT construction of Masny & Rindal (CCS 2019) for all N>2, and has essentially the same cost for N=2. The new technique underlying these results is a primitive we call programmable-once public function (POPF). Intuitively, a POPF is a function whose output can be programmed by one party on exactly one point. All other outputs of the function are outside of any party's control, in a provable sense.","PeriodicalId":20481,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88108499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Online gaming, with a reported 152 billion US dollar market, is immensely popular today. One of the critical issues in multiplayer online games is cheating, in which a player uses an illegal methodology to create an advantage beyond honest game play. For example, wallhacks, the main focus of this work, animate enemy objects on a cheating player's screen, despite being actually hidden behind walls (or other occluding objects). Since such cheats discourage honest players and cause game companies to lose revenue, gaming companies deploy mitigation solutions alongside game applications on the player's machine. However, their solutions are fundamentally flawed since they are deployed on a machine where the attacker has absolute control.
{"title":"BlackMirror: Preventing Wallhacks in 3D Online FPS Games","authors":"Seonghyun Park, Adil Ahmad, Byoungyoung Lee","doi":"10.1145/3372297.3417890","DOIUrl":"https://doi.org/10.1145/3372297.3417890","url":null,"abstract":"Online gaming, with a reported 152 billion US dollar market, is immensely popular today. One of the critical issues in multiplayer online games is cheating, in which a player uses an illegal methodology to create an advantage beyond honest game play. For example, wallhacks, the main focus of this work, animate enemy objects on a cheating player's screen, despite being actually hidden behind walls (or other occluding objects). Since such cheats discourage honest players and cause game companies to lose revenue, gaming companies deploy mitigation solutions alongside game applications on the player's machine. However, their solutions are fundamentally flawed since they are deployed on a machine where the attacker has absolute control.","PeriodicalId":20481,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88165562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, the security implication of stale NS records, which point to a nameserver that no longer resolves the domain, has been unveiled. Prior research studied the stale DNS records that point to expired domains. The popularity of DNS hosting services brings in a new category of stale NS records, which reside in the domain's zone (instead of the TLD zone) for an active domain. To the best of our knowledge, the security risk of this kind of stale NS record has never been studied before. In our research, we show that this new type of stale NS record can be practically exploited, causing a stealthier hijack of domains associated with the DNS hosting service. We also performed a large-scale analysis on over 1M high-profile domains, 17 DNS hosting providers and 12 popular public resolver operators to confirm the prevalence of this security risk. Our research further discovers 628 hijackable domains (e.g., 6 government entities and 2 payment services), 14 affected DNS hosting providers (e.g., Amazon Route 53), and 10 vulnerable public resolver operators (e.g., CloudFlare). Furthermore, we conducted an in-depth measurement analysis on them, thus providing a better understanding of this new security risk. Also, we explore the mitigation techniques that can be adopted by different affected parties.
{"title":"Zombie Awakening: Stealthy Hijacking of Active Domains through DNS Hosting Referral","authors":"Eihal Alowaisheq, Siyuan Tang, Zhihao Wang, Fatemah Alharbi, Xiaojing Liao, Xiaofeng Wang","doi":"10.1145/3372297.3417864","DOIUrl":"https://doi.org/10.1145/3372297.3417864","url":null,"abstract":"In recent years, the security implication of stale NS records, which point to a nameserver that no longer resolves the domain, has been unveiled. Prior research studied the stale DNS records that point to expired domains. The popularity of DNS hosting services brings in a new category of stale NS records, which reside in the domain's zone (instead of the TLD zone) for an active domain. To the best of our knowledge, the security risk of this kind of stale NS record has never been studied before. In our research, we show that this new type of stale NS record can be practically exploited, causing a stealthier hijack of domains associated with the DNS hosting service. We also performed a large-scale analysis on over 1M high-profile domains, 17 DNS hosting providers and 12 popular public resolver operators to confirm the prevalence of this security risk. Our research further discovers 628 hijackable domains (e.g., 6 government entities and 2 payment services), 14 affected DNS hosting providers (e.g., Amazon Route 53), and 10 vulnerable public resolver operators (e.g., CloudFlare). Furthermore, we conducted an in-depth measurement analysis on them, thus providing a better understanding of this new security risk. Also, we explore the mitigation techniques that can be adopted by different affected parties.","PeriodicalId":20481,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91090457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I would like to share my thoughts on the interactions between machine learning and security. The good: We now have more data, more powerful machines and algorithms, and better yet, we don't need to always manually engineered the features. The ML process is now much more automated and the learned models are more powerful, and this is a positive feedback loop: more data leads to better models, which lead to more deployments, which lead to more data. All security vendors now advertise that they use ML in their products. The bad: There are more unknowns. In the past, we knew the capabilities and limitations of our security models, including the ML-based models, and understood how they can be evaded. But the state-of-the-art models such as deep neural networks are not as intelligible as classical models such as decision trees. How do we decide to deploy a deep learning-based model for security when we don't know for sure it is learned correctly? Data poisoning becomes easier. On-line learning and web-based learning use data collected in run-time and often from an open environment. Since such data is often resulted from human actions, it can be intentionally polluted, e.g., in misinformation campaigns. How do we make it harder for attackers to manipulate the training data? The ugly: Attackers will keep on exploiting the holes in ML, and automate their attacks using ML. Why don't we just secure ML? This would be no different than trying to secure our programs, and systems, and networks, so we can't. We have to prepare for ML failures. Ultimately, humans have to be involved. The question is how and when? For example, what information should a ML-based system present to humans and what input can humans provide to the system?
{"title":"Machine Learning and Security: The Good, The Bad, and The Ugly","authors":"Wenke Lee","doi":"10.1145/3372297.3424552","DOIUrl":"https://doi.org/10.1145/3372297.3424552","url":null,"abstract":"I would like to share my thoughts on the interactions between machine learning and security. The good: We now have more data, more powerful machines and algorithms, and better yet, we don't need to always manually engineered the features. The ML process is now much more automated and the learned models are more powerful, and this is a positive feedback loop: more data leads to better models, which lead to more deployments, which lead to more data. All security vendors now advertise that they use ML in their products. The bad: There are more unknowns. In the past, we knew the capabilities and limitations of our security models, including the ML-based models, and understood how they can be evaded. But the state-of-the-art models such as deep neural networks are not as intelligible as classical models such as decision trees. How do we decide to deploy a deep learning-based model for security when we don't know for sure it is learned correctly? Data poisoning becomes easier. On-line learning and web-based learning use data collected in run-time and often from an open environment. Since such data is often resulted from human actions, it can be intentionally polluted, e.g., in misinformation campaigns. How do we make it harder for attackers to manipulate the training data? The ugly: Attackers will keep on exploiting the holes in ML, and automate their attacks using ML. Why don't we just secure ML? This would be no different than trying to secure our programs, and systems, and networks, so we can't. We have to prepare for ML failures. Ultimately, humans have to be involved. The question is how and when? For example, what information should a ML-based system present to humans and what input can humans provide to the system?","PeriodicalId":20481,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74206674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Session details: Session 2E: Smart Contracts and Cryptocurrencies","authors":"Foteini Baldimtsi","doi":"10.1145/3432966","DOIUrl":"https://doi.org/10.1145/3432966","url":null,"abstract":"","PeriodicalId":20481,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73984157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many censorship circumvention tools rely on trusted proxies that allow users within censored regions to access blocked Internet content by tunneling it through a covert channel (e.g,. piggybacking on Skype video calls). However, building tools that can simultaneously (i) provide good bandwidth capacity for accommodating the typical activities of Internet users, and (ii) be secure against traffic analysis attacks has remained an open problem and a stumbling block to the practical adoption of such tools for censorship evasion. We present Protozoa, a censorship-resistant tunneling tool featuring both high-performing covert channels and strong traffic analysis resistance. To create a covert channel, a user only needs to make a video call with a trusted party located outside the censored region using a popular WebRTC streaming service, e.g., Whereby. Protozoa can then covertly tunnel all IP traffic from unmodified user applications (e.g., Firefox) through the WebRTC video stream. This is achieved by hooking into the WebRTC stack and replacing the encoded video frame data with IP packet payload, while ensuring that the payload of the WebRTC stream remains encrypted, and the stream's statistical properties remain in all identical to those of any common video call. This technique allows for sustaining enough throughput to enable common-use Internet applications, e.g., web browsing or bulk data transfer, and avoid detection by state-of-the-art traffic analysis attacks. We show that Protozoa is able to evade state-level censorship in China, Russia, and India.
{"title":"Poking a Hole in the Wall: Efficient Censorship-Resistant Internet Communications by Parasitizing on WebRTC","authors":"Diogo Barradas, Nuno Santos, Luís Rodrigues, Vítor Nunes","doi":"10.1145/3372297.3417874","DOIUrl":"https://doi.org/10.1145/3372297.3417874","url":null,"abstract":"Many censorship circumvention tools rely on trusted proxies that allow users within censored regions to access blocked Internet content by tunneling it through a covert channel (e.g,. piggybacking on Skype video calls). However, building tools that can simultaneously (i) provide good bandwidth capacity for accommodating the typical activities of Internet users, and (ii) be secure against traffic analysis attacks has remained an open problem and a stumbling block to the practical adoption of such tools for censorship evasion. We present Protozoa, a censorship-resistant tunneling tool featuring both high-performing covert channels and strong traffic analysis resistance. To create a covert channel, a user only needs to make a video call with a trusted party located outside the censored region using a popular WebRTC streaming service, e.g., Whereby. Protozoa can then covertly tunnel all IP traffic from unmodified user applications (e.g., Firefox) through the WebRTC video stream. This is achieved by hooking into the WebRTC stack and replacing the encoded video frame data with IP packet payload, while ensuring that the payload of the WebRTC stream remains encrypted, and the stream's statistical properties remain in all identical to those of any common video call. This technique allows for sustaining enough throughput to enable common-use Internet applications, e.g., web browsing or bulk data transfer, and avoid detection by state-of-the-art traffic analysis attacks. We show that Protozoa is able to evade state-level censorship in China, Russia, and India.","PeriodicalId":20481,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","volume":"07 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78371271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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