Mitigating Malicious Attack in Manet Using Machine Learning Method

Mobile Ad Hoc Networks (MANETs) are distributed, self-configuring systems of mobile devices that interact with the use of permanent infrastructure. Because of their unsecured and constantly changing behavior, MANETs are extremely sensitive to a variety of security concerns, especially malicious assaults such as spoofing, Sybil and black hole. Traditional security mechanisms often fail to provide adequate protection due to the lack of centralized control and the unpredictable topology of these networks. In recent years, Machine Learning (ML) techniques have shown promising potential in enhancing security within MANETs by enabling the discovery and extenuation of malicious activities in real-time. This paper explores the application of ML methods to mitigate malicious attacks in MANETs. We propose an approach where various ML algorithms are used to identify and classify malicious behavior based on network traffic patterns and node interactions. The model is qualified on a dataset of normal and malicious network behaviors to progress its ability to recognize attacks with high accuracy. Furthermore, we discuss the integration of anomaly detection and supervised learning techniques to adapt to the self-motivated and evolving nature of the network. Experimental results demonstrate that ML-based methods significantly improve the detection and mitigation of common MANET attacks, offering a robust security solution for these vulnerable networks.