Intrusion Detection in Mobile Adhoc Networks: A Review of Signature-Based, Anomaly-Based, and Hybrid Approaches - Oluwasanmi S. ADANIGBO; Opeyemi O. ASAOLU; Adedayo A. SOBOWALE; Temidayo AKINDAHUNSI; Akinbayode A. ASAOLU (FUDMA Journal of Engineering and Technology, 2025)
This systematic literature review examines recent advances in Intrusion Detection Systems (IDS) for Mobile Ad Hoc Networks (MANETs), focusing on comparative analysis of signature-based, anomaly-based, and hybrid detection approaches. Through comprehensive analysis of 52 recent journal articles published between 2024-2025, this review identifies key methodologies, performance metrics, contributions to knowledge, strengths, limitations, and research gaps in MANET security. The review reveals a significant trend toward hybrid and machine learning-enhanced approaches, with ensemble methods and deep learning models achieving detection accuracies exceeding 95%. Key findings indicate that hybrid approaches combining signature and anomaly detection offer superior performance, while challenges remain in real-time processing, scalability, and adaptive learning for dynamic network environments.
Keywords: MANET, Intrusion Detection Systems, Signature-based Detection, Anomaly-based Detection, Hybrid Detection, Machine Learning, Deep Learning.