Journal of Software:2016.27(12):3172-3191

(解放军信息工程大学, 河南 郑州 450001;数学工程与先进计算国家重点实验室, 河南 郑州 450001)
Data Characteristics-Based Kernel Malware Detection
CHEN Zhi-Feng,LI Qing-Bao,ZHANG Ping,DING Wen-Bo
(PLA Information Engineering University, Zhengzhou 450001, China;State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450001, China)
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Received:January 11, 2015    Revised:September 10, 2015
> 中文摘要: 内核恶意软件对操作系统的安全造成了严重威胁.现有的内核恶意软件检测方法主要从代码角度出发,无法检测代码复用、代码混淆攻击,且少量检测数据篡改攻击的方法因不变量特征有限导致检测能力受限.针对这些问题,提出了一种基于数据特征的内核恶意软件检测方法,通过分析内核运行过程中内核数据对象的访问过程,构建了内核数据对象访问模型;然后,基于该模型讨论了构建数据特征的过程,采用动态监控和静态分析相结合的方法识别内核数据对象,利用EPT监控内存访问操作构建数据特征;最后讨论了基于数据特征的内核恶意软件检测算法.在此基础上,实现了内核恶意软件检测原型系统MDS-DCB,并通过实验评测MDS-DCB的有效性和性能.实验结果表明:MDS-DCB能够有效检测内核恶意软件,且性能开销在可接受的范围内.
Abstract:Kernel malwares are serious threat to the security of operating system. Existing kernel malware detection methods are mainly code view-based, which cannot detect the code reuse and code obfuscation attacks; and a small number of available detection methods for data attacks have limit detection capability due to the limited data invariants. To solve these problems, a kernel malware detection technique based on data characteristics is proposed. First, a kernel data object access model is built by analyzing the kernel object access process during the kernel running. Then, data characteristics building process is discussed based on the model. The process uses dynamic monitoring and static analysis methods to identify the kernel data objects, and employs EPT to monitor the memory access operations to build data characteristics. Finally, the kernel malware detection algorithm based on data characteristics is realized. With this groundwork, a kernel malware detection prototype system MDS-DCB is designed and implemented based on Bitvisor, and the effectiveness and performance overhead of MDS-DCB are evaluated by comprehensive experiments. The results show that MDS-DCB can effectively detect kernel malwares, and the performance penalty induced by MDS-DCB is acceptable.
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基金项目:“核高基”国家科技重大专项(2013JH00103);国家高技术研究发展计划(863)(2009AA01Z434) “核高基”国家科技重大专项(2013JH00103);国家高技术研究发展计划(863)(2009AA01Z434)
Foundation items:National Science and Technology Major Project of China (2013JH00103); National High-Tech R&D Program of China (863) (863) (2009AA01Z434)
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CHEN Zhi-Feng,LI Qing-Bao,ZHANG Ping,DING Wen-Bo.Data Characteristics-Based Kernel Malware Detection.Journal of Software,2016,27(12):3172-3191