基于性能计数器的在线恶意软件检测可行性研究

J. Demme, Matthew Maycock, J. Schmitz, Adrian Tang, A. Waksman, S. Sethumadhavan, S. Stolfo
{"title":"基于性能计数器的在线恶意软件检测可行性研究","authors":"J. Demme, Matthew Maycock, J. Schmitz, Adrian Tang, A. Waksman, S. Sethumadhavan, S. Stolfo","doi":"10.1145/2485922.2485970","DOIUrl":null,"url":null,"abstract":"The proliferation of computers in any domain is followed by the proliferation of malware in that domain. Systems, including the latest mobile platforms, are laden with viruses, rootkits, spyware, adware and other classes of malware. Despite the existence of anti-virus software, malware threats persist and are growing as there exist a myriad of ways to subvert anti-virus (AV) software. In fact, attackers today exploit bugs in the AV software to break into systems. In this paper, we examine the feasibility of building a malware detector in hardware using existing performance counters. We find that data from performance counters can be used to identify malware and that our detection techniques are robust to minor variations in malware programs. As a result, after examining a small set of variations within a family of malware on Android ARM and Intel Linux platforms, we can detect many variations within that family. Further, our proposed hardware modifications allow the malware detector to run securely beneath the system software, thus setting the stage for AV implementations that are simpler and less buggy than software AV. Combined, the robustness and security of hardware AV techniques have the potential to advance state-of-the-art online malware detection.","PeriodicalId":20555,"journal":{"name":"Proceedings of the 40th Annual International Symposium on Computer Architecture","volume":"600 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"409","resultStr":"{\"title\":\"On the feasibility of online malware detection with performance counters\",\"authors\":\"J. Demme, Matthew Maycock, J. Schmitz, Adrian Tang, A. Waksman, S. Sethumadhavan, S. Stolfo\",\"doi\":\"10.1145/2485922.2485970\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The proliferation of computers in any domain is followed by the proliferation of malware in that domain. Systems, including the latest mobile platforms, are laden with viruses, rootkits, spyware, adware and other classes of malware. Despite the existence of anti-virus software, malware threats persist and are growing as there exist a myriad of ways to subvert anti-virus (AV) software. In fact, attackers today exploit bugs in the AV software to break into systems. In this paper, we examine the feasibility of building a malware detector in hardware using existing performance counters. We find that data from performance counters can be used to identify malware and that our detection techniques are robust to minor variations in malware programs. As a result, after examining a small set of variations within a family of malware on Android ARM and Intel Linux platforms, we can detect many variations within that family. Further, our proposed hardware modifications allow the malware detector to run securely beneath the system software, thus setting the stage for AV implementations that are simpler and less buggy than software AV. Combined, the robustness and security of hardware AV techniques have the potential to advance state-of-the-art online malware detection.\",\"PeriodicalId\":20555,\"journal\":{\"name\":\"Proceedings of the 40th Annual International Symposium on Computer Architecture\",\"volume\":\"600 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"409\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 40th Annual International Symposium on Computer Architecture\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2485922.2485970\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 40th Annual International Symposium on Computer Architecture","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2485922.2485970","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 409

摘要

计算机在任何领域的扩散都伴随着该领域恶意软件的扩散。系统,包括最新的移动平台,充斥着病毒、rootkit、间谍软件、广告软件和其他类型的恶意软件。尽管反病毒软件已经存在,但恶意软件的威胁仍然存在,而且还在不断增长,因为有无数种方法可以破坏反病毒(AV)软件。事实上,今天的攻击者利用反病毒软件中的漏洞侵入系统。在本文中,我们研究了利用现有的性能计数器在硬件上构建恶意软件检测器的可行性。我们发现来自性能计数器的数据可用于识别恶意软件,并且我们的检测技术对恶意软件程序中的微小变化具有鲁棒性。因此,在检查了Android、ARM和Intel Linux平台上的恶意软件家族中的一小部分变体后,我们可以在该家族中检测到许多变体。此外,我们提出的硬件修改允许恶意软件检测器在系统软件下安全运行,从而为比软件反病毒更简单、更少错误的反病毒实现奠定了基础。结合起来,硬件反病毒技术的鲁棒性和安全性有可能推进最先进的在线恶意软件检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
On the feasibility of online malware detection with performance counters
The proliferation of computers in any domain is followed by the proliferation of malware in that domain. Systems, including the latest mobile platforms, are laden with viruses, rootkits, spyware, adware and other classes of malware. Despite the existence of anti-virus software, malware threats persist and are growing as there exist a myriad of ways to subvert anti-virus (AV) software. In fact, attackers today exploit bugs in the AV software to break into systems. In this paper, we examine the feasibility of building a malware detector in hardware using existing performance counters. We find that data from performance counters can be used to identify malware and that our detection techniques are robust to minor variations in malware programs. As a result, after examining a small set of variations within a family of malware on Android ARM and Intel Linux platforms, we can detect many variations within that family. Further, our proposed hardware modifications allow the malware detector to run securely beneath the system software, thus setting the stage for AV implementations that are simpler and less buggy than software AV. Combined, the robustness and security of hardware AV techniques have the potential to advance state-of-the-art online malware detection.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
AC-DIMM: associative computing with STT-MRAM Deconfigurable microprocessor architectures for silicon debug acceleration Thin servers with smart pipes: designing SoC accelerators for memcached An experimental study of data retention behavior in modern DRAM devices: implications for retention time profiling mechanisms Dynamic reduction of voltage margins by leveraging on-chip ECC in Itanium II processors
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1