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Automatic malware classification and new malwaredetection using machine learning Article

Liu LIU, Bao-sheng WANG, Bo YU, Qiu-xi ZHONG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 9, Pages 1336-1347 doi: 10.1631/FITEE.1601325

Abstract: The explosive growth of malware variants poses a major threatto information security.Traditional anti-virus systems based on signaturesfail to classify unknown malware into their correspondingfamiliesand to detect new kinds of malware programs.The decision-making module uses the featuresto classify the malware and to identify suspicious malware98.9%, and successfullydetects 86.7% of the new malware.

Keywords： Malware classification Machine learning n-gram Gray-scale image Feature extraction Malware detection

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A survey of malware behavior description and analysis Review

Bo YU, Ying FANG, Qiang YANG, Yong TANG, Liu LIU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 5, Pages 583-603 doi: 10.1631/FITEE.1601745

Abstract: Behavior-based malware analysis is an important technique for automatically analyzing and detecting malwareBy considering how malware behaves, we can tackle the malware obfuscation problem, which cannot be processedwe can also derive the as-built behavior specifications and cover the entire behavior space of the malwareAlthough there have been several works focusing on malware behavior analysis, such research is far from: malware behavior description, behavior analysis methods, and visualization techniques.

Keywords： Malware behavior Static analysis Dynamic Analysis Behavior data expression Behavior analysis Machinelearning Semantics-based analysis Behavior visualization Malware evolution

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Discovering optimal features using static analysis and a genetic search based method for Android malware None

Ahmad FIRDAUS, Nor Badrul ANUAR, Ahmad KARIM, Mohd Faizal Ab RAZAK

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 6, Pages 712-736 doi: 10.1631/FITEE.1601491

Abstract: With this in mind, security practitioners have conducted static and dynamic analyses to identify malwareHowever, static analysis requires a minimum number of features to efficiently classify malware.

Keywords： Genetic algorithm Static analysis Android Malware Machine learning

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Malware homology identification based on a gene perspective Research Papers

Bing-lin ZHAO, Zheng SHAN, Fu-dong LIU, Bo ZHAO, Yi-hang CHEN, Wen-jie SUN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 6, Pages 801-815 doi: 10.1631/FITEE.1800523

Abstract:

Malware homology identification is important in attacking event tracing, emergency response schemeCurrent malware homology identification methods still rely on manual analysis, which is inefficient andA malware gene is represented by the subgraph, which can describe the homology of malware families.We extract the key subgraph from the function dependency graph as the malware gene by selecting the keyFinally, we use the family genes to guide the identification of malware based on homology.

Keywords： Malware classification Gene perspective Dependency graph Homology analysis