Pub Date : 2019-01-01DOI: 10.14722/NDSS.2019.23326
Z. Berkay Celik, Gang Tan, P. Mcdaniel
Broadly defined as the Internet of Things (IoT), the growth of commodity devices that integrate physical processes with digital connectivity has changed the way we live, play, and work. To date, the traditional approach to securing IoT has treated devices individually. However, in practice, it has been recently shown that the interactions among devices are often the real cause of safety and security violations. In this paper, we present IOTGUARD, a dynamic, policy-based enforcement system for IoT, which protects users from unsafe and insecure device states by monitoring the behavior of IoT and triggeraction platform apps. IOTGUARD operates in three phases: (a) implementation of a code instrumentor that adds extra logic to an app’s source code to collect app’s information at runtime, (b) storing the apps’ information in a dynamic model that represents the runtime execution behavior of apps, and (c) identifying IoT safety and security policies, and enforcing relevant policies on the dynamic model of individual apps or sets of interacting apps. We demonstrate IOTGUARD on 20 flawed apps and find that IOTGUARD correctly enforces 12 of the 12 policy violations. In addition, we evaluate IOTGUARD on 35 SmartThings IoT and 30 IFTTT trigger-action platform market apps executed in a simulated smart home. IOTGUARD enforces 11 unique policies and blocks 16 states in six (17.1%) SmartThings and five (16.6%) IFTTT apps. IOTGUARD imposes only 17.3% runtime overhead on an app and 19.8% for five interacting apps. Through this effort, we introduce a rigorously grounded system for enforcing correct operation of IoT devices through systematically identified IoT policies, demonstrating the effectiveness and value of monitoring IoT apps with tools such as IOTGUARD.
{"title":"IoTGuard: Dynamic Enforcement of Security and Safety Policy in Commodity IoT","authors":"Z. Berkay Celik, Gang Tan, P. Mcdaniel","doi":"10.14722/NDSS.2019.23326","DOIUrl":"https://doi.org/10.14722/NDSS.2019.23326","url":null,"abstract":"Broadly defined as the Internet of Things (IoT), the growth of commodity devices that integrate physical processes with digital connectivity has changed the way we live, play, and work. To date, the traditional approach to securing IoT has treated devices individually. However, in practice, it has been recently shown that the interactions among devices are often the real cause of safety and security violations. In this paper, we present IOTGUARD, a dynamic, policy-based enforcement system for IoT, which protects users from unsafe and insecure device states by monitoring the behavior of IoT and triggeraction platform apps. IOTGUARD operates in three phases: (a) implementation of a code instrumentor that adds extra logic to an app’s source code to collect app’s information at runtime, (b) storing the apps’ information in a dynamic model that represents the runtime execution behavior of apps, and (c) identifying IoT safety and security policies, and enforcing relevant policies on the dynamic model of individual apps or sets of interacting apps. We demonstrate IOTGUARD on 20 flawed apps and find that IOTGUARD correctly enforces 12 of the 12 policy violations. In addition, we evaluate IOTGUARD on 35 SmartThings IoT and 30 IFTTT trigger-action platform market apps executed in a simulated smart home. IOTGUARD enforces 11 unique policies and blocks 16 states in six (17.1%) SmartThings and five (16.6%) IFTTT apps. IOTGUARD imposes only 17.3% runtime overhead on an app and 19.8% for five interacting apps. Through this effort, we introduce a rigorously grounded system for enforcing correct operation of IoT devices through systematically identified IoT policies, demonstrating the effectiveness and value of monitoring IoT apps with tools such as IOTGUARD.","PeriodicalId":20444,"journal":{"name":"Proceedings 2019 Network and Distributed System Security Symposium","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89284063","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}
Pub Date : 2019-01-01DOI: 10.14722/ndss.2019.23409
Jack Wampler, Ian Martiny, Eric Wustrow
Recently, the Spectre and Meltdown attacks revealed serious vulnerabilities in modern CPU designs, allowing an attacker to exfiltrate data from sensitive programs. These vulnerabilities take advantage of speculative execution to coerce a processor to perform computation that would otherwise not occur, leaking the resulting information via side channels to an attacker. In this paper, we extend these ideas in a different direction, and leverage speculative execution in order to hide malware from both static and dynamic analysis. Using this technique, critical portions of a malicious program’s computation can be shielded from view, such that even a debugger following an instructionlevel trace of the program cannot tell how its results were computed. We introduce ExSpectre, which compiles arbitrary malicious code into a seemingly-benign payload binary. When a separate trigger program runs on the same machine, it mistrains the CPU’s branch predictor, causing the payload program to speculatively execute its malicious payload, which communicates speculative results back to the rest of the payload program to change its real-world behavior. We study the extent and types of execution that can be performed speculatively, and demonstrate several computations that can be performed covertly. In particular, within speculative execution we are able to decrypt memory using AES-NI instructions at over 11 kbps. Building on this, we decrypt and interpret a custom virtual machine language to perform arbitrary computation and system calls in the real world. We demonstrate this with a proof-of-concept dial back shell, which takes only a few milliseconds to execute after the trigger is issued. We also show how our corresponding trigger program can be a preexisting benign application already running on the system, and demonstrate this concept with OpenSSL driven remotely by the attacker as a trigger program. ExSpectre demonstrates a new kind of malware that evades existing reverse engineering and binary analysis techniques. Because its true functionality is contained in seemingly unreachable dead code, and its control flow driven externally by potentially any other program running at the same time, ExSpectre poses a novel threat to state-of-the-art malware analysis techniques.
{"title":"ExSpectre: Hiding Malware in Speculative Execution","authors":"Jack Wampler, Ian Martiny, Eric Wustrow","doi":"10.14722/ndss.2019.23409","DOIUrl":"https://doi.org/10.14722/ndss.2019.23409","url":null,"abstract":"Recently, the Spectre and Meltdown attacks revealed serious vulnerabilities in modern CPU designs, allowing an attacker to exfiltrate data from sensitive programs. These vulnerabilities take advantage of speculative execution to coerce a processor to perform computation that would otherwise not occur, leaking the resulting information via side channels to an attacker. In this paper, we extend these ideas in a different direction, and leverage speculative execution in order to hide malware from both static and dynamic analysis. Using this technique, critical portions of a malicious program’s computation can be shielded from view, such that even a debugger following an instructionlevel trace of the program cannot tell how its results were computed. We introduce ExSpectre, which compiles arbitrary malicious code into a seemingly-benign payload binary. When a separate trigger program runs on the same machine, it mistrains the CPU’s branch predictor, causing the payload program to speculatively execute its malicious payload, which communicates speculative results back to the rest of the payload program to change its real-world behavior. We study the extent and types of execution that can be performed speculatively, and demonstrate several computations that can be performed covertly. In particular, within speculative execution we are able to decrypt memory using AES-NI instructions at over 11 kbps. Building on this, we decrypt and interpret a custom virtual machine language to perform arbitrary computation and system calls in the real world. We demonstrate this with a proof-of-concept dial back shell, which takes only a few milliseconds to execute after the trigger is issued. We also show how our corresponding trigger program can be a preexisting benign application already running on the system, and demonstrate this concept with OpenSSL driven remotely by the attacker as a trigger program. ExSpectre demonstrates a new kind of malware that evades existing reverse engineering and binary analysis techniques. Because its true functionality is contained in seemingly unreachable dead code, and its control flow driven externally by potentially any other program running at the same time, ExSpectre poses a novel threat to state-of-the-art malware analysis techniques.","PeriodicalId":20444,"journal":{"name":"Proceedings 2019 Network and Distributed System Security Symposium","volume":"117 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79461598","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}
Middleboxes are widely deployed in order to enhance security and performance in networking. As communication over TLS becomes increasingly common, however, the end-to-end channel model of TLS undermines the efficacy of middleboxes. Existing solutions, such as ‘SplitTLS’, which intercepts TLS sessions, often introduce significant security risks by installing a custom root certificate or sharing a private key. Many studies have confirmed security vulnerabilities when combining TLS with middleboxes, which include certificate validation failures, use of obsolete ciphersuites, and unwanted content modification. To address the above issues, we introduce a middlebox-aware TLS protocol, dubbed maTLS, which allows middleboxes to participate in the TLS session in a visible and auditable fashion. Every participating middlebox now splits a session into two segments with their own security parameters in collaboration with the two endpoints. The maTLS protocol is designed to authenticate the middleboxes to verify the security parameters of segments, and to audit the middleboxes’ write operations. Thus, security of the session is ensured. We prove the security model of maTLS by using Tamarin, a state-of-theart security verification tool. We also carry out testbed-based experiments to show that maTLS achieves the above security goals with marginal overhead.
{"title":"maTLS: How to Make TLS middlebox-aware?","authors":"Hyunwoo Lee, Zach Smith, Junghwan Lim, Gyeongjae Choi, Selin Chun, Taejoong Chung, T. Kwon","doi":"10.14722/ndss.2019.23547","DOIUrl":"https://doi.org/10.14722/ndss.2019.23547","url":null,"abstract":"Middleboxes are widely deployed in order to enhance security and performance in networking. As communication over TLS becomes increasingly common, however, the end-to-end channel model of TLS undermines the efficacy of middleboxes. Existing solutions, such as ‘SplitTLS’, which intercepts TLS sessions, often introduce significant security risks by installing a custom root certificate or sharing a private key. Many studies have confirmed security vulnerabilities when combining TLS with middleboxes, which include certificate validation failures, use of obsolete ciphersuites, and unwanted content modification. To address the above issues, we introduce a middlebox-aware TLS protocol, dubbed maTLS, which allows middleboxes to participate in the TLS session in a visible and auditable fashion. Every participating middlebox now splits a session into two segments with their own security parameters in collaboration with the two endpoints. The maTLS protocol is designed to authenticate the middleboxes to verify the security parameters of segments, and to audit the middleboxes’ write operations. Thus, security of the session is ensured. We prove the security model of maTLS by using Tamarin, a state-of-theart security verification tool. We also carry out testbed-based experiments to show that maTLS achieves the above security goals with marginal overhead.","PeriodicalId":20444,"journal":{"name":"Proceedings 2019 Network and Distributed System Security Symposium","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89891685","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}
Pub Date : 2019-01-01DOI: 10.14722/ndss.2019.23126
Ruian Duan, Ashish Bijlani, Yang Ji, Omar Alrawi, Yiyuan Xiong, Moses Ike, Brendan Saltaformaggio, Wenke Lee
Mobile application developers rely heavily on opensource software (OSS) to offload common functionalities such as the implementation of protocols and media format playback. Over the past years, several vulnerabilities have been found in popular open-source libraries like OpenSSL and FFmpeg. Mobile applications that include such libraries inherit these flaws, which make them vulnerable. Fortunately, the open-source community is responsive and patches are made available within days. However, mobile application developers are often left unaware of these flaws. The App Security Improvement Program (ASIP) is a commendable effort by Google to notify application developers of these flaws, but recent work has shown that many developers do not act on this information. Our work addresses vulnerable mobile applications through automatic binary patching from source patches provided by the OSS maintainers and without involving the developers. We propose novel techniques to overcome difficult challenges like patching feasibility analysis, source-code-to-binary-code matching, and in-memory patching. Our technique uses a novel variabilityaware approach, which we implement as OSSPATCHER. We evaluated OSSPATCHER with 39 OSS and a collection of 1,000 Android applications using their vulnerable versions. OSSPATCHER generated 675 function-level patches that fixed the affected mobile applications without breaking their binary code. Further, we evaluated 10 vulnerabilities in popular apps such as Chrome with public exploits, which OSSPATCHER was able to mitigate and thwart their exploitation.
{"title":"Automating Patching of Vulnerable Open-Source Software Versions in Application Binaries","authors":"Ruian Duan, Ashish Bijlani, Yang Ji, Omar Alrawi, Yiyuan Xiong, Moses Ike, Brendan Saltaformaggio, Wenke Lee","doi":"10.14722/ndss.2019.23126","DOIUrl":"https://doi.org/10.14722/ndss.2019.23126","url":null,"abstract":"Mobile application developers rely heavily on opensource software (OSS) to offload common functionalities such as the implementation of protocols and media format playback. Over the past years, several vulnerabilities have been found in popular open-source libraries like OpenSSL and FFmpeg. Mobile applications that include such libraries inherit these flaws, which make them vulnerable. Fortunately, the open-source community is responsive and patches are made available within days. However, mobile application developers are often left unaware of these flaws. The App Security Improvement Program (ASIP) is a commendable effort by Google to notify application developers of these flaws, but recent work has shown that many developers do not act on this information. Our work addresses vulnerable mobile applications through automatic binary patching from source patches provided by the OSS maintainers and without involving the developers. We propose novel techniques to overcome difficult challenges like patching feasibility analysis, source-code-to-binary-code matching, and in-memory patching. Our technique uses a novel variabilityaware approach, which we implement as OSSPATCHER. We evaluated OSSPATCHER with 39 OSS and a collection of 1,000 Android applications using their vulnerable versions. OSSPATCHER generated 675 function-level patches that fixed the affected mobile applications without breaking their binary code. Further, we evaluated 10 vulnerabilities in popular apps such as Chrome with public exploits, which OSSPATCHER was able to mitigate and thwart their exploitation.","PeriodicalId":20444,"journal":{"name":"Proceedings 2019 Network and Distributed System Security Symposium","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88284969","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}
Pub Date : 2019-01-01DOI: 10.14722/ndss.2019.23206
Ajaya Neupane, Nitesh Saxena, Leanne M. Hirshfield, Sarah E. Bratt
A new generation of scams has emerged that uses voice impersonation to obtain sensitive information, eavesdrop over voice calls and extort money from unsuspecting human users. Research demonstrates that users are fallible to voice impersonation attacks that exploit the current advancement in speech synthesis. In this paper, we set out to elicit a deeper understanding of such human-centered “voice hacking” based on a neuro-scientific methodology (thereby corroborating and expanding the traditional behavioral-only approach in significant ways). Specifically, we investigate the neural underpinnings of voice security through functional near-infrared spectroscopy (fNIRS), a cutting-edge neuroimaging technique, that captures neural signals in both temporal and spatial domains. We design and conduct an fNIRS study to pursue a thorough investigation of users’ mental processing related to speaker legitimacy detection – whether a voice sample is rendered by a target speaker, a different other human speaker or a synthesizer mimicking the speaker. We analyze the neural activity associated within this task as well as the brain areas that may control such activity. Our key insight is that there may be no statistically significant differences in the way the human brain processes the legitimate speakers vs. synthesized speakers, whereas clear differences are visible when encountering legitimate vs. different other human speakers. This finding may help to explain users’ susceptibility to synthesized attacks, as seen from the behavioral self-reported analysis. That is, the impersonated synthesized voices may seem indistinguishable from the real voices in terms of both behavioral and neural perspectives. In sharp contrast, prior studies showed subconscious neural differences in other real vs. fake artifacts (e.g., paintings and websites), despite users failing to note these differences behaviorally. Overall, our work dissects the fundamental neural patterns underlying voice-based insecurity and reveals users’ susceptibility to voice synthesis attacks at a biological level. We believe that this could be a significant insight for the security community suggesting that the human detection of voice synthesis attacks may not improve over time, especially given that voice synthesis techniques will likely continue to improve, calling for the design of careful machine-assisted techniques to help humans counter these attacks. *Work done while being a student at UAB
{"title":"The Crux of Voice (In)Security: A Brain Study of Speaker Legitimacy Detection","authors":"Ajaya Neupane, Nitesh Saxena, Leanne M. Hirshfield, Sarah E. Bratt","doi":"10.14722/ndss.2019.23206","DOIUrl":"https://doi.org/10.14722/ndss.2019.23206","url":null,"abstract":"A new generation of scams has emerged that uses voice impersonation to obtain sensitive information, eavesdrop over voice calls and extort money from unsuspecting human users. Research demonstrates that users are fallible to voice impersonation attacks that exploit the current advancement in speech synthesis. In this paper, we set out to elicit a deeper understanding of such human-centered “voice hacking” based on a neuro-scientific methodology (thereby corroborating and expanding the traditional behavioral-only approach in significant ways). Specifically, we investigate the neural underpinnings of voice security through functional near-infrared spectroscopy (fNIRS), a cutting-edge neuroimaging technique, that captures neural signals in both temporal and spatial domains. We design and conduct an fNIRS study to pursue a thorough investigation of users’ mental processing related to speaker legitimacy detection – whether a voice sample is rendered by a target speaker, a different other human speaker or a synthesizer mimicking the speaker. We analyze the neural activity associated within this task as well as the brain areas that may control such activity. Our key insight is that there may be no statistically significant differences in the way the human brain processes the legitimate speakers vs. synthesized speakers, whereas clear differences are visible when encountering legitimate vs. different other human speakers. This finding may help to explain users’ susceptibility to synthesized attacks, as seen from the behavioral self-reported analysis. That is, the impersonated synthesized voices may seem indistinguishable from the real voices in terms of both behavioral and neural perspectives. In sharp contrast, prior studies showed subconscious neural differences in other real vs. fake artifacts (e.g., paintings and websites), despite users failing to note these differences behaviorally. Overall, our work dissects the fundamental neural patterns underlying voice-based insecurity and reveals users’ susceptibility to voice synthesis attacks at a biological level. We believe that this could be a significant insight for the security community suggesting that the human detection of voice synthesis attacks may not improve over time, especially given that voice synthesis techniques will likely continue to improve, calling for the design of careful machine-assisted techniques to help humans counter these attacks. *Work done while being a student at UAB","PeriodicalId":20444,"journal":{"name":"Proceedings 2019 Network and Distributed System Security Symposium","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77361276","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}
Pub Date : 2019-01-01DOI: 10.14722/ndss.2019.23210
Xiaokuan Zhang, Jihun Hamm, M. Reiter, Yinqian Zhang
Machine learning empowers traffic-analysis attacks that breach users’ privacy from their encrypted traffic. Recent advances in deep learning drastically escalate such threats. One prominent example demonstrated recently is a traffic-analysis attack against video streaming by using convolutional neural networks. In this paper, we explore the adaption of techniques previously used in the domains of adversarial machine learning and differential privacy to mitigate the machine-learning-powered analysis of streaming traffic. Our findings are twofold. First, constructing adversarial samples effectively confounds an adversary with a predetermined classifier but is less effective when the adversary can adapt to the defense by using alternative classifiers or training the classifier with adversarial samples. Second, differential-privacy guarantees are very effective against such statistical-inference-based traffic analysis, while remaining agnostic to the machine learning classifiers used by the adversary. We propose two mechanisms for enforcing differential privacy for encrypted streaming traffic, and evaluate their security and utility. Our empirical implementation and evaluation suggest that the proposed statistical privacy approaches are promising solutions in the underlying scenarios.
{"title":"Statistical Privacy for Streaming Traffic","authors":"Xiaokuan Zhang, Jihun Hamm, M. Reiter, Yinqian Zhang","doi":"10.14722/ndss.2019.23210","DOIUrl":"https://doi.org/10.14722/ndss.2019.23210","url":null,"abstract":"Machine learning empowers traffic-analysis attacks that breach users’ privacy from their encrypted traffic. Recent advances in deep learning drastically escalate such threats. One prominent example demonstrated recently is a traffic-analysis attack against video streaming by using convolutional neural networks. In this paper, we explore the adaption of techniques previously used in the domains of adversarial machine learning and differential privacy to mitigate the machine-learning-powered analysis of streaming traffic. Our findings are twofold. First, constructing adversarial samples effectively confounds an adversary with a predetermined classifier but is less effective when the adversary can adapt to the defense by using alternative classifiers or training the classifier with adversarial samples. Second, differential-privacy guarantees are very effective against such statistical-inference-based traffic analysis, while remaining agnostic to the machine learning classifiers used by the adversary. We propose two mechanisms for enforcing differential privacy for encrypted streaming traffic, and evaluate their security and utility. Our empirical implementation and evaluation suggest that the proposed statistical privacy approaches are promising solutions in the underlying scenarios.","PeriodicalId":20444,"journal":{"name":"Proceedings 2019 Network and Distributed System Security Symposium","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91204376","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}
Pub Date : 2019-01-01DOI: 10.14722/ndss.2019.23511
Sergey Frolov, Eric Wustrow
TLS, the Transport Layer Security protocol, has quickly become the most popular protocol on the Internet, already used to load over 70% of web pages in Mozilla Firefox. Due to its ubiquity, TLS is also a popular protocol for censorship circumvention tools, including Tor and Signal, among others. However, the wide range of features supported in TLS makes it possible to distinguish implementations from one another by what set of cipher suites, elliptic curves, signature algorithms, and other extensions they support. Already, censors have used deep packet inspection (DPI) to identify and block popular circumvention tools based on the fingerprint of their TLS implementation. In response, many circumvention tools have attempted to mimic popular TLS implementations such as browsers, but this technique has several challenges. First, it is burdensome to keep up with the rapidly-changing browser TLS implementations, and know what fingerprints would be good candidates to mimic. Second, TLS implementations can be difficult to mimic correctly, as they offer many features that may not be supported by the relatively lightweight libraries used in typical circumvention tools. Finally, dependency changes and updates to the underlying libraries can silently impact what an application’s TLS fingerprint looks like, making it difficult for tool maintainers to keep up. In this paper, we collect and analyze real-world TLS traffic from over 11.8 billion TLS connections over 9 months to identify a wide range of TLS client implementations actually used on the Internet. We use our data to analyze TLS implementations of several popular censorship circumvention tools, including Lantern, Psiphon, Signal, Outline, TapDance, and Tor (Snowflake and meek pluggable transports). We find that the many of these tools use TLS configurations that are easily distinguishable from the real-world traffic they attempt to mimic, even when these tools have put effort into parroting popular TLS implementations. To address this problem, we have developed a library, uTLS, that enables tool maintainers to automatically mimic other popular TLS implementations. Using our real-world traffic dataset, we observe many popular TLS implementations we are able to correctly mimic with uTLS, and we describe ways our tool can more flexibly adapt to the dynamic TLS ecosystem with minimal manual effort.
{"title":"The use of TLS in Censorship Circumvention","authors":"Sergey Frolov, Eric Wustrow","doi":"10.14722/ndss.2019.23511","DOIUrl":"https://doi.org/10.14722/ndss.2019.23511","url":null,"abstract":"TLS, the Transport Layer Security protocol, has quickly become the most popular protocol on the Internet, already used to load over 70% of web pages in Mozilla Firefox. Due to its ubiquity, TLS is also a popular protocol for censorship circumvention tools, including Tor and Signal, among others. However, the wide range of features supported in TLS makes it possible to distinguish implementations from one another by what set of cipher suites, elliptic curves, signature algorithms, and other extensions they support. Already, censors have used deep packet inspection (DPI) to identify and block popular circumvention tools based on the fingerprint of their TLS implementation. In response, many circumvention tools have attempted to mimic popular TLS implementations such as browsers, but this technique has several challenges. First, it is burdensome to keep up with the rapidly-changing browser TLS implementations, and know what fingerprints would be good candidates to mimic. Second, TLS implementations can be difficult to mimic correctly, as they offer many features that may not be supported by the relatively lightweight libraries used in typical circumvention tools. Finally, dependency changes and updates to the underlying libraries can silently impact what an application’s TLS fingerprint looks like, making it difficult for tool maintainers to keep up. In this paper, we collect and analyze real-world TLS traffic from over 11.8 billion TLS connections over 9 months to identify a wide range of TLS client implementations actually used on the Internet. We use our data to analyze TLS implementations of several popular censorship circumvention tools, including Lantern, Psiphon, Signal, Outline, TapDance, and Tor (Snowflake and meek pluggable transports). We find that the many of these tools use TLS configurations that are easily distinguishable from the real-world traffic they attempt to mimic, even when these tools have put effort into parroting popular TLS implementations. To address this problem, we have developed a library, uTLS, that enables tool maintainers to automatically mimic other popular TLS implementations. Using our real-world traffic dataset, we observe many popular TLS implementations we are able to correctly mimic with uTLS, and we describe ways our tool can more flexibly adapt to the dynamic TLS ecosystem with minimal manual effort.","PeriodicalId":20444,"journal":{"name":"Proceedings 2019 Network and Distributed System Security Symposium","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86582308","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}
Pub Date : 2019-01-01DOI: 10.14722/ndss.2019.23060
Gabriel Kaptchuk, M. Green, Ian Miers
In this work we investigate new computational properties that can be achieved by combining stateless trusted devices with public ledgers. We consider a hybrid paradigm in which a client-side device (such as a co-processor or trusted enclave) performs secure computation, while interacting with a public ledger via a possibly malicious host computer. We explore both the constructive and potentially destructive implications of such systems. We first show that this combination allows for the construction of stateful interactive functionalities (including general computation) even when the device has no persistent storage; this allows us to build sophisticated applications using inexpensive trusted hardware or even pure cryptographic obfuscation techniques. We further show how to use this paradigm to achieve censorship-resistant communication with a network, even when network communications are mediated by a potentially malicious host. Finally we describe a number of practical applications that can be achieved today. These include the synchronization of private smart contracts; rate limited mandatory logging; strong encrypted backups from weak passwords; enforcing fairness in multi-party computation; and destructive applications such as autonomous ransomware, which allows for payments without an online party.
{"title":"Giving State to the Stateless: Augmenting Trustworthy Computation with Ledgers","authors":"Gabriel Kaptchuk, M. Green, Ian Miers","doi":"10.14722/ndss.2019.23060","DOIUrl":"https://doi.org/10.14722/ndss.2019.23060","url":null,"abstract":"In this work we investigate new computational properties that can be achieved by combining stateless trusted devices with public ledgers. We consider a hybrid paradigm in which a client-side device (such as a co-processor or trusted enclave) performs secure computation, while interacting with a public ledger via a possibly malicious host computer. We explore both the constructive and potentially destructive implications of such systems. We first show that this combination allows for the construction of stateful interactive functionalities (including general computation) even when the device has no persistent storage; this allows us to build sophisticated applications using inexpensive trusted hardware or even pure cryptographic obfuscation techniques. We further show how to use this paradigm to achieve censorship-resistant communication with a network, even when network communications are mediated by a potentially malicious host. Finally we describe a number of practical applications that can be achieved today. These include the synchronization of private smart contracts; rate limited mandatory logging; strong encrypted backups from weak passwords; enforcing fairness in multi-party computation; and destructive applications such as autonomous ransomware, which allows for payments without an online party.","PeriodicalId":20444,"journal":{"name":"Proceedings 2019 Network and Distributed System Security Symposium","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81000259","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}
Pub Date : 2019-01-01DOI: 10.14722/ndss.2019.23513
Adil Ahmad, Byunggill Joe, Yuan Xiao, Yinqian Zhang, I. Shin, Byoungyoung Lee
Program obfuscation is a popular cryptographic construct with a wide range of uses such as IP theft prevention. Although cryptographic solutions for program obfuscation impose impractically high overheads, a recent breakthrough in systematically leveraging trusted hardware has shown promise. However, the existing solution is based on special-purpose trusted hardware, restricting its use-cases to a limited few. In this paper, we first study if such obfuscation is feasible based on commodity trusted hardware, Intel SGX, and we observe that certain important security considerations are not afforded by commodity hardware. In particular, we found that existing obfuscation/obliviousness schemes are insecure if directly applied to the SGX environment mainly due to the side-channel limitations. To this end, we present OBFSCURO, the first system providing program obfuscation using commodity trusted hardware, Intel SGX. The key idea is to leverage ORAMbased operations to perform secure code execution and data access. Initially, OBFSCURO transforms the regular program layout into a side-channel-secure and ORAM-compatible layout. Then, OBFSCURO ensures that its ORAM controller always performs data oblivious accesses in order to protect itself from the side-channel attacks. Furthermore, OBFSCURO ensures that the program is secure from timing-based attacks by ensuring that the program always runs for a pre-configured time interval. Along the way, OBFSCURO also introduces a systematic optimization such as register-based ORAM stash. We provide a thorough security analysis of OBFSCURO along with empirical attack evaluations showing that OBFSCURO can protect the SGX program execution from being leaked by access pattern-based and timing-based channels. We also provide a detailed performance benchmark results in order to show the practical aspects of OBFSCURO.
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