Detection of Obfuscated Malicious JavaScript Code

Ammar Alazab, Ansam Khraisat, Moutaz Alazab and Sarabjot Singh
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Ammar Alazab: School of Information Technology and Engineering, Melbourne Institute of Technology, Melbourne, VIC 3000, Australia
Ansam Khraisat: School of Information Technology and Engineering, Melbourne Institute of Technology, Melbourne, VIC 3000, Australia
Moutaz Alazab: Faculty of Artificial Intelligence, Al-Balqa Applied University, Amman 1705, Jordan
Sarabjot Singh: School of Information Technology and Engineering, Melbourne Institute of Technology, Melbourne, VIC 3000, Australia

Future Internet, 2022, vol. 14, issue 8, 1-15

Abstract: Websites on the Internet are becoming increasingly vulnerable to malicious JavaScript code because of its strong impact and dramatic effect. Numerous recent cyberattacks use JavaScript vulnerabilities, and in some cases employ obfuscation to conceal their malice and elude detection. To secure Internet users, an adequate intrusion-detection system (IDS) for malicious JavaScript must be developed. This paper proposes an automatic IDS of obfuscated JavaScript that employs several features and machine-learning techniques that effectively distinguish malicious and benign JavaScript codes. We also present a new set of features, which can detect obfuscation in JavaScript. The features are selected based on identifying obfuscation, a popular method to bypass conventional malware detection systems. The performance of the suggested approach has been tested on JavaScript obfuscation attacks. The studies have shown that IDS based on selected features has a detection rate of 94% for malicious samples and 81% for benign samples within the dimension of the feature vector of 60.

Keywords: malware detection; intrusion detection; obfuscated malicious; machine learning; malicious JavaScript (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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