Taylor–HHO algorithm: A hybrid optimization algorithm with deep long short‐term for malicious JavaScript detection

Abstract

The security of information has become a major issue due to the development of network information-based technologies. The malicious script, like, JavaScript, is a major threat to computer networks in terms of network security. Here, the JavaScript allows the programmers not only to build advanced client-side interfaces for web-based applications but also utilized for carrying out attacks that may steal the user's confidential data. In addition, the attackers can easily induce malicious JavaScript into webpages for implanting attacks, like, phishing, spreading viruses, and Trojan horses. This paper devises a novel method, namely, Taylor–Harris Hawks Optimization driven deep long short-term memory (Taylor–HHO-based Deep LSTM) for malicious JavaScript discovery. Initially, the JavaScript is subjected as input to feature extraction in which certain features, such as time of execution, function calls, condition statement, break statement, loop statements, Boolean, number of lines, and number of O(N2) loops, are extracted. The obtained features are fed to transformation, wherein log transformation is applied for data transformation. The obtained transformed features are fused using information gain and Deep LSTM. Furthermore, the proposed Taylor–HHO-based Deep LSTM is employed for discovering malevolent JavaScript. The proposed Taylor–HHO-based Deep LSTM provided enhanced performance with the highest accuracy of 0.955, minimal FPR of 0.059, and highest TPR of 0.967.