Challenging Machine Learning Algorithms in Predicting Vulnerable JavaScript Functions

arXiv:2405.07213v1 Announce Type: new
Abstract: The rapid rise of cyber-crime activities and the growing number of devices threatened by them place software security issues in the spotlight. As around 90% of all attacks exploit known types of security issues, finding vulnerable components and applying existing mitigation techniques is a viable practical approach for fighting against cyber-crime. In this paper, we investigate how the state-of-the-art machine learning techniques, including a popular deep learning algorithm, perform in predicting functions with possible security …

algorithms arxiv attacks components crime cs.cr cs.se cyber devices exploit functions javascript machine machine learning machine learning algorithms mitigation rapid security security issues software software security spotlight techniques types vulnerable