Detection of Obfuscated Malicious JavaScript Code

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 Intern...

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Bibliographic Details
Published in:Future internet Vol. 14; no. 8; p. 217
Main Authors: Alazab, Ammar, Khraisat, Ansam, Alazab, Moutaz, Singh, Sarabjot
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.08.2022
Subjects:
Accuracy
Algorithms
Classification
Feature selection
Internet
intrusion detection
Java
JavaScript
Machine learning
malicious JavaScript
Malware
malware detection
obfuscated malicious
Performance evaluation
Programming languages
Websites
ISSN:1999-5903, 1999-5903
Online Access:Get full text
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Summary: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.
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ISSN:1999-5903
1999-5903
DOI:10.3390/fi14080217