Research on Evasion and Detection of Malicious JavaScript Code

2024
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Authors: Yujie Ma; Haokai Wu; Yu-an Tan; Yuanzhang Li
Published in: Machine Learning for Cyber Security
Publisher: Springer Nature Singapore

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Abstract

The chapter delves into a detailed introduction of a JavaScript malware detection method based on taint analysis. It begins by analyzing how malicious JavaScript code implements its malicious functions and summarizes semantic features that reflect the maliciousness of the code. The method defines taint sources and sinks, proposes a new taint propagation technique, and demonstrates its effectiveness, particularly in scenarios with obfuscation. The chapter also covers the experimental validation of the method, showing significant differences in taint distributions between malicious and benign samples and highlighting the method's robustness against obfuscation techniques.

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Metadata
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Title: Research on Evasion and Detection of Malicious JavaScript Code
Authors: Yujie Ma
Haokai Wu
Yu-an Tan
Yuanzhang Li
Publisher: Springer Nature Singapore
Book: Machine Learning for Cyber Security
Print ISBN: 978-981-9724-57-4

Electronic ISBN: 978-981-9724-58-1

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-981-97-2458-1_8

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