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ORIGINAL RESEARCH article

Front. Phys.
Sec. Social Physics
Volume 12 - 2024 | doi: 10.3389/fphy.2024.1493209
This article is part of the Research Topic Network Learning and Propagation Dynamics Analysis View all 11 articles

Dynamic analysis of malicious behavior propagation based on feature selection in software network

Provisionally accepted
Huajian Xue Huajian Xue 1Yali Wang Yali Wang 2*Qiguang Tang Qiguang Tang 3*
  • 1 Tongling University, Tongling, China
  • 2 Suzhou city University, suzhou jiangsu china, China
  • 3 Zhongyuan Oilfield Oil and Gas Engineering Service Center, puyang henan china, China

The final, formatted version of the article will be published soon.

    In the era of big data, the propagation of malicious software poses a significant threat to corporate data security. To safeguard data assets from the encroachment of malware, it is essential to conduct a dynamic analysis of various information propagation behaviors within software. This paper introduces a dynamic analysis detection method for malicious behavior based on feature extraction (MBDFE), designed to effectively identify and thwart the spread of malicious software. The method is divided into three stages: First, variable-length N-gram algorithms are utilized to extract subsequences of varying lengths from the sample API call sequences as continuous dynamic features. Second, feature selection techniques based on information gain are employed to identify suitable classification features. Lastly, recurrent neural networks (RNN) are applied for the classification training and prediction of diverse software behaviors. Experimental results and analysis demonstrate that this approach can accurately detect and promptly interrupt the information dissemination of malicious software when such behavior occurs, thereby enhancing the precision and timeliness of malware detection.

    Keywords: recurrent neural networks 1, information propagation 2, Feature selection 3, dynamic analysis 4, software network 5

    Received: 08 Sep 2024; Accepted: 14 Oct 2024.

    Copyright: © 2024 Xue, Wang and Tang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Yali Wang, Suzhou city University, suzhou jiangsu china, China
    Qiguang Tang, Zhongyuan Oilfield Oil and Gas Engineering Service Center, puyang henan china, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.