Treffer: Novel JavaScript malware detection based on fuzzy Petri nets.

Currently, JavaScript is a popular scripting language for building web pages. It allows website creators to run any program code they want when users are visiting their websites. Meanwhile, malicious JavaScript becomes one of the biggest threats in the cyber world. Researchers are now searching for a convenient and effective way to detect JavaScript malware. Consequently, this paper aims to propose a novel method of detecting the JavaScript malware by using a high-level fuzzy Petri net (HLFPN). First, the web pages are crawled to get JavaScript files. Second, those main features are extracted from JavaScript files. In total, six main features of the JavaScript, including longest word size, entropy, specific character, commenting style, function calls, and abstract syntax tree (AST) features are collected. Finally, an HLFPN model is used to determine whether the malicious code is available or not. The experimental results have fully demonstrated the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Volltext ist im Gastzugang nicht verfügbar. Login für vollen Zugriff.