NDLI: MGeT: Malware Gene-Based Malware Dynamic Analyses

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MGeT: Malware Gene-Based Malware Dynamic Analyses

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MGeT: Malware Gene-Based Malware Dynamic Analyses

Content Provider	ACM Digital Library
Author	Zhao, Yue Ding, Jianwei Chen, Zhouguo Sun, Enbo Guo, Yubin Su, Hong
Abstract	Malware, as a malicious software, or applications or execution codes, has become the centerpiece of most security threats in such a unceasing open Internet environment. The essential technology of malware analysis is to extract the characteristics of malware, intended to supply signatures to detection systems and provide evidence for recovery and cleanup. The focal point in the malware analysis is how to detect malicious behaviors versus how to hide a malware analyzer from malware during runtime. In this paper, we propose an approach called Malware Gene Topology Model (MGeT) inspired by Biotechnological Genomics that can quickly detect potential malware from a large amount of software or execution codes including metamorphic or new variants of malware. Instead of extracting the signatures from the malware in the execution file level or operating system level, we identify the key malicious behaviors of malware by the underlying instructions, named malware Gene. We evaluate our method based on real-world datasets and the results demonstrate the advantages of our method over the previous studies, validating the contribution of our method.
Starting Page	96
Ending Page	101
Page Count	6
File Format	PDF
ISBN	9781450348676
DOI	10.1145/3058060.3058065
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-03-17
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Security and protection Malware gene Malware analysis
Content Type	Text
Resource Type	Article

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