A comprehensive systematic review of intrusion detection systems: emerging techniques, challenges, and future research directions

The role of Intrusion Detection Systems (IDS) in the protection against the increasing variety of cybersecurity threats in complex environments, including the Internet of Things (IoT), cloud computing, and industrial networks. This study evaluates the existing state-of-the-art IDS methodologies using Deep Learning (DL) approaches, and advanced feature engineering techniques. This research also highlights the success of models such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Explainable AI (XAI) in improving detection accuracy as well as computational efficiency and interoperability. Blockchain and quantum computing technologies are explored to improve data privacy, resilience, and scalability in decentralized and resource-constrained environments. This work primarily identifies key challenges, including real-time anomaly detection, adversarial robustness, and imbalance datasets, to assist researchers in investigating further research opportunities. Focusing on future research in filling these gaps, proceeds toward developing lightweight, adaptive, and ethical IDS frameworks that can operate in real-time across dynamic and heterogeneous environments. In this paper, existing IDS approaches, research opportunities, and advanced cybersecurity strategies are critically synthesized to create a useful resource for academics, researchers, and industry practitioners.