Intrusion Detection in Mobile Adhoc Networks: A Review of Signature-Based, Anomaly-Based, and Hybrid Approaches

• Oluwasanmi S. ADANIGBO

  Department of Computer Science, Federal University of Technology, Akure, Nigeria

  Author

• Opeyemi O. ASAOLU

  Department of Computer Engineering, Federal University, Oye-Ekiti, Nigeria

  Author

• Adedayo A. SOBOWALE

  Department of Computer Engineering, Federal University, Oye-Ekiti, Nigeria

  Author

• Temidayo AKINDAHUNSI

  Obafemi Awolowo University, Ile-Ife, Nigeria

  Author

• Akinbayode A. ASAOLU

  Department of Computer Engineering, Federal University, Oye-Ekiti, Nigeria

  Author

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.

• FJET_12_79_72

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