A Review of AI and Dataset-Driven Approaches for Intrusion Detection Systems
DOI:
https://doi.org/10.65204/djes.v3i1.422Keywords:
IDS Machine learning Cyber threats Deep learning DatasetAbstract
The increasing complexity of cyber threats has underscored the constraints of conventional Intrusion Detection Systems (IDS) and the need to have more flexible and intelligent security systems. The growth of artificial intelligence (AI) has made automated threat response, real-time analysis, and learning in the field of intrusion detection a focus. The review paper related to AI-driven IDS published between 2023 and 2025 have been examined, and efforts have focused on the hybrid metaheuristic model, machine learning, and deep learning. Well-known datasets, including CICIDS2017, CICIDS2018, UNSW-NB15, and NSL-KDD, have been tested on their appropriateness in measuring the performance of a system. It is reported that hybrid deep learning metaheuristic structures have a higher detection efficiency and flexibility than fifteen modern models, but these are reported to be highly computational. The future research direction is presented, including the explanation of AI to enhance transparency, the creation of lightweight IDS that can be implemented in the conditions of the IoT, and the enhancement of adversarial attack resistance. Overall, AI-based IDS may be deemed as a serious breakthrough in intelligent, scalable, and resilient network security.
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