Semi-supervised Community Detection

Leng, Mingwei

doi:10.1145/3377170.3377243

Proceedings of the 2019 7th International Conference on Information Technology: IoT and Smart City 2019

DOI: 10.1145/3377170.3377243

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Semi-supervised Community Detection

Mingwei Leng

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2023

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A Survey of Malware Analysis Using Community Detection Algorithms

et al. 2023

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In recent years, we have witnessed an overwhelming and fast proliferation of different types of malware targeting organizations and individuals, which considerably increased the time required to detect malware. The malware developers make this issue worse by spreading many variants of the same malware [13]. To deal with this issue, graph theory techniques, and particularly community detection algorithms, can be leveraged to achieve bulk detection of malware families and variants in order to identify malicious communities instead of focusing on the detection of an individual instance of malware, which could significantly reduce the detection time. In this paper, we review the state-of-the-art malware analysis solutions that employ community detection algorithms, and provide a taxonomy that classifies the solutions with respect to five facets: (1) Analysis task, (2) Community detection approach, (3) Target platform, (4) Analysis type, and (5) Source of features. We present the solutions with respect to the analysis task, which covers: (a) malware detection, (b) malware classification, (c) cyber-threat infrastructure detection, and (d) feature selection. The findings of this survey indicate that there is still room for contributions to further improve the state-of-the-art and address research gaps. Finally, we discuss the advantages and the limitations of the solutions, identify open issues, and provide future research directions.

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A Survey of Malware Analysis Using Community Detection Algorithms

et al. 2023

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Semi-supervised Community Detection

Cited by 1 publication

References 21 publications

A Survey of Malware Analysis Using Community Detection Algorithms

A Survey of Malware Analysis Using Community Detection Algorithms

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