Research on Malicious Code Analysis Method Based on Semi-supervised Learning

He, Tingting; Xue, Jingfeng; Jian-wen, FU; Wang, Yong; Shan, Chun

doi:10.1007/978-981-10-7080-8_17

Cited by 2 publications

(1 citation statement)

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“…The first category of papers referenced the challenge to either perform an abstract comparison or highlight the importance of machine learning for malware classification in industry, where the size of data is huge [43,19,28,47,18,38,49,44,25,53,46,21,4,57,16,17,39,50]. Papers in the second category performed partial or complete evaluation on the dataset to verify the effectiveness and/or efficiency of their proposed approach for various tasks.…”

Section: Citations Comparisonmentioning

confidence: 99%

Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification

Ahmadi

Ulyanov

Semenov

et al. 2016

Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy

273

198

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Modern malware is designed with mutation characteristics, namely polymorphism and metamorphism, which causes an enormous growth in the number of variants of malware samples. Categorization of malware samples on the basis of their behaviors is essential for the computer security community, because they receive huge number of malware everyday, and the signature extraction process is usually based on malicious parts characterizing malware families. Microsoft released a malware classification challenge in 2015 with a huge dataset of near 0.5 terabytes of data, containing more than 20K malware samples. The analysis of this dataset inspired the development of a novel paradigm that is effective in categorizing malware variants into their actual family groups. This paradigm is presented and discussed in the present paper, where emphasis has been given to the phases related to the extraction, and selection of a set of novel features for the effective representation of malware samples. Features can be grouped according to different characteristics of malware behavior, and their fusion is performed according to a per-class weighting paradigm. The proposed method achieved a very high accuracy ($\approx$ 0.998) on the Microsoft Malware Challenge dataset

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