A Review on Deep-Learning Based Network Intrusion Detection Systems

Saket S. Jajoo ； Kakelli Anil Kumar

《International Journal of Electronics and Information Engineering》 13卷4期 (2021/12) Pp. 170-179

https://doi.org/10.6636/IJEIE.202112_13(4).04

摘要

Network Security is an extremely arising field that secures frameworks, organizations, and information from advanced attacks. With the evolution of the Internet and the development of different types of cyberattacks, developing advanced cybersecurity tools has become an important task to protect any potential breach of sensitive data. This paper introduces the concept of Network Intrusion Detection Systems, elucidates their different types, and compares how different techniques of Deep Learning are implemented in the Network Intrusion Detection Systems to detect any malicious breach in a network. Furthermore, the paper compares the use of Machine Learning and the use of Deep Learning methods and which approach is more effective to detect unwanted network intrusions. This paper further reviews the literature about Network Intrusion Detection Systems and the domain of Machine Learning and Deep Learning that could help make informed decisions about such network intrusions and aid in their prevention and mitigation.

關鍵字

Artificial Neural Networks ； Deep Learning ； Generative Adversarial Networks ； Network Intrusion Detection ； Recurrent Neural Networks