We present the first defence against DNS-amplification DoS attacks, which is compatible with the common DNS servers configurations and with the (important standard) DNSSEC. We show that the proposed DNS-authentication system is efficient, and effectively prevents DNS-based amplification DoS attacks abusing DNS name servers. We present a game-theoretic model and analysis, predicting a wide-spread adoption of our design, sufficient to reduce the threat of DNS amplification DoS attacks. To further reduce costs and provide additional defences for DNS servers, we show how to deploy our design as a cloud based service.
{"title":"DNS authentication as a service: preventing amplification attacks","authors":"A. Herzberg, Haya Schulmann","doi":"10.1145/2664243.2664281","DOIUrl":"https://doi.org/10.1145/2664243.2664281","url":null,"abstract":"We present the first defence against DNS-amplification DoS attacks, which is compatible with the common DNS servers configurations and with the (important standard) DNSSEC. We show that the proposed DNS-authentication system is efficient, and effectively prevents DNS-based amplification DoS attacks abusing DNS name servers. We present a game-theoretic model and analysis, predicting a wide-spread adoption of our design, sufficient to reduce the threat of DNS amplification DoS attacks. To further reduce costs and provide additional defences for DNS servers, we show how to deploy our design as a cloud based service.","PeriodicalId":104443,"journal":{"name":"Proceedings of the 30th Annual Computer Security Applications Conference","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117345349","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}
M. Zubair Rafique, Juan Caballero, Christophe Huygens, W. Joosen
In this work, we present two fundamental primitives for network security: network dialog minimization and network dialog diffing. Network dialog minimization (NDM) simplifies an original dialog with respect to a goal, so that the minimized dialog when replayed still achieves the goal, but requires minimal network communication, achieving significant time and bandwidth savings. We present network delta debugging, the first technique to solve NDM. Network dialog diffing compares two dialogs, aligns them, and identifies their common and different parts. We propose a novel dialog diffing technique that aligns two dialogs by finding a mapping that maximizes similarity. We have applied our techniques to 5 applications. We apply our dialog minimization approach for: building drive-by download milkers for 9 exploit kits, integrating them in a infrastructure that has collected over 14,000 malware samples running from a single machine; efficiently measuring the percentage of popular sites that allow cookie replay, finding that 31% do not destroy the server-side state when a user logs out and that 17% provide cookies that live over a month; simplifying a cumbersome user interface, saving our institution 3 hours of time per year and employee; and finding a new vulnerability in a SIP server. We apply our dialog diffing approach for clustering benign (F-Measure = 100%) and malicious (F-Measure = 87.6%) dialogs.
{"title":"Network dialog minimization and network dialog diffing: two novel primitives for network security applications","authors":"M. Zubair Rafique, Juan Caballero, Christophe Huygens, W. Joosen","doi":"10.1145/2664243.2664261","DOIUrl":"https://doi.org/10.1145/2664243.2664261","url":null,"abstract":"In this work, we present two fundamental primitives for network security: network dialog minimization and network dialog diffing. Network dialog minimization (NDM) simplifies an original dialog with respect to a goal, so that the minimized dialog when replayed still achieves the goal, but requires minimal network communication, achieving significant time and bandwidth savings. We present network delta debugging, the first technique to solve NDM. Network dialog diffing compares two dialogs, aligns them, and identifies their common and different parts. We propose a novel dialog diffing technique that aligns two dialogs by finding a mapping that maximizes similarity. We have applied our techniques to 5 applications. We apply our dialog minimization approach for: building drive-by download milkers for 9 exploit kits, integrating them in a infrastructure that has collected over 14,000 malware samples running from a single machine; efficiently measuring the percentage of popular sites that allow cookie replay, finding that 31% do not destroy the server-side state when a user logs out and that 17% provide cookies that live over a month; simplifying a cumbersome user interface, saving our institution 3 hours of time per year and employee; and finding a new vulnerability in a SIP server. We apply our dialog diffing approach for clustering benign (F-Measure = 100%) and malicious (F-Measure = 87.6%) dialogs.","PeriodicalId":104443,"journal":{"name":"Proceedings of the 30th Annual Computer Security Applications Conference","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121058768","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}
M. Backes, Sven Bugiel, S. Gerling, Philipp von Styp-Rekowsky
We introduce the Android Security Framework (ASF), a generic, extensible security framework for Android that enables the development and integration of a wide spectrum of security models in form of code-based security modules. The design of ASF reflects lessons learned from the literature on established security frameworks (such as Linux Security Modules or the BSD MAC Framework) and intertwines them with the particular requirements and challenges from the design of Android's software stack. ASF provides a novel security API that supports authors of Android security extensions in developing their modules. This overcomes the current unsatisfactory situation to provide security solutions as separate patches to the Android software stack or to embed them into Android's mainline codebase. This system security extensibility is of particular benefit for enterprise or government solutions that require deployment of advanced security models, not supported by vanilla Android. We present a prototypical implementation of ASF and demonstrate its effectiveness and efficiency by modularizing different security models from related work, such as dynamic permissions, inlined reference monitoring, and type enforcement.
{"title":"Android security framework: extensible multi-layered access control on Android","authors":"M. Backes, Sven Bugiel, S. Gerling, Philipp von Styp-Rekowsky","doi":"10.1145/2664243.2664265","DOIUrl":"https://doi.org/10.1145/2664243.2664265","url":null,"abstract":"We introduce the Android Security Framework (ASF), a generic, extensible security framework for Android that enables the development and integration of a wide spectrum of security models in form of code-based security modules. The design of ASF reflects lessons learned from the literature on established security frameworks (such as Linux Security Modules or the BSD MAC Framework) and intertwines them with the particular requirements and challenges from the design of Android's software stack. ASF provides a novel security API that supports authors of Android security extensions in developing their modules. This overcomes the current unsatisfactory situation to provide security solutions as separate patches to the Android software stack or to embed them into Android's mainline codebase. This system security extensibility is of particular benefit for enterprise or government solutions that require deployment of advanced security models, not supported by vanilla Android. We present a prototypical implementation of ASF and demonstrate its effectiveness and efficiency by modularizing different security models from related work, such as dynamic permissions, inlined reference monitoring, and type enforcement.","PeriodicalId":104443,"journal":{"name":"Proceedings of the 30th Annual Computer Security Applications Conference","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116871734","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}
Hongyu Gao, Yi Yang, Kai Bu, Yan Chen, Doug Downey, Kathy Lee, A. Choudhary
In online social networks (OSNs), spam originating from friends and acquaintances not only reduces the joy of Internet surfing but also causes damage to less security-savvy users. Prior countermeasures combat OSN spam from different angles. Due to the diversity of spam, there is hardly any existing method that can independently detect the majority or most of OSN spam. In this paper, we empirically analyze the textual pattern of a large collection of OSN spam. An inspiring finding is that the majority (63.0%) of the collected spam is generated with underlying templates. We therefore propose extracting templates of spam detected by existing methods and then matching messages against the templates toward accurate and fast spam detection. We implement this insight through Tangram, an OSN spam filtering system that performs online inspection on the stream of user-generated messages. Tangram automatically divides OSN spam into segments and uses the segments to construct templates to filter future spam. Experimental results show that Tangram is highly accurate and can rapidly generate templates to throttle newly emerged campaigns. Specifically, Tangram detects the most prevalent template-based spam with 95.7% true positive rate, whereas the existing template generation approach detects only 32.3%. The integration of Tangram and its auxiliary spam filter achieves an overall accuracy of 85.4% true positive rate and 0.33% false positive rate.
{"title":"Spam ain't as diverse as it seems: throttling OSN spam with templates underneath","authors":"Hongyu Gao, Yi Yang, Kai Bu, Yan Chen, Doug Downey, Kathy Lee, A. Choudhary","doi":"10.1145/2664243.2664251","DOIUrl":"https://doi.org/10.1145/2664243.2664251","url":null,"abstract":"In online social networks (OSNs), spam originating from friends and acquaintances not only reduces the joy of Internet surfing but also causes damage to less security-savvy users. Prior countermeasures combat OSN spam from different angles. Due to the diversity of spam, there is hardly any existing method that can independently detect the majority or most of OSN spam. In this paper, we empirically analyze the textual pattern of a large collection of OSN spam. An inspiring finding is that the majority (63.0%) of the collected spam is generated with underlying templates. We therefore propose extracting templates of spam detected by existing methods and then matching messages against the templates toward accurate and fast spam detection. We implement this insight through Tangram, an OSN spam filtering system that performs online inspection on the stream of user-generated messages. Tangram automatically divides OSN spam into segments and uses the segments to construct templates to filter future spam. Experimental results show that Tangram is highly accurate and can rapidly generate templates to throttle newly emerged campaigns. Specifically, Tangram detects the most prevalent template-based spam with 95.7% true positive rate, whereas the existing template generation approach detects only 32.3%. The integration of Tangram and its auxiliary spam filter achieves an overall accuracy of 85.4% true positive rate and 0.33% false positive rate.","PeriodicalId":104443,"journal":{"name":"Proceedings of the 30th Annual Computer Security Applications Conference","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129018390","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}
Web browsers are one of the most used, complex, and popular software systems nowadays. They are prone to dangling pointers that result in use-after-free vulnerabilites and this is the de-facto way to exploit them. From a technical point of view, an attacker uses a technique called vtable hijacking to exploit such bugs. More specifically, she crafts bogus virtual tables and lets a freed C++ object point to it in order to gain control over the program at virtual function call sites. In this paper, we present a novel approach towards mitigating and detecting such attacks against C++ binary code. We propose a static binary analysis technique to extract virtual function call site information in an automated way. Leveraging this information, we instrument the given binary executable and add runtime policy enforcements to thwart the illegal usage of these call sites. We implemented the proposed techniques in a prototype called T-VIP and successfully hardened three versions of Microsoft's Internet Explorer and Mozilla Firefox. An evaluation with several zero-day exploits demonstrates that our method prevents all of them. Performance benchmarks both on micro and macro level indicate that the overhead is reasonable with about 2.2%, which is only slightly higher compared to recent compiler-based approaches that address this problem.
{"title":"Towards automated integrity protection of C++ virtual function tables in binary programs","authors":"R. Gawlik, Thorsten Holz","doi":"10.1145/2664243.2664249","DOIUrl":"https://doi.org/10.1145/2664243.2664249","url":null,"abstract":"Web browsers are one of the most used, complex, and popular software systems nowadays. They are prone to dangling pointers that result in use-after-free vulnerabilites and this is the de-facto way to exploit them. From a technical point of view, an attacker uses a technique called vtable hijacking to exploit such bugs. More specifically, she crafts bogus virtual tables and lets a freed C++ object point to it in order to gain control over the program at virtual function call sites. In this paper, we present a novel approach towards mitigating and detecting such attacks against C++ binary code. We propose a static binary analysis technique to extract virtual function call site information in an automated way. Leveraging this information, we instrument the given binary executable and add runtime policy enforcements to thwart the illegal usage of these call sites. We implemented the proposed techniques in a prototype called T-VIP and successfully hardened three versions of Microsoft's Internet Explorer and Mozilla Firefox. An evaluation with several zero-day exploits demonstrates that our method prevents all of them. Performance benchmarks both on micro and macro level indicate that the overhead is reasonable with about 2.2%, which is only slightly higher compared to recent compiler-based approaches that address this problem.","PeriodicalId":104443,"journal":{"name":"Proceedings of the 30th Annual Computer Security Applications Conference","volume":"205 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127034811","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}
Qian Feng, Aravind Prakash, Heng Yin, Zhiqiang Lin
Memory forensic analysis collects evidence for digital crimes and malware attacks from the memory of a live system. It is increasingly valuable, especially in cloud computing. However, memory analysis on on commodity operating systems (such as Microsoft Windows) faces the following key challenges: (1) a partial knowledge of kernel data structures; (2) difficulty in handling ambiguous pointers; and (3) lack of robustness by relying on soft constraints that can be easily violated by kernel attacks. To address these challenges, we present MACE, a memory analysis system that can extract a more complete view of the kernel data structures for closed-source operating systems and significantly improve the robustness by only leveraging pointer constraints (which are hard to manipulate) and evaluating these constraint globally (to even tolerate certain amount of pointer attacks). We have evaluated MACE on 100 memory images for Windows XP SP3 and Windows 7 SP0. Overall, MACE can construct a kernel object graph from a memory image in just a few minutes, and achieves over 95% recall and over 96% precision. Our experiments on real-world rootkit samples and synthetic attacks further demonstrate that MACE outperforms other external memory analysis tools with respect to wider coverage and better robustness.
内存取证分析从实时系统的内存中收集数字犯罪和恶意软件攻击的证据。它越来越有价值，尤其是在云计算领域。然而，对商品操作系统(如Microsoft Windows)的内存分析面临以下主要挑战:(1)内核数据结构的部分知识;(2)歧义指针处理困难;(3)依赖于容易被内核攻击破坏的软约束，缺乏鲁棒性。为了应对这些挑战，我们提出了MACE，这是一个内存分析系统，它可以为闭源操作系统提取更完整的内核数据结构视图，并通过仅利用指针约束(难以操作)和全局评估这些约束(甚至容忍一定数量的指针攻击)来显着提高鲁棒性。我们在Windows XP SP3和Windows 7 SP0的100个内存映像上评估了MACE。总的来说，MACE可以在几分钟内从内存图像构建内核对象图，并且达到95%以上的召回率和96%以上的精度。我们对真实世界的rootkit样本和合成攻击的实验进一步证明，MACE在更广泛的覆盖范围和更好的鲁棒性方面优于其他外部内存分析工具。
{"title":"MACE: high-coverage and robust memory analysis for commodity operating systems","authors":"Qian Feng, Aravind Prakash, Heng Yin, Zhiqiang Lin","doi":"10.1145/2664243.2664248","DOIUrl":"https://doi.org/10.1145/2664243.2664248","url":null,"abstract":"Memory forensic analysis collects evidence for digital crimes and malware attacks from the memory of a live system. It is increasingly valuable, especially in cloud computing. However, memory analysis on on commodity operating systems (such as Microsoft Windows) faces the following key challenges: (1) a partial knowledge of kernel data structures; (2) difficulty in handling ambiguous pointers; and (3) lack of robustness by relying on soft constraints that can be easily violated by kernel attacks. To address these challenges, we present MACE, a memory analysis system that can extract a more complete view of the kernel data structures for closed-source operating systems and significantly improve the robustness by only leveraging pointer constraints (which are hard to manipulate) and evaluating these constraint globally (to even tolerate certain amount of pointer attacks). We have evaluated MACE on 100 memory images for Windows XP SP3 and Windows 7 SP0. Overall, MACE can construct a kernel object graph from a memory image in just a few minutes, and achieves over 95% recall and over 96% precision. Our experiments on real-world rootkit samples and synthetic attacks further demonstrate that MACE outperforms other external memory analysis tools with respect to wider coverage and better robustness.","PeriodicalId":104443,"journal":{"name":"Proceedings of the 30th Annual Computer Security Applications Conference","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123983810","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}
Node.js is a popular JavaScript server-side framework with an efficient runtime for cloud-based event-driven architectures. Its strength is the presence of thousands of third-party libraries which allow developers to quickly build and deploy applications. These very libraries are a source of security threats as a vulnerability in one library can (and in some cases did) compromise one's entire server. In order to support the least-privilege integration of libraries, we developed NodeSentry, the first security architecture for server-side JavaScript. Our policy enforcement infrastructure supports an easy deployment of web-hardening techniques and access control policies on interactions between libraries and their environment, including any dependent library. We discuss the implementation of NodeSentry, and present its practical evaluation. For hundreds of concurrent clients, NodeSentry has the same capacity and throughput as plain Node.js. Only on a large scale, when Node.js itself yields to a heavy load, NodeSentry shows a limited overhead.
{"title":"NodeSentry: least-privilege library integration for server-side JavaScript","authors":"Willem De Groef, F. Massacci, F. Piessens","doi":"10.1145/2664243.2664276","DOIUrl":"https://doi.org/10.1145/2664243.2664276","url":null,"abstract":"Node.js is a popular JavaScript server-side framework with an efficient runtime for cloud-based event-driven architectures. Its strength is the presence of thousands of third-party libraries which allow developers to quickly build and deploy applications. These very libraries are a source of security threats as a vulnerability in one library can (and in some cases did) compromise one's entire server. In order to support the least-privilege integration of libraries, we developed NodeSentry, the first security architecture for server-side JavaScript. Our policy enforcement infrastructure supports an easy deployment of web-hardening techniques and access control policies on interactions between libraries and their environment, including any dependent library. We discuss the implementation of NodeSentry, and present its practical evaluation. For hundreds of concurrent clients, NodeSentry has the same capacity and throughput as plain Node.js. Only on a large scale, when Node.js itself yields to a heavy load, NodeSentry shows a limited overhead.","PeriodicalId":104443,"journal":{"name":"Proceedings of the 30th Annual Computer Security Applications Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125449991","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}
Arthur Gervais, Ghassan O. Karame, Damian Gruber, Srdjan Capkun
Lightweight Bitcoin clients are gaining increasing adoption among Bitcoin users, owing to their reduced resource and bandwidth consumption. These clients support a simplified payment verification (SPV) mode as they are only required to download and verify a part of the block chain---thus supporting the usage of Bitcoin on constrained devices, such as smartphones. SPV clients rely on Bloom filters to receive transactions that are relevant to their local wallet. These filters embed all the Bitcoin addresses used by the SPV clients, and are outsourced to more powerful Bitcoin nodes which then only forward to those clients transactions relevant to their outsourced Bloom filters. In this paper, we explore the privacy of existing SPV clients. We show analytically and empirically that the reliance on Bloom filters within existing SPV clients leaks considerable information about the addresses of Bitcoin users. Our results show that an SPV client who uses a modest number of Bitcoin addresses (e.g., < 20) risks revealing almost all of his addresses. We also show that this information leakage is further exacerbated when users restart their SPV clients and/or when the adversary has access to more than one Bloom filter pertaining to the same SPV client. Motivated by these findings, we propose an efficient countermeasure to enhance the privacy of users which rely on SPV clients; our proposal can be directly integrated within existing SPV client implementations.
{"title":"On the privacy provisions of Bloom filters in lightweight bitcoin clients","authors":"Arthur Gervais, Ghassan O. Karame, Damian Gruber, Srdjan Capkun","doi":"10.1145/2664243.2664267","DOIUrl":"https://doi.org/10.1145/2664243.2664267","url":null,"abstract":"Lightweight Bitcoin clients are gaining increasing adoption among Bitcoin users, owing to their reduced resource and bandwidth consumption. These clients support a simplified payment verification (SPV) mode as they are only required to download and verify a part of the block chain---thus supporting the usage of Bitcoin on constrained devices, such as smartphones. SPV clients rely on Bloom filters to receive transactions that are relevant to their local wallet. These filters embed all the Bitcoin addresses used by the SPV clients, and are outsourced to more powerful Bitcoin nodes which then only forward to those clients transactions relevant to their outsourced Bloom filters. In this paper, we explore the privacy of existing SPV clients. We show analytically and empirically that the reliance on Bloom filters within existing SPV clients leaks considerable information about the addresses of Bitcoin users. Our results show that an SPV client who uses a modest number of Bitcoin addresses (e.g., < 20) risks revealing almost all of his addresses. We also show that this information leakage is further exacerbated when users restart their SPV clients and/or when the adversary has access to more than one Bloom filter pertaining to the same SPV client. Motivated by these findings, we propose an efficient countermeasure to enhance the privacy of users which rely on SPV clients; our proposal can be directly integrated within existing SPV client implementations.","PeriodicalId":104443,"journal":{"name":"Proceedings of the 30th Annual Computer Security Applications Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130814080","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}
Drive-by download attacks, which exploit vulnerabilities of web browsers to control client computers, have become a major venue for attackers. To detect such attacks, researchers have proposed many approaches such as anomaly-based [22, 23] and vulnerability-based [44, 50] detections. However, anomaly-based approaches are vulnerable to data pollution, and existing vulnerability-based approaches cannot accurately describe the vulnerability condition of all the drive-by download attacks. In this paper, we propose a vulnerability-based approach, namely JShield, which uses novel opcode vulnerability signature, a deterministic finite automaton (DFA) with a variable pool at opcode level, to match drive-by download vulnerabilities. We investigate all the JavaScript engine vulnerabilities of web browsers from 2009 to 2014, as well as those of portable document files (PDF) readers from 2007 to 2014. JShield is able to match all of those vulnerabilities; furthermore, the overall evaluation shows that JShield is so lightweight that it only adds 2.39 percent of overhead to original execution as the median among top 500 Alexa web sites.
{"title":"JShield: towards real-time and vulnerability-based detection of polluted drive-by download attacks","authors":"Yinzhi Cao, Xiang Pan, Yan Chen, Jianwei Zhuge","doi":"10.1145/2664243.2664256","DOIUrl":"https://doi.org/10.1145/2664243.2664256","url":null,"abstract":"Drive-by download attacks, which exploit vulnerabilities of web browsers to control client computers, have become a major venue for attackers. To detect such attacks, researchers have proposed many approaches such as anomaly-based [22, 23] and vulnerability-based [44, 50] detections. However, anomaly-based approaches are vulnerable to data pollution, and existing vulnerability-based approaches cannot accurately describe the vulnerability condition of all the drive-by download attacks. In this paper, we propose a vulnerability-based approach, namely JShield, which uses novel opcode vulnerability signature, a deterministic finite automaton (DFA) with a variable pool at opcode level, to match drive-by download vulnerabilities. We investigate all the JavaScript engine vulnerabilities of web browsers from 2009 to 2014, as well as those of portable document files (PDF) readers from 2007 to 2014. JShield is able to match all of those vulnerabilities; furthermore, the overall evaluation shows that JShield is so lightweight that it only adds 2.39 percent of overhead to original execution as the median among top 500 Alexa web sites.","PeriodicalId":104443,"journal":{"name":"Proceedings of the 30th Annual Computer Security Applications Conference","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117265317","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}
Tamas K. Lengyel, S. Maresca, B. Payne, George D. Webster, S. Vogl, A. Kiayias
Malware is one of the biggest security threats on the Internet today and deploying effective defensive solutions requires the rapid analysis of a continuously increasing number of malware samples. With the proliferation of metamorphic malware the analysis is further complicated as the efficacy of signature-based static analysis systems is greatly reduced. While dynamic malware analysis is an effective alternative, the approach faces significant challenges as the ever increasing number of samples requiring analysis places a burden on hardware resources. At the same time modern malware can both detect the monitoring environment and hide in unmonitored corners of the system. In this paper we present DRAKVUF, a novel dynamic malware analysis system designed to address these challenges by building on the latest hardware virtualization extensions and the Xen hypervisor. We present a technique for improving stealth by initiating the execution of malware samples without leaving any trace in the analysis machine. We also present novel techniques to eliminate blind-spots created by kernel-mode rootkits by extending the scope of monitoring to include kernel internal functions, and to monitor file-system accesses through the kernel's heap allocations. With extensive tests performed on recent malware samples we show that DRAKVUF achieves significant improvements in conserving hardware resources while providing a stealthy, in-depth view into the behavior of modern malware.
{"title":"Scalability, fidelity and stealth in the DRAKVUF dynamic malware analysis system","authors":"Tamas K. Lengyel, S. Maresca, B. Payne, George D. Webster, S. Vogl, A. Kiayias","doi":"10.1145/2664243.2664252","DOIUrl":"https://doi.org/10.1145/2664243.2664252","url":null,"abstract":"Malware is one of the biggest security threats on the Internet today and deploying effective defensive solutions requires the rapid analysis of a continuously increasing number of malware samples. With the proliferation of metamorphic malware the analysis is further complicated as the efficacy of signature-based static analysis systems is greatly reduced. While dynamic malware analysis is an effective alternative, the approach faces significant challenges as the ever increasing number of samples requiring analysis places a burden on hardware resources. At the same time modern malware can both detect the monitoring environment and hide in unmonitored corners of the system. In this paper we present DRAKVUF, a novel dynamic malware analysis system designed to address these challenges by building on the latest hardware virtualization extensions and the Xen hypervisor. We present a technique for improving stealth by initiating the execution of malware samples without leaving any trace in the analysis machine. We also present novel techniques to eliminate blind-spots created by kernel-mode rootkits by extending the scope of monitoring to include kernel internal functions, and to monitor file-system accesses through the kernel's heap allocations. With extensive tests performed on recent malware samples we show that DRAKVUF achieves significant improvements in conserving hardware resources while providing a stealthy, in-depth view into the behavior of modern malware.","PeriodicalId":104443,"journal":{"name":"Proceedings of the 30th Annual Computer Security Applications Conference","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114995419","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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