Adaptive Spider Bird Swarm Algorithm-Based Deep Recurrent Neural Network for Malicious JavaScript Detection Using Box-Cox Transformation

Abstract

JavaScript is a scripting language that is commonly used in the web pages for providing dynamic functionality in order to enhance user experience. Malicious JavaScript in webpages on internet is an important security issue due to their potentially and universality severe impact. Finding the malicious JavaScript is usually more difficult and time-consuming task in the research community. Hence, an adaptive spider bird swarm algorithm-based deep recurrent neural network (adaptive SBSA-based deep RNN) is proposed for detecting the malicious JavaScript codes in web applications. However, the proposed adaptive SBSA is designed by integrating the adaptive concept with the bird swarm algorithm (BSA) and spider monkey optimization (SMO). With the deep RNN classifier, the complexity issues exists in detecting the malicious codes is effectively resolved through the process of hierarchical computation. Due to the efficiency of the proposed approach, it can evaluate under large real-life datasets.