Güliz Seray Tuncay, Soteris Demetriou, Carl A. Gunter
In-app embedded browsers are commonly used by app developers to display web content without having to redirect the user to heavy-weight web browsers. Just like the conventional web browsers, embedded browsers can allow the execution of web code. In addition, they provide mechanisms (viz., JavaScript bridges) to give web code access to internal app code that might implement critical functionalities and expose device resources. This is intrinsically dangerous since there is currently no means for app developers to perform origin-based access control on the JavaScript bridges, and any web code running in an embedded browser is free to use all the exposed app and device resources. Previous work that addresses this problem provided access control solutions that work only for apps that are built using hybrid frameworks. Additionally, these solutions focused on protecting only the parts of JavaScript bridges that expose permissions-protected resources. In this work, our goal is to provide a generic solution that works for all apps that utilize embedded web browsers and protects all channels that give access to internal app and device resources. Towards realizing this goal, we built Draco, a uniform and fine-grained access control framework for web code running on Android embedded browsers (viz., WebView). Draco provides a declarative policy language that allows developers to define policies to specify the desired access characteristics of web origins in a fine-grained fashion, and a runtime system that dynamically enforces the policies. In contrast with previous work, we do not assume any modifications to the Android operating system, and implement Draco in the Chromium Android System WebView app to enable seamless deployment. Our evaluation of the the Draco runtime system shows that Draco incurs negligible overhead, which is in the order of microseconds.
应用程序内嵌浏览器通常被应用程序开发人员用来显示网页内容，而不必将用户重定向到重型网页浏览器。就像传统的web浏览器一样，嵌入式浏览器可以允许执行web代码。此外，它们还提供机制(即JavaScript桥接)，让web代码访问可能实现关键功能和暴露设备资源的内部应用代码。这在本质上是危险的，因为目前应用开发者没有办法在JavaScript桥上执行基于源的访问控制，任何在嵌入式浏览器中运行的web代码都可以自由地使用所有暴露的应用和设备资源。以前解决这个问题的工作提供的访问控制解决方案只适用于使用混合框架构建的应用程序。此外，这些解决方案只关注于保护暴露受权限保护资源的JavaScript桥接部分。在这项工作中，我们的目标是提供一个通用的解决方案，适用于所有使用嵌入式web浏览器的应用程序，并保护访问内部应用程序和设备资源的所有通道。为了实现这个目标，我们构建了Draco，这是一个统一的、细粒度的访问控制框架，用于运行在Android嵌入式浏览器(即WebView)上的web代码。Draco提供了一种声明性策略语言，允许开发人员定义策略，以细粒度的方式指定web源的所需访问特征，以及动态执行策略的运行时系统。与之前的工作相反，我们不假设对Android操作系统进行任何修改，并在Chromium Android system WebView应用程序中实现Draco以实现无缝部署。我们对Draco运行时系统的评估表明，Draco产生的开销可以忽略不计，其数量级为微秒。
{"title":"Draco: A System for Uniform and Fine-grained Access Control for Web Code on Android","authors":"Güliz Seray Tuncay, Soteris Demetriou, Carl A. Gunter","doi":"10.1145/2976749.2978322","DOIUrl":"https://doi.org/10.1145/2976749.2978322","url":null,"abstract":"In-app embedded browsers are commonly used by app developers to display web content without having to redirect the user to heavy-weight web browsers. Just like the conventional web browsers, embedded browsers can allow the execution of web code. In addition, they provide mechanisms (viz., JavaScript bridges) to give web code access to internal app code that might implement critical functionalities and expose device resources. This is intrinsically dangerous since there is currently no means for app developers to perform origin-based access control on the JavaScript bridges, and any web code running in an embedded browser is free to use all the exposed app and device resources. Previous work that addresses this problem provided access control solutions that work only for apps that are built using hybrid frameworks. Additionally, these solutions focused on protecting only the parts of JavaScript bridges that expose permissions-protected resources. In this work, our goal is to provide a generic solution that works for all apps that utilize embedded web browsers and protects all channels that give access to internal app and device resources. Towards realizing this goal, we built Draco, a uniform and fine-grained access control framework for web code running on Android embedded browsers (viz., WebView). Draco provides a declarative policy language that allows developers to define policies to specify the desired access characteristics of web origins in a fine-grained fashion, and a runtime system that dynamically enforces the policies. In contrast with previous work, we do not assume any modifications to the Android operating system, and implement Draco in the Chromium Android System WebView app to enable seamless deployment. Our evaluation of the the Draco runtime system shows that Draco incurs negligible overhead, which is in the order of microseconds.","PeriodicalId":432261,"journal":{"name":"Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124413968","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 a protocol to enable privacy preserving advertising reporting at scale. Unlike previous systems, our work scales to millions of users and tens of thousands of distinct ads. Our approach builds on the homomorphic encryption approach proposed by Adnostic, but uses new cryptographic proof techniques to efficiently report billions of ad impressions a day using an additively homomorphic voting schemes. Most importantly, our protocol scales without imposing high loads on trusted third parties. Finally, we investigate a cost effective method to privately deliver ads with computational private information retrieval.
{"title":"A Protocol for Privately Reporting Ad Impressions at Scale","authors":"M. Green, Watson Ladd, Ian Miers","doi":"10.1145/2976749.2978407","DOIUrl":"https://doi.org/10.1145/2976749.2978407","url":null,"abstract":"We present a protocol to enable privacy preserving advertising reporting at scale. Unlike previous systems, our work scales to millions of users and tens of thousands of distinct ads. Our approach builds on the homomorphic encryption approach proposed by Adnostic, but uses new cryptographic proof techniques to efficiently report billions of ad impressions a day using an additively homomorphic voting schemes. Most importantly, our protocol scales without imposing high loads on trusted third parties. Finally, we investigate a cost effective method to privately deliver ads with computational private information retrieval.","PeriodicalId":432261,"journal":{"name":"Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127893551","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}
Password vaults are used to store login credentials, usually encrypted by a master password, relieving the user from memorizing a large number of complex passwords. To manage accounts on multiple devices, vaults are often stored at an online service, which substantially increases the risk of leaking the (encrypted) vault. To protect the master password against guessing attacks, previous work has introduced cracking-resistant password vaults based on Honey Encryption. If decryption is attempted with a wrong master password, they output plausible-looking decoy vaults, thus seemingly disabling offline guessing attacks. In this work, we propose attacks against cracking-resistant password vaults that are able to distinguish between real and decoy vaults with high accuracy and thus circumvent the offered protection. These attacks are based on differences in the generated distribution of passwords, which are measured using Kullback-Leibler divergence. Our attack is able to rank the correct vault into the 1.3% most likely vaults (on median), compared to 37.8% of the best-reported attack in previous work. (Note that smaller ranks are better, and 50% is achievable by random guessing.) We demonstrate that this attack is, to a certain extent, a fundamental problem with all static Natural Language Encoders (NLE), where the distribution of decoy vaults is fixed. We propose the notion of adaptive NLEs and demonstrate that they substantially limit the effectiveness of such attacks. We give one example of an adaptive NLE based on Markov models and show that the attack is only able to rank the decoy vaults with a median rank of 35.1%.
{"title":"On the Security of Cracking-Resistant Password Vaults","authors":"M. Golla, Benedict Beuscher, Markus Dürmuth","doi":"10.1145/2976749.2978416","DOIUrl":"https://doi.org/10.1145/2976749.2978416","url":null,"abstract":"Password vaults are used to store login credentials, usually encrypted by a master password, relieving the user from memorizing a large number of complex passwords. To manage accounts on multiple devices, vaults are often stored at an online service, which substantially increases the risk of leaking the (encrypted) vault. To protect the master password against guessing attacks, previous work has introduced cracking-resistant password vaults based on Honey Encryption. If decryption is attempted with a wrong master password, they output plausible-looking decoy vaults, thus seemingly disabling offline guessing attacks. In this work, we propose attacks against cracking-resistant password vaults that are able to distinguish between real and decoy vaults with high accuracy and thus circumvent the offered protection. These attacks are based on differences in the generated distribution of passwords, which are measured using Kullback-Leibler divergence. Our attack is able to rank the correct vault into the 1.3% most likely vaults (on median), compared to 37.8% of the best-reported attack in previous work. (Note that smaller ranks are better, and 50% is achievable by random guessing.) We demonstrate that this attack is, to a certain extent, a fundamental problem with all static Natural Language Encoders (NLE), where the distribution of decoy vaults is fixed. We propose the notion of adaptive NLEs and demonstrate that they substantially limit the effectiveness of such attacks. We give one example of an adaptive NLE based on Markov models and show that the attack is only able to rank the decoy vaults with a median rank of 35.1%.","PeriodicalId":432261,"journal":{"name":"Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115836362","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 Second International Workshop on Cyber-Physical Systems Security and PrivaCy (CPS-SPC'16) is being held in conjunction with the 23rd ACM CCS Conference. This second edition follows a successful workshop held with ACM CCS in 2015. The workshop was motivated by several observations. First, cyber-physical systems represent the new frontier for cyber risk. The attack surface imposed by the convergence of computing, communications and physical control represents unique challenges for security researchers and practitioners. Second, majority of the published literature addressing the security and privacy of CPS reflect a field still in its infancy. As such, the overall principles, models, and theories for securing CPS have not yet emerged. Third, the organizers of this workshop strongly felt that a premiere forum associated with a premiere conference was needed for rapidly publishing diverse, multidisciplinary in-progress work on the security and privacy of CPS and galvanizing the research community. The set of accepted papers reflect this vision. We have organized an exciting program for this workshop and look forward to active participation in this and future workshops.
{"title":"Second Workshop on Cyber-Physical Systems Security and PrivaCy (CPS-SPC'16)","authors":"A. Cárdenas, R. Bobba","doi":"10.1145/2976749.2990481","DOIUrl":"https://doi.org/10.1145/2976749.2990481","url":null,"abstract":"The Second International Workshop on Cyber-Physical Systems Security and PrivaCy (CPS-SPC'16) is being held in conjunction with the 23rd ACM CCS Conference. This second edition follows a successful workshop held with ACM CCS in 2015. The workshop was motivated by several observations. First, cyber-physical systems represent the new frontier for cyber risk. The attack surface imposed by the convergence of computing, communications and physical control represents unique challenges for security researchers and practitioners. Second, majority of the published literature addressing the security and privacy of CPS reflect a field still in its infancy. As such, the overall principles, models, and theories for securing CPS have not yet emerged. Third, the organizers of this workshop strongly felt that a premiere forum associated with a premiere conference was needed for rapidly publishing diverse, multidisciplinary in-progress work on the security and privacy of CPS and galvanizing the research community. The set of accepted papers reflect this vision. We have organized an exciting program for this workshop and look forward to active participation in this and future workshops.","PeriodicalId":432261,"journal":{"name":"Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131720761","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}
Guan-Hua Tu, Chi-Yu Li, Chunyi Peng, Yuanjie Li, Songwu Lu
SMS (Short Messaging Service) is a text messaging service for mobile users to exchange short text messages. It is also widely used to provide SMS-powered services (e.g., mobile banking). With the rapid deployment of all-IP 4G mobile networks, the underlying technology of SMS evolves from the legacy circuit-switched network to the IMS (IP Multimedia Subsystem) system over packet-switched network. In this work, we study the insecurity of the IMS-based SMS. We uncover its security vulnerabilities and exploit them to devise four SMS attacks: silent SMS abuse, SMS spoofing, SMS client DoS, and SMS spamming. We further discover that those SMS threats can propagate towards SMS-powered services, thereby leading to three malicious attacks: social network account hijacking, unauthorized donation, and unauthorized subscription. Our analysis reveals that the problems stem from the loose security regulations among mobile phones, carrier networks, and SMS-powered services. We finally propose remedies to the identified security issues.
{"title":"New Security Threats Caused by IMS-based SMS Service in 4G LTE Networks","authors":"Guan-Hua Tu, Chi-Yu Li, Chunyi Peng, Yuanjie Li, Songwu Lu","doi":"10.1145/2976749.2978393","DOIUrl":"https://doi.org/10.1145/2976749.2978393","url":null,"abstract":"SMS (Short Messaging Service) is a text messaging service for mobile users to exchange short text messages. It is also widely used to provide SMS-powered services (e.g., mobile banking). With the rapid deployment of all-IP 4G mobile networks, the underlying technology of SMS evolves from the legacy circuit-switched network to the IMS (IP Multimedia Subsystem) system over packet-switched network. In this work, we study the insecurity of the IMS-based SMS. We uncover its security vulnerabilities and exploit them to devise four SMS attacks: silent SMS abuse, SMS spoofing, SMS client DoS, and SMS spamming. We further discover that those SMS threats can propagate towards SMS-powered services, thereby leading to three malicious attacks: social network account hijacking, unauthorized donation, and unauthorized subscription. Our analysis reveals that the problems stem from the loose security regulations among mobile phones, carrier networks, and SMS-powered services. We finally propose remedies to the identified security issues.","PeriodicalId":432261,"journal":{"name":"Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130697252","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 work, we significantly improve the efficiency of non-malleable codes in the split state model, by constructing a code with codeword length (roughly), where |s| is the length of the message, and k is the security parameter. This is a substantial improvement over previous constructions, both asymptotically and concretely. Our construction relies on a new primitive which we define and study, called l-more extractable hash functions. This notion, which may be of independent interest, is strictly stronger than the previous notion of extractable hash by Goldwasser et al. (Eprint '11) and Bitansky et al. (ITCS '12, Eprint '14), yet we can instantiate it under the same assumption used for the previous extractable hash function (a variant of the Knowledge of Exponent Assumption).
{"title":"Practical Non-Malleable Codes from l-more Extractable Hash Functions","authors":"A. Kiayias, Feng-Hao Liu, Yiannis Tselekounis","doi":"10.1145/2976749.2978352","DOIUrl":"https://doi.org/10.1145/2976749.2978352","url":null,"abstract":"In this work, we significantly improve the efficiency of non-malleable codes in the split state model, by constructing a code with codeword length (roughly), where |s| is the length of the message, and k is the security parameter. This is a substantial improvement over previous constructions, both asymptotically and concretely. Our construction relies on a new primitive which we define and study, called l-more extractable hash functions. This notion, which may be of independent interest, is strictly stronger than the previous notion of extractable hash by Goldwasser et al. (Eprint '11) and Bitansky et al. (ITCS '12, Eprint '14), yet we can instantiate it under the same assumption used for the previous extractable hash function (a variant of the Knowledge of Exponent Assumption).","PeriodicalId":432261,"journal":{"name":"Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security","volume":"225 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131640016","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}
Tao Li, Yimin Chen, Jingchao Sun, Xiaocong Jin, Yanchao Zhang
Mobile device losses and thefts are skyrocketing. The sensitive data hosted on a lost/stolen device are fully exposed to the adversary. Although password-based authentication mechanisms are available on mobile devices, many users reportedly do not use them, and a device may be lost/stolen while in the unlocked mode. This paper presents the design and evaluation of iLock, a secure and usable defense against data theft on a lost/stolen mobile device. iLock automatically, quickly, and accurately recognizes the user's physical separation from his/her device by detecting and analyzing the changes in wireless signals. Once significant physical separation is detected, the device is immediately locked to prevent data theft. iLock relies on acoustic signals and requires at least one speaker and one microphone that are available on most COTS (commodity-off-the-shelf) mobile devices. Extensive experiments on Samsung Galaxy S5 show that iLock can lock the device with negligible false positives and negatives.
{"title":"iLock: Immediate and Automatic Locking of Mobile Devices against Data Theft","authors":"Tao Li, Yimin Chen, Jingchao Sun, Xiaocong Jin, Yanchao Zhang","doi":"10.1145/2976749.2978294","DOIUrl":"https://doi.org/10.1145/2976749.2978294","url":null,"abstract":"Mobile device losses and thefts are skyrocketing. The sensitive data hosted on a lost/stolen device are fully exposed to the adversary. Although password-based authentication mechanisms are available on mobile devices, many users reportedly do not use them, and a device may be lost/stolen while in the unlocked mode. This paper presents the design and evaluation of iLock, a secure and usable defense against data theft on a lost/stolen mobile device. iLock automatically, quickly, and accurately recognizes the user's physical separation from his/her device by detecting and analyzing the changes in wireless signals. Once significant physical separation is detected, the device is immediately locked to prevent data theft. iLock relies on acoustic signals and requires at least one speaker and one microphone that are available on most COTS (commodity-off-the-shelf) mobile devices. Extensive experiments on Samsung Galaxy S5 show that iLock can lock the device with negligible false positives and negatives.","PeriodicalId":432261,"journal":{"name":"Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114323914","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. Kolesnikov, H. Krawczyk, Yehuda Lindell, A. Malozemoff, T. Rabin
Attribute-based methods provide authorization to parties based on whether their set of attributes (e.g., age, organization, etc.) fulfills a policy. In attribute-based encryption (ABE), authorized parties can decrypt, and in attribute-based credentials (ABCs), authorized parties can authenticate themselves. In this paper, we combine elements of ABE and ABCs together with garbled circuits to construct attribute-based key exchange (ABKE). Our focus is on an interactive solution involving a client that holds a certificate (issued by an authority) vouching for that client's attributes and a server that holds a policy computable on such a set of attributes. The goal is for the server to establish a shared key with the client but only if the client's certified attributes satisfy the policy. Our solution enjoys strong privacy guarantees for both the client and the server, including attribute privacy and unlinkability of client sessions. Our main contribution is a construction of ABKE for arbitrary circuits with high (concrete) efficiency. Specifically, we support general policies expressible as boolean circuits computed on a set of attributes. Even for policies containing hundreds of thousands of gates the performance cost is dominated by two pairing computations per policy input. Put another way, for a similar cost to prior ABE/ABC solutions, which can only support small formulas efficiently, we can support vastly richer policies. We implemented our solution and report on its performance. For policies with 100,000 gates and 200 inputs over a realistic network, the server and client spend 957 ms and 176 ms on computation, respectively. When using offline preprocessing and batch signature verification, this drops to only 243 ms and 97 ms.
{"title":"Attribute-based Key Exchange with General Policies","authors":"V. Kolesnikov, H. Krawczyk, Yehuda Lindell, A. Malozemoff, T. Rabin","doi":"10.1145/2976749.2978359","DOIUrl":"https://doi.org/10.1145/2976749.2978359","url":null,"abstract":"Attribute-based methods provide authorization to parties based on whether their set of attributes (e.g., age, organization, etc.) fulfills a policy. In attribute-based encryption (ABE), authorized parties can decrypt, and in attribute-based credentials (ABCs), authorized parties can authenticate themselves. In this paper, we combine elements of ABE and ABCs together with garbled circuits to construct attribute-based key exchange (ABKE). Our focus is on an interactive solution involving a client that holds a certificate (issued by an authority) vouching for that client's attributes and a server that holds a policy computable on such a set of attributes. The goal is for the server to establish a shared key with the client but only if the client's certified attributes satisfy the policy. Our solution enjoys strong privacy guarantees for both the client and the server, including attribute privacy and unlinkability of client sessions. Our main contribution is a construction of ABKE for arbitrary circuits with high (concrete) efficiency. Specifically, we support general policies expressible as boolean circuits computed on a set of attributes. Even for policies containing hundreds of thousands of gates the performance cost is dominated by two pairing computations per policy input. Put another way, for a similar cost to prior ABE/ABC solutions, which can only support small formulas efficiently, we can support vastly richer policies. We implemented our solution and report on its performance. For policies with 100,000 gates and 200 inputs over a realistic network, the server and client spend 957 ms and 176 ms on computation, respectively. When using offline preprocessing and batch signature verification, this drops to only 243 ms and 97 ms.","PeriodicalId":432261,"journal":{"name":"Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114841767","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}
Malware detection increasingly relies on machine learning techniques, which utilize multiple features to separate the malware from the benign apps. The effectiveness of these techniques primarily depends on the manual feature engineering process, based on human knowledge and intuition. However, given the adversaries' efforts to evade detection and the growing volume of publications on malware behaviors, the feature engineering process likely draws from a fraction of the relevant knowledge. We propose an end-to-end approach for automatic feature engineering. We describe techniques for mining documents written in natural language (e.g. scientific papers) and for representing and querying the knowledge about malware in a way that mirrors the human feature engineering process. Specifically, we first identify abstract behaviors that are associated with malware, and then we map these behaviors to concrete features that can be tested experimentally. We implement these ideas in a system called FeatureSmith, which generates a feature set for detecting Android malware. We train a classifier using these features on a large data set of benign and malicious apps. This classifier achieves a 92.5% true positive rate with only 1% false positives, which is comparable to the performance of a state-of-the-art Android malware detector that relies on manually engineered features. In addition, FeatureSmith is able to suggest informative features that are absent from the manually engineered set and to link the features generated to abstract concepts that describe malware behaviors.
{"title":"FeatureSmith: Automatically Engineering Features for Malware Detection by Mining the Security Literature","authors":"Ziyun Zhu, T. Dumitras","doi":"10.1145/2976749.2978304","DOIUrl":"https://doi.org/10.1145/2976749.2978304","url":null,"abstract":"Malware detection increasingly relies on machine learning techniques, which utilize multiple features to separate the malware from the benign apps. The effectiveness of these techniques primarily depends on the manual feature engineering process, based on human knowledge and intuition. However, given the adversaries' efforts to evade detection and the growing volume of publications on malware behaviors, the feature engineering process likely draws from a fraction of the relevant knowledge. We propose an end-to-end approach for automatic feature engineering. We describe techniques for mining documents written in natural language (e.g. scientific papers) and for representing and querying the knowledge about malware in a way that mirrors the human feature engineering process. Specifically, we first identify abstract behaviors that are associated with malware, and then we map these behaviors to concrete features that can be tested experimentally. We implement these ideas in a system called FeatureSmith, which generates a feature set for detecting Android malware. We train a classifier using these features on a large data set of benign and malicious apps. This classifier achieves a 92.5% true positive rate with only 1% false positives, which is comparable to the performance of a state-of-the-art Android malware detector that relies on manually engineered features. In addition, FeatureSmith is able to suggest informative features that are absent from the manually engineered set and to link the features generated to abstract concepts that describe malware behaviors.","PeriodicalId":432261,"journal":{"name":"Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125729530","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}
Voice authentication is drawing increasing attention and becomes an attractive alternative to passwords for mobile authentication. Recent advances in mobile technology further accelerate the adoption of voice biometrics in an array of diverse mobile applications. However, recent studies show that voice authentication is vulnerable to replay attacks, where an adversary can spoof a voice authentication system using a pre-recorded voice sample collected from the victim. In this paper, we propose VoiceLive, a practical liveness detection system for voice authentication on smartphones. VoiceLive detects a live user by leveraging the user's unique vocal system and the stereo recording of smartphones. In particular, with the phone closely placed to a user's mouth, it captures time-difference-of-arrival (TDoA) changes in a sequence of phoneme sounds to the two microphones of the phone, and uses such unique TDoA dynamic which doesn't exist under replay attacks for liveness detection. VoiceLive is practical as it doesn't require additional hardware but two-channel stereo recording that is supported by virtually all smartphones. Our experimental evaluation with 12 participants and different types of phones shows that VoiceLive achieves over 99% detection accuracy at around 1% Equal Error Rate (EER). Results also show that VoiceLive is robust to different phone placements and is compatible to different sampling rates and phone models.
{"title":"VoiceLive: A Phoneme Localization based Liveness Detection for Voice Authentication on Smartphones","authors":"Linghan Zhang, Sheng Tan, J. Yang, Yingying Chen","doi":"10.1145/2976749.2978296","DOIUrl":"https://doi.org/10.1145/2976749.2978296","url":null,"abstract":"Voice authentication is drawing increasing attention and becomes an attractive alternative to passwords for mobile authentication. Recent advances in mobile technology further accelerate the adoption of voice biometrics in an array of diverse mobile applications. However, recent studies show that voice authentication is vulnerable to replay attacks, where an adversary can spoof a voice authentication system using a pre-recorded voice sample collected from the victim. In this paper, we propose VoiceLive, a practical liveness detection system for voice authentication on smartphones. VoiceLive detects a live user by leveraging the user's unique vocal system and the stereo recording of smartphones. In particular, with the phone closely placed to a user's mouth, it captures time-difference-of-arrival (TDoA) changes in a sequence of phoneme sounds to the two microphones of the phone, and uses such unique TDoA dynamic which doesn't exist under replay attacks for liveness detection. VoiceLive is practical as it doesn't require additional hardware but two-channel stereo recording that is supported by virtually all smartphones. Our experimental evaluation with 12 participants and different types of phones shows that VoiceLive achieves over 99% detection accuracy at around 1% Equal Error Rate (EER). Results also show that VoiceLive is robust to different phone placements and is compatible to different sampling rates and phone models.","PeriodicalId":432261,"journal":{"name":"Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128920926","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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