NDLI: Detection and classification of malicious JavaScript via attack behavior modelling

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Detection and classification of malicious JavaScript via attack behavior modelling

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Detection and classification of malicious JavaScript via attack behavior modelling

Content Provider	ACM Digital Library
Author	Xue, Yinxing Sun, Jun Wang, Junjie Liu, Yang Xiao, Hao Chandramohan, Mahinthan
Abstract	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 behaviors. Such limitations root in the incapability of capturing attack behaviors in these approches. In this paper, we propose to use Deterministic Finite Automaton (DFA) to abstract and summarize common behaviors of malicious JS of the same attack type. We propose an automatic behavior learning framework, named JS, to learn DFAs from dynamic execution traces of JS malware, where we implement an effective online teacher by combining data dependency analysis, defense rules and trace replay mechanism. We evaluate JS using real world data of 10000 benign and 276 malicious JS samples to cover 8 most-infectious attack types. The results demonstrate the scalability and effectiveness of our approach in the malware detection and classification, compared with commercial anti-virus tools. We also show how to use our DFAs to detect variants and new attacks. .
Starting Page	48
Ending Page	59
Page Count	12
File Format	PDF
ISBN	9781450336208
DOI	10.1145/2771783.2771814
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-07-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Malicious javascript Malware detection L* Behavior modelling
Content Type	Text
Resource Type	Article

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