A Graph Learning Approach for Malicious Javascript Detection

In the digital age, electronic devices and digital products have become indispensable. They play an important role in both personal and professional settings for millions of people worldwide. As our dependence on technology increases, so does the urgency of ensuring cybersecurity. Cyber threats, particularly those attacking individual users and organizations, have become more frequent, complex, and difficult to detect. Among these, malicious JavaScript code is one of the most popular tools used by attackers to gain unauthorized access to systems and compromise sensitive information. Detecting these threats requires advanced and creative solutions. This paper introduces a graph learning approach, followed the rules of Markov chains, aims at detecting and mitigating malicious JavaScript attacks. Our approach takes the advantage of the structural properties of JavaScript code, transforming it into graph representation, which allows for a more effective identification of abnormal behavior. To evaluate the performance of our model, we use two popular datasets: Hynek Petrak’s dataset, which includes a variety of malicious JavaScript samples, and the SRILAB dataset, which contains benign JavaScript examples. Our method is compared with other leading graph learning models, and the results demonstrate a notable increase in detection accuracy. This approach offers a powerful tool to strengthen cyber security defenses in facing of evolving digital threats.