May 14, 2024, 4:11 a.m. | Rudolf Ferenc, P\'eter Heged\H{u}s, P\'eter Gyimesi, G\'abor Antal, D\'enes B\'an, Tibor Gyim\'othy

cs.CR updates on

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 cyber devices exploit functions javascript machine machine learning machine learning algorithms mitigation rapid security security issues software software security spotlight techniques types vulnerable

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