SCAGuard: Detection and Classification of Cache Side-Channel Attacks via Attack Behavior Modeling and Similarity Comparison

doi:10.1109/DAC56929.2023.10247890

	SCAGuard: Detection and Classification of Cache Side-Channel Attacks via Attack Behavior Modeling and Similarity Comparison
	Limin Wang 1; Lei Bu 1; Fu Song2
	2023-07-09
会议录名称	2023 60TH ACM/IEEE DESIGN AUTOMATION CONFERENCE (DAC)
ISSN	0738-100X
卷号	2023-July
发表状态	已发表
DOI	10.1109/DAC56929.2023.10247890
摘要	Cache side-channel attacks (CSCAs), capable of deducing secrets by analyzing timing differences in the shared cache behavior of modern processors, pose a serious security threat. While there are approaches for detecting CSCAs and mitigating information leaks, they either fail to detect and classify new variants or have to impractically update deployed systems (e.g., CPU). In this work, we propose a novel approach, named SCAGuard, to detect and classify CSCAs via attack behavior modeling and similarity comparison. Specifically, we introduce the notion of cache state transition enhanced basic block sequences (CST-BBSes) to model attack behaviors which is able to capture both attack-relevant syntactic code information and semantic cache information. We propose an approach to automatically construct CST-BBS models from binary programs. To detect and classify attacks, we adapt a dynamic time warping algorithm to compare the similarity of CST-BBSes between attack and target programs. We implement our approach in a tool SCAGuard and evaluate it using real-world attacks and diverse benign programs. The results confirm the effectiveness of our approach, compared over existing detection approaches. In particular, SCAGuard significantly outperforms the other detection approaches on new variants. © 2023 IEEE.
关键词	Adaptation models Codes Program processors Heuristic algorithms Semantics Side-channel attacks Syntactics
会议名称	60th ACM/IEEE Design Automation Conference, DAC 2023
会议地点	San Francisco, CA, USA
会议日期	9-13 July 2023
URL	查看原文
收录类别	EI
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20234014844404
EI主题词	Side channel attack
EI分类号	716.1 Information Theory and Signal Processing ; 903.1 Information Sources and Analysis
原始文献类型	Conference article (CA)
来源库	IEEE
引用统计	正在获取...
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/333451
专题	信息科学与技术学院信息科学与技术学院_PI研究组_宋富组
作者单位	1.State Key Laboratory of Novel Software Techniques, Nanjing University, Nanjing, China 2.School of Information Science and Technology, ShanghaiTech University, Shanghai, China
推荐引用方式 GB/T 7714	Limin Wang,Lei Bu,Fu Song. SCAGuard: Detection and Classification of Cache Side-Channel Attacks via Attack Behavior Modeling and Similarity Comparison[C]:Institute of Electrical and Electronics Engineers Inc.,2023.