The analysis of real-world protocols, in particular key exchange protocols and protocols building on these protocols, is a very complex, error-prone, and tedious task. Besides the complexity of the protocols itself, one important reason for this is that the security of the protocols has to be reduced to the security of the underlying cryptographic primitives for every protocol time and again. We would therefore like to get rid of reduction proofs for real-world key exchange protocols as much as possible and in many cases altogether, also for higher-level protocols which use the exchanged keys. So far some first steps have been taken in this direction. But existing work is still quite limited, and, for example, does not support Diffie-Hellman (DH) key exchange, a prevalent cryptographic primitive for real-world protocols. In this paper, building on work by Küsters and Tuengerthal, we provide an ideal functionality in the universal composability setting which supports several common cryptographic primitives, including DH key exchange. This functionality helps to avoid reduction proofs in the analysis of real-world protocols and often eliminates them completely. We also propose a new general ideal key exchange functionality which allows higher-level protocols to use exchanged keys in an ideal way. As a proof of concept, we apply our framework to three practical DH key exchange protocols, namely ISO 9798-3, SIGMA, and OPTLS.
{"title":"A Framework for Universally Composable Diffie-Hellman Key Exchange","authors":"Ralf Küsters, Daniel Rausch","doi":"10.1109/SP.2017.63","DOIUrl":"https://doi.org/10.1109/SP.2017.63","url":null,"abstract":"The analysis of real-world protocols, in particular key exchange protocols and protocols building on these protocols, is a very complex, error-prone, and tedious task. Besides the complexity of the protocols itself, one important reason for this is that the security of the protocols has to be reduced to the security of the underlying cryptographic primitives for every protocol time and again. We would therefore like to get rid of reduction proofs for real-world key exchange protocols as much as possible and in many cases altogether, also for higher-level protocols which use the exchanged keys. So far some first steps have been taken in this direction. But existing work is still quite limited, and, for example, does not support Diffie-Hellman (DH) key exchange, a prevalent cryptographic primitive for real-world protocols. In this paper, building on work by Küsters and Tuengerthal, we provide an ideal functionality in the universal composability setting which supports several common cryptographic primitives, including DH key exchange. This functionality helps to avoid reduction proofs in the analysis of real-world protocols and often eliminates them completely. We also propose a new general ideal key exchange functionality which allows higher-level protocols to use exchanged keys in an ideal way. As a proof of concept, we apply our framework to three practical DH key exchange protocols, namely ISO 9798-3, SIGMA, and OPTLS.","PeriodicalId":6502,"journal":{"name":"2017 IEEE Symposium on Security and Privacy (SP)","volume":"30 1","pages":"881-900"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82797888","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 this paper we present a simple and reliable authentication method for mobile devices equipped with multi-touch screens such as smart phones, tablets and laptops. Users are authenticated by performing specially designed multi-touch gestures with one swipe on the touchscreen. During this process, both hand geometry and behavioral characteristics are recorded in the multi-touch traces and used for authentication. By combining both geometry information and behavioral characteristics, we overcome the problem of behavioral variability plaguing many behavior based authentication techniques – which often leads to less accurate authentication or poor user experience – while also ensuring the discernibility of different users with possibly similar handshapes. We evaluate the design of the proposed authentication method thoroughly using a large multi-touch dataset collected from 161 subjects with an elaborately designed procedure to capture behavior variability. The results demonstrate that the fusion of behavioral information with hand geometry features produces effective resistance to behavioral variability over time while at the same time retains discernibility. Our approach achieves EER of 5.84% with only 5 training samples and the performance is further improved to EER of 1.88% with enough training. Security analyses are also conducted to demonstrate that the proposed method is resilient against common smartphone authentication threats such as smudge attack, shoulder surfing attack and statistical attack. Finally, user acceptance of the method is illustrated via a usability study.
{"title":"Multi-touch Authentication Using Hand Geometry and Behavioral Information","authors":"Yunpeng Song, Zhongmin Cai, Zhi-Li Zhang","doi":"10.1109/SP.2017.54","DOIUrl":"https://doi.org/10.1109/SP.2017.54","url":null,"abstract":"In this paper we present a simple and reliable authentication method for mobile devices equipped with multi-touch screens such as smart phones, tablets and laptops. Users are authenticated by performing specially designed multi-touch gestures with one swipe on the touchscreen. During this process, both hand geometry and behavioral characteristics are recorded in the multi-touch traces and used for authentication. By combining both geometry information and behavioral characteristics, we overcome the problem of behavioral variability plaguing many behavior based authentication techniques – which often leads to less accurate authentication or poor user experience – while also ensuring the discernibility of different users with possibly similar handshapes. We evaluate the design of the proposed authentication method thoroughly using a large multi-touch dataset collected from 161 subjects with an elaborately designed procedure to capture behavior variability. The results demonstrate that the fusion of behavioral information with hand geometry features produces effective resistance to behavioral variability over time while at the same time retains discernibility. Our approach achieves EER of 5.84% with only 5 training samples and the performance is further improved to EER of 1.88% with enough training. Security analyses are also conducted to demonstrate that the proposed method is resilient against common smartphone authentication threats such as smudge attack, shoulder surfing attack and statistical attack. Finally, user acceptance of the method is illustrated via a usability study.","PeriodicalId":6502,"journal":{"name":"2017 IEEE Symposium on Security and Privacy (SP)","volume":"23 1","pages":"357-372"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83038704","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}
The past ten years has seen increasing calls to makesecurity research more "scientific".On the surface, most agree that this is desirable, given universal recognition of "science" as a positive force. However, we find that there is little clarity on what "scientific" means inthe context of computer security research, or consensus onwhat a "Science of Security" should look like. We selectively review work in the history and philosophy of scienceand more recent work under the label "Science of Security".We explore what has been done under the theme of relating science and security, put this in context with historical science, and offer observations and insights we hope maymotivate further exploration and guidance. Among our findings are thatpractices on which the rest of science has reached consensus appear little usedor recognized in security, and a pattern of methodological errors continues unaddressed.
{"title":"SoK: Science, Security and the Elusive Goal of Security as a Scientific Pursuit","authors":"Cormac Herley, P. V. Oorschot","doi":"10.1109/SP.2017.38","DOIUrl":"https://doi.org/10.1109/SP.2017.38","url":null,"abstract":"The past ten years has seen increasing calls to makesecurity research more \"scientific\".On the surface, most agree that this is desirable, given universal recognition of \"science\" as a positive force. However, we find that there is little clarity on what \"scientific\" means inthe context of computer security research, or consensus onwhat a \"Science of Security\" should look like. We selectively review work in the history and philosophy of scienceand more recent work under the label \"Science of Security\".We explore what has been done under the theme of relating science and security, put this in context with historical science, and offer observations and insights we hope maymotivate further exploration and guidance. Among our findings are thatpractices on which the rest of science has reached consensus appear little usedor recognized in security, and a pattern of methodological errors continues unaddressed.","PeriodicalId":6502,"journal":{"name":"2017 IEEE Symposium on Security and Privacy (SP)","volume":"41 1","pages":"99-120"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76354746","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}
Felix Fischer, Konstantin Böttinger, Huang Xiao, Christian Stransky, Y. Acar, M. Backes, S. Fahl
Online programming discussion platforms such as Stack Overflow serve as a rich source of information for software developers. Available information include vibrant discussions and oftentimes ready-to-use code snippets. Previous research identified Stack Overflow as one of the most important information sources developers rely on. Anecdotes report that software developers copy and paste code snippets from those information sources for convenience reasons. Such behavior results in a constant flow of community-provided code snippets into production software. To date, the impact of this behaviour on code security is unknown. We answer this highly important question by quantifying the proliferation of security-related code snippets from Stack Overflow in Android applications available on Google Play. Access to the rich source of information available on Stack Overflow including ready-to-use code snippets provides huge benefits for software developers. However, when it comes to code security there are some caveats to bear in mind: Due to the complex nature of code security, it is very difficult to provide ready-to-use and secure solutions for every problem. Hence, integrating a security-related code snippet from Stack Overflow into production software requires caution and expertise. Unsurprisingly, we observed insecure code snippets being copied into Android applications millions of users install from Google Play every day. To quantitatively evaluate the extent of this observation, we scanned Stack Overflow for code snippets and evaluated their security score using a stochastic gradient descent classifier. In order to identify code reuse in Android applications, we applied state-of-the-art static analysis. Our results are alarming: 15.4% of the 1.3 million Android applications we analyzed, contained security-related code snippets from Stack Overflow. Out of these 97.9% contain at least one insecure code snippet.
{"title":"Stack Overflow Considered Harmful? The Impact of Copy&Paste on Android Application Security","authors":"Felix Fischer, Konstantin Böttinger, Huang Xiao, Christian Stransky, Y. Acar, M. Backes, S. Fahl","doi":"10.1109/SP.2017.31","DOIUrl":"https://doi.org/10.1109/SP.2017.31","url":null,"abstract":"Online programming discussion platforms such as Stack Overflow serve as a rich source of information for software developers. Available information include vibrant discussions and oftentimes ready-to-use code snippets. Previous research identified Stack Overflow as one of the most important information sources developers rely on. Anecdotes report that software developers copy and paste code snippets from those information sources for convenience reasons. Such behavior results in a constant flow of community-provided code snippets into production software. To date, the impact of this behaviour on code security is unknown. We answer this highly important question by quantifying the proliferation of security-related code snippets from Stack Overflow in Android applications available on Google Play. Access to the rich source of information available on Stack Overflow including ready-to-use code snippets provides huge benefits for software developers. However, when it comes to code security there are some caveats to bear in mind: Due to the complex nature of code security, it is very difficult to provide ready-to-use and secure solutions for every problem. Hence, integrating a security-related code snippet from Stack Overflow into production software requires caution and expertise. Unsurprisingly, we observed insecure code snippets being copied into Android applications millions of users install from Google Play every day. To quantitatively evaluate the extent of this observation, we scanned Stack Overflow for code snippets and evaluated their security score using a stochastic gradient descent classifier. In order to identify code reuse in Android applications, we applied state-of-the-art static analysis. Our results are alarming: 15.4% of the 1.3 million Android applications we analyzed, contained security-related code snippets from Stack Overflow. Out of these 97.9% contain at least one insecure code snippet.","PeriodicalId":6502,"journal":{"name":"2017 IEEE Symposium on Security and Privacy (SP)","volume":"49 1 1","pages":"121-136"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73053053","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}
Geumhwan Cho, J. Huh, Junsung Cho, Seongyeol Oh, Youngbae Song, Hyoungshick Kim
To improve the security of user-chosen Android screen lock patterns, we propose a novel system-guided pattern lock scheme called "SysPal" that mandates the use of a small number of randomly selected points while selecting a pattern. Users are given the freedom to use those mandated points at any position. We conducted a large-scale online study with 1,717 participants to evaluate the security and usability of three SysPal policies, varying the number of mandatory points that must be used (upon selecting a pattern) from one to three. Our results suggest that the two SysPal policies that mandate the use of one and two points can help users select significantly more secure patterns compared to the current Android policy: 22.58% and 23.19% fewer patterns were cracked. Those two SysPal policies, however, did not show any statistically significant inferiority in pattern recall success rate (the percentage of participants who correctly recalled their pattern after 24 hours). In our lab study, we asked participants to install our screen unlock application on their own Android device, and observed their real-life phone unlock behaviors for a day. Again, our lab study did not show any statistically significant difference in memorability for those two SysPal policies compared to the current Android policy.
{"title":"SysPal: System-Guided Pattern Locks for Android","authors":"Geumhwan Cho, J. Huh, Junsung Cho, Seongyeol Oh, Youngbae Song, Hyoungshick Kim","doi":"10.1109/SP.2017.61","DOIUrl":"https://doi.org/10.1109/SP.2017.61","url":null,"abstract":"To improve the security of user-chosen Android screen lock patterns, we propose a novel system-guided pattern lock scheme called \"SysPal\" that mandates the use of a small number of randomly selected points while selecting a pattern. Users are given the freedom to use those mandated points at any position. We conducted a large-scale online study with 1,717 participants to evaluate the security and usability of three SysPal policies, varying the number of mandatory points that must be used (upon selecting a pattern) from one to three. Our results suggest that the two SysPal policies that mandate the use of one and two points can help users select significantly more secure patterns compared to the current Android policy: 22.58% and 23.19% fewer patterns were cracked. Those two SysPal policies, however, did not show any statistically significant inferiority in pattern recall success rate (the percentage of participants who correctly recalled their pattern after 24 hours). In our lab study, we asked participants to install our screen unlock application on their own Android device, and observed their real-life phone unlock behaviors for a day. Again, our lab study did not show any statistically significant difference in memorability for those two SysPal policies compared to the current Android policy.","PeriodicalId":6502,"journal":{"name":"2017 IEEE Symposium on Security and Privacy (SP)","volume":"34 1","pages":"338-356"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80189066","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}
We present Catena, an efficiently-verifiable Bitcoinwitnessing scheme. Catena enables any number of thin clients, such as mobile phones, to efficiently agree on a log of application-specific statements managed by an adversarial server. Catenaimplements a log as an OP_RETURN transaction chain andprevents forks in the log by leveraging Bitcoin's security againstdouble spends. Specifically, if a log server wants to equivocate ithas to double spend a Bitcoin transaction output. Thus, Catenalogs are as hard to fork as the Bitcoin blockchain: an adversarywithout a large fraction of the network's computational powercannot fork Bitcoin and thus cannot fork a Catena log either. However, different from previous Bitcoin-based work, Catenadecreases the bandwidth requirements of log auditors from 90GB to only tens of megabytes. More precisely, our clients onlyneed to download all Bitcoin block headers (currently less than35 MB) and a small, 600-byte proof for each statement in a block. We implement Catena in Java using the bitcoinj library and use itto extend CONIKS, a recent key transparency scheme, to witnessits public-key directory in the Bitcoin blockchain where it can beefficiently verified by auditors. We show that Catena can securemany systems today, such as public-key directories, Tor directoryservers and software transparency schemes.
{"title":"Catena: Efficient Non-equivocation via Bitcoin","authors":"Alin Tomescu, S. Devadas","doi":"10.1109/SP.2017.19","DOIUrl":"https://doi.org/10.1109/SP.2017.19","url":null,"abstract":"We present Catena, an efficiently-verifiable Bitcoinwitnessing scheme. Catena enables any number of thin clients, such as mobile phones, to efficiently agree on a log of application-specific statements managed by an adversarial server. Catenaimplements a log as an OP_RETURN transaction chain andprevents forks in the log by leveraging Bitcoin's security againstdouble spends. Specifically, if a log server wants to equivocate ithas to double spend a Bitcoin transaction output. Thus, Catenalogs are as hard to fork as the Bitcoin blockchain: an adversarywithout a large fraction of the network's computational powercannot fork Bitcoin and thus cannot fork a Catena log either. However, different from previous Bitcoin-based work, Catenadecreases the bandwidth requirements of log auditors from 90GB to only tens of megabytes. More precisely, our clients onlyneed to download all Bitcoin block headers (currently less than35 MB) and a small, 600-byte proof for each statement in a block. We implement Catena in Java using the bitcoinj library and use itto extend CONIKS, a recent key transparency scheme, to witnessits public-key directory in the Bitcoin blockchain where it can beefficiently verified by auditors. We show that Catena can securemany systems today, such as public-key directories, Tor directoryservers and software transparency schemes.","PeriodicalId":6502,"journal":{"name":"2017 IEEE Symposium on Security and Privacy (SP)","volume":"1 1","pages":"393-409"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83369692","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}
Mathias Lécuyer, Riley Spahn, Roxana Geambasu, Tzu-Kuo Huang, S. Sen
Protecting vast quantities of data poses a daunting challenge for the growing number of organizations that collect, stockpile, and monetize it. The ability to distinguish data that is actually needed from data collected "just in case" would help these organizations to limit the latter's exposure to attack. A natural approach might be to monitor data use and retain only the working-set of in-use data in accessible storage, unused data can be evicted to a highly protected store. However, many of today's big data applications rely on machine learning (ML) workloads that are periodically retrained by accessing, and thus exposing to attack, the entire data store. Training set minimization methods, such as count featurization, are often used to limit the data needed to train ML workloads to improve performance or scalability. We present Pyramid, a limited-exposure data management system that builds upon count featurization to enhance data protection. As such, Pyramid uniquely introduces both the idea and proof-of-concept for leveraging training set minimization methods to instill rigor and selectivity into big data management. We integrated Pyramid into Spark Velox, a framework for ML-based targeting and personalization. We evaluate it on three applications and show that Pyramid approaches state-of-the-art models while training on less than 1% of the raw data.
{"title":"Pyramid: Enhancing Selectivity in Big Data Protection with Count Featurization","authors":"Mathias Lécuyer, Riley Spahn, Roxana Geambasu, Tzu-Kuo Huang, S. Sen","doi":"10.1109/SP.2017.60","DOIUrl":"https://doi.org/10.1109/SP.2017.60","url":null,"abstract":"Protecting vast quantities of data poses a daunting challenge for the growing number of organizations that collect, stockpile, and monetize it. The ability to distinguish data that is actually needed from data collected \"just in case\" would help these organizations to limit the latter's exposure to attack. A natural approach might be to monitor data use and retain only the working-set of in-use data in accessible storage, unused data can be evicted to a highly protected store. However, many of today's big data applications rely on machine learning (ML) workloads that are periodically retrained by accessing, and thus exposing to attack, the entire data store. Training set minimization methods, such as count featurization, are often used to limit the data needed to train ML workloads to improve performance or scalability. We present Pyramid, a limited-exposure data management system that builds upon count featurization to enhance data protection. As such, Pyramid uniquely introduces both the idea and proof-of-concept for leveraging training set minimization methods to instill rigor and selectivity into big data management. We integrated Pyramid into Spark Velox, a framework for ML-based targeting and personalization. We evaluate it on three applications and show that Pyramid approaches state-of-the-art models while training on less than 1% of the raw data.","PeriodicalId":6502,"journal":{"name":"2017 IEEE Symposium on Security and Privacy (SP)","volume":"18 1","pages":"78-95"},"PeriodicalIF":0.0,"publicationDate":"2017-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90339811","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}
Yaohui Chen, Dongli Zhang, Ruowen Wang, Rui Qiao, Ahmed M. Azab, Long Lu, H. Vijayakumar, Wenbo Shen
Code reuse attacks exploiting memory disclosure vulnerabilities can bypass all deployed mitigations. One promising defense against this class of attacks is to enable execute-only memory (XOM) protection on top of fine-grained address space layout randomization (ASLR). However, recent works implementing XOM, despite their efficacy, only protect programs that have been (re)built with new compiler support, leaving commercial-off-the-shelf (COTS) binaries and source-unavailable programs unprotected. We present the design and implementation of NORAX, a practical system that retrofits XOM into stripped COTS binaries on AArch64 platforms. Unlike previous techniques, NORAX requires neither source code nor debugging symbols. NORAX statically transforms existing binaries so that during runtime their code sections can be loaded into XOM memory pages with embedded data relocated and data references properly updated. NORAX allows transformed binaries to leverage the new hardware-based XOM support—a feature widely available on AArch64 platforms (e.g., recent mobile devices) yet virtually unused due to the incompatibility of existing binaries. Furthermore, NORAX is designed to co-exist with other COTS binary hardening techniques, such as in-place randomization (IPR). We apply NORAX to the commonly used Android system binaries running on SAMSUNG Galaxy S6 and LG Nexus 5X devices. The results show that NORAX on average slows down the execution of transformed binaries by 1.18% and increases their memory footprint by 2.21%, suggesting NORAX is practical for real-world adoption.
{"title":"NORAX: Enabling Execute-Only Memory for COTS Binaries on AArch64","authors":"Yaohui Chen, Dongli Zhang, Ruowen Wang, Rui Qiao, Ahmed M. Azab, Long Lu, H. Vijayakumar, Wenbo Shen","doi":"10.1109/SP.2017.30","DOIUrl":"https://doi.org/10.1109/SP.2017.30","url":null,"abstract":"Code reuse attacks exploiting memory disclosure vulnerabilities can bypass all deployed mitigations. One promising defense against this class of attacks is to enable execute-only memory (XOM) protection on top of fine-grained address space layout randomization (ASLR). However, recent works implementing XOM, despite their efficacy, only protect programs that have been (re)built with new compiler support, leaving commercial-off-the-shelf (COTS) binaries and source-unavailable programs unprotected. We present the design and implementation of NORAX, a practical system that retrofits XOM into stripped COTS binaries on AArch64 platforms. Unlike previous techniques, NORAX requires neither source code nor debugging symbols. NORAX statically transforms existing binaries so that during runtime their code sections can be loaded into XOM memory pages with embedded data relocated and data references properly updated. NORAX allows transformed binaries to leverage the new hardware-based XOM support—a feature widely available on AArch64 platforms (e.g., recent mobile devices) yet virtually unused due to the incompatibility of existing binaries. Furthermore, NORAX is designed to co-exist with other COTS binary hardening techniques, such as in-place randomization (IPR). We apply NORAX to the commonly used Android system binaries running on SAMSUNG Galaxy S6 and LG Nexus 5X devices. The results show that NORAX on average slows down the execution of transformed binaries by 1.18% and increases their memory footprint by 2.21%, suggesting NORAX is practical for real-world adoption.","PeriodicalId":6502,"journal":{"name":"2017 IEEE Symposium on Security and Privacy (SP)","volume":"14 1","pages":"304-319"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75552010","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, researchers have shown that unwanted web tracking is on the rise, as advertisers are trying to capitalize on users' online activity, using increasingly intrusive and sophisticated techniques. Among these, browser fingerprinting has received the most attention since it allows trackers to uniquely identify users despite the clearing of cookies and the use of a browser's private mode. In this paper, we investigate and quantify the fingerprintability of browser extensions, such as, AdBlock and Ghostery. We show that an extension's organic activity in a page's DOM can be used to infer its presence, and develop XHound, the first fully automated system for fingerprinting browser extensions. By applying XHound to the 10,000 most popular Google Chrome extensions, we find that a significant fraction of popular browser extensions are fingerprintable and could thus be used to supplement existing fingerprinting methods. Moreover, by surveying the installed extensions of 854 users, we discover that many users tend to install different sets of fingerprintable browser extensions and could thus be uniquely, or near-uniquely identifiable by extension-based fingerprinting. We use XHound's results to build a proof-of-concept extension-fingerprinting script and show that trackers can fingerprint tens of extensions in just a few seconds. Finally, we describe why the fingerprinting of extensions is more intrusive than the fingerprinting of other browser and system properties, and sketch two different approaches towards defending against extension-based fingerprinting.
{"title":"XHOUND: Quantifying the Fingerprintability of Browser Extensions","authors":"Oleksii Starov, Nick Nikiforakis","doi":"10.1109/SP.2017.18","DOIUrl":"https://doi.org/10.1109/SP.2017.18","url":null,"abstract":"In recent years, researchers have shown that unwanted web tracking is on the rise, as advertisers are trying to capitalize on users' online activity, using increasingly intrusive and sophisticated techniques. Among these, browser fingerprinting has received the most attention since it allows trackers to uniquely identify users despite the clearing of cookies and the use of a browser's private mode. In this paper, we investigate and quantify the fingerprintability of browser extensions, such as, AdBlock and Ghostery. We show that an extension's organic activity in a page's DOM can be used to infer its presence, and develop XHound, the first fully automated system for fingerprinting browser extensions. By applying XHound to the 10,000 most popular Google Chrome extensions, we find that a significant fraction of popular browser extensions are fingerprintable and could thus be used to supplement existing fingerprinting methods. Moreover, by surveying the installed extensions of 854 users, we discover that many users tend to install different sets of fingerprintable browser extensions and could thus be uniquely, or near-uniquely identifiable by extension-based fingerprinting. We use XHound's results to build a proof-of-concept extension-fingerprinting script and show that trackers can fingerprint tens of extensions in just a few seconds. Finally, we describe why the fingerprinting of extensions is more intrusive than the fingerprinting of other browser and system properties, and sketch two different approaches towards defending against extension-based fingerprinting.","PeriodicalId":6502,"journal":{"name":"2017 IEEE Symposium on Security and Privacy (SP)","volume":"36 1","pages":"941-956"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80854274","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}
V. Ngo, Mario Dehesa-Azuara, Matt Fredrikson, Jan Hoffmann
Side channel attacks have been used to extract critical data such as encryption keys and confidential user data in a variety of adversarial settings. In practice, this threat is addressed by adhering to a constant-time programming discipline, which imposes strict constraints on the way in which programs are written. This introduces an additional hurdle for programmers faced with the already difficult task of writing secure code, highlighting the need for solutions that give the same source-level guarantees while supporting more natural programming models. We propose a novel type system for verifying that programs correctly implement constant-resource behavior. Our type system extends recent work on automatic amortized resource analysis (AARA), a set of techniques that automatically derive provable upper bounds on the resource consumption of programs. We devise new techniques that build on the potential method to achieve compositionality, precision, and automation. A strict global requirement that a program always maintains constant resource usage is too restrictive for most practical applications. It is sufficient to require that the program's resource behavior remain constant with respect to an attacker who is only allowed to observe part of the program's state and behavior. To account for this, our type system incorporates information flow tracking into its resource analysis. This allows our system to certify programs that need to violate the constant-time requirement in certain cases, as long as doing so does not leak confidential information to attackers. We formalize this guarantee by defining a new notion of resource-aware noninterference, and prove that our system enforces it. Finally, we show how our type inference algorithm can be used to synthesize a constant-time implementation from one that cannot be verified as secure, effectively repairing insecure programs automatically. We also show how a second novel AARA system that computes lower bounds on resource usage can be used to derive quantitative bounds on the amount of information that a program leaks through its resource use. We implemented each of these systems in Resource Aware ML, and show that it can be applied to verify constant-time behavior in a number of applicationsincluding encryption and decryption routines, database queries, and other resource-aware functionality.
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