Affiliations: [a] Department of Electronic Engineering, Ming Chi University of Technology, New Taipei, Taiwan
| [b] Department of Electrical Engineering, Ming Chi University of Technology, New Taipei, Taiwan
| [c] Department of Computer Science, University of Taipei, Taipei, Taiwan
| [d] Department of Computer Science and Information Engineering, National Taipei University, Taiwan
| [e] Department of Information Management, Chaoyang University of Technology, Taiwan
Correspondence:
[*]
Corresponding author. Victor R.L. Shen, Department of Computer Science and Information Engineering, National Taipei University, New Taipei City, 237 Taiwan. E-mails: rlshen@mail.ntpu.edu.tw or victor.rlshen@msa.hinet.net.
Abstract: 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.
