A HYBRID APPROACH FOR INTRUSION DETECTION AND PREVENTION IN MOBILE AD HOC NETWORKS

Mobile Ad Hoc Networks (MANETs) are highly dynamic and decentralized, making them vulnerable to various security threats, especially intrusion attacks. This paper presents a hybrid approach combining machine learning techniques and trust-based systems for efficient intrusion detection and prevention in MANETs. The proposed system leverages feature extraction, anomaly detection, and node trust evaluation to identify malicious activities while ensuring minimal impact on network performance. Experimental results demonstrate that our approach outperforms existing methods in terms of detection accuracy, false positive/negative rates, and computational overhead. Specifically, it achieves a significant improvement in energy efficiency and network reliability under varying network conditions, including node mobility and density. The findings highlight the effectiveness of integrating machine learning with trust-based systems for securing MANETs. Future work will explore scalability improvements and the integration of hybrid detection mechanisms to enhance system robustness.