Hybrid Optimization Driven Technique for Malicious Javascript Detection Based on Deep Learning Classifier

Abstract

The growth of the web users and thecontents are increasing in a daily basis. In all these webpages the implementation of javascripts are a common factor. These scripts are used for the simplicity and achieve interaction with the user, but, also could be used to harm the end user by stealing information, redirecting to phishing pages and installing harmful softwares. This alarms an immediate look into the security concerns of the javascript. There exist many machine learningbased malicious script detection approaches, but majority of them follow a shallow discriminating models where manual definition of features are constructed with artificial rules. In this paper, a deep learning framework for detecting malicious JavaScript code is proposed combing the optimization power of Bird Swarm Algorithm. To extract high-level features from JavaScript code Stacked denoising auto-encoders are implemented and BSA is used to optimise the features and identify the malicious codes. The theoretical model [2] have an accuracy of 94% in identifying the malicious codes.