Mobile Ad Hoc Networks (MANETs) are dynamic and self-configuring wireless networks, making them vulnerable to various security threats, including intrusion attempts. Intrusion detection systems (IDS) play a critical role in safeguarding MANETs against unauthorized access and malicious activities. In this study, we propose an innovative approach to enhance the security of MANETs through intrusion detection, leveraging the power of Support Vector Machines (SVM) with Ant Colony Optimization (ACO). Our approach harnesses the robustness of SVM in pattern recognition and classification, while ACO optimizes the SVM parameters, improving the accuracy and efficiency of intrusion detection. Through extensive experiments and evaluations, we demonstrate the effectiveness of this combined approach in mitigating intrusion threats in MANETs.

Mobile Ad Hoc Networks (MANETs), Intrusion Detection, Support Vector Machine (SVM)

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