Abstract
Software vulnerabilities are one of the main entry points of cyberattacks. Several papers focused on the challenge of automatically identifying vulnerabilities and recent works started applying machine learning techniques. With the advent of newer deep learning technologies like transformers, there is more room for improvement in vulnerability detection tools. This survey explores the various techniques used to identify vulnerabilities in code, from recurrent neural networks to transformers, such as the Bidirectional Encoder Representations from Transformers (BERT).
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Elsheikh, M.O. (2022). Vulnerability Detection Using Deep Learning. In: Ragab Hassen, H., Batatia, H. (eds) Proceedings of the International Conference on Applied CyberSecurity (ACS) 2021. ACS 2021. Lecture Notes in Networks and Systems, vol 378. Springer, Cham. https://doi.org/10.1007/978-3-030-95918-0_6
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