A Review of AI-Based Intrusion Detection Systems (IDS) for Network Security
Keywords:
Artificial Intelligence (AI), Deep Learning (DL), Intrusion Detection Systems (IDS), Long Short-Term Memory (LSTM), Machine Learning (ML)Abstract
The perpetual nature of cyber threats requires the incorporation of more resistant and smart systems to identify and avert intrusions. Conventional Intrusion Detection Systems (IDS) that are based on signature detection mechanisms are limited in their ability to detect new attacks or those that are of zero-day attacks. Machine Learning (ML) and Deep Learning (DL) methods of Artificial Intelligence (AI) have found considerable popularity in improving the capabilities of the IDS. The context of this review paper is a review of the AI application in IDS addressing different AI methods, such as supervised and unsupervised learning, hybrid models, and deep learning structures. In addition, the paper analyzes popular datasets, including NSL-KDD, CIC-IDS 2017, and UNSW-NB15, to assess AI-based IDS, contrasts the performance of various AI methods, and outlines current problems and perspectives of the discipline. The paper will set out to offer a thorough insight into the significance of AI in IDS and how it can be used to overcome the growing complexity of cyber-attacks.
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