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In the ever-evolving landscape of cybersecurity, the efficacy of IDS (Intrusion Detection System) is paramount. This paper explores the incorporation of advanced IDS techniques namely, reinforcement learning, predictive analysis, genetic algorithms, and artificial neural networks, to enhance the capabilities of IDS. The literature review encompasses a comprehensive examination of existing IDS, shedding light on their limitations and the need for innovative approaches. We delve into studies that employ reinforcement learning, predictive analysis, genetic algorithms, and artificial neural networks, individually, to bolster intrusion detection. The paper then synthesizes these approaches, exploring how their combined application offers a synergistic solution to the challenges posed by modern cyber threats. Methodologies employed in relevant studies are discussed, and the results are taken into account to reveal insights into the strengths and weaknesses of the integrated techniques. Additionally, we highlight challenges in implementation. This paper gives a comprehensive review of the current state of IDS and the idea for the most robust and reliable Intrusion Detection System.
Durole et al. (Sat,) studied this question.