Toward Resilient Networks: AI and Deep Learning Strategies for Intrusion Detection
DOI:
https://doi.org/10.61978/digitus.v3i2.881Keywords:
Privacy-Preserving Techniques, Differential Privacy, Federated Learning, Machine Learning, Data Mining, AI Ethics, Data GovernanceAbstract
As cyber threats become more sophisticated and pervasive, the demand for advanced Network Intrusion Detection Systems (NIDS) has increased dramatically. This narrative review investigates the application of Artificial Intelligence (AI) and Deep Learning (DL) techniques in enhancing NIDS performance, aiming to address the limitations of conventional rule-based systems. The literature was systematically retrieved from reputable databases such as Scopus and IEEE Xplore using keywords including "Network Intrusion Detection," "Deep Learning," and "Cybersecurity." Inclusion criteria focused on peer-reviewed studies that utilized AI models for intrusion detection, particularly within complex domains like IoT and smart grids. The review identifies CNN, LSTM, and DNN as the dominant AI models employed in modern NIDS, achieving detection accuracies ranging from 88% to 99% across benchmark datasets such as NSL-KDD and CICIDS2017. These models also demonstrate reduced false-positive rates and enhanced detection of zero-day attacks. Despite their promise, challenges remain, including regulatory constraints, computational limitations in edge devices, and difficulties in model interpretability. Systemic organizational factors—such as leadership commitment, IT infrastructure readiness, and cybersecurity culture—further affect successful implementation. This study highlights the potential of AI-based NIDS as a strategic approach to cybersecurity enhancement and proposes solutions including Explainable AI, hybrid model designs, and federated learning. The findings support further research into cross-domain applications, model transparency, and real-time scalability to unlock the full potential of intelligent intrusion detection systems.
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