Detection and classification of malicious JavaScript via attack behavior modelling

• Main Authors: XUE, Yinxing, WANG, Junjie, LIU, Yang, XIAO, Hao, SUN, Jun, CHANDRAMOHAN, Mahinthan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: malware detection; malicious JavaScript; L*; behavior modelling; Programming Languages and Compilers; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4953; https://ink.library.smu.edu.sg/context/sis_research/article/5956/viewcontent/issta2015.pdf
• Institution: Singapore Management University

Existing malicious JavaScript (JS) detection tools and commercial anti-virus tools mostly use feature-based or signature-based approaches to detect JS malware. These tools are weak in resistance to obfuscation and JS malware variants, not mentioning about providing detailed information of attack beh...