Future-Driven Security in Mobile Ad-Hoc Networks: Advanced Intrusion Detection Mechanisms

Ad-Hoc Mobile Networks (MANET) is a type of dynamic and decentralized network where mobile devices communicate wirelessly. without depending on static infrastructure Due to its flexibility and inertial adaptability, MANETs are increasingly being used in critical applications such as military operations. emergency response system and vehicular networks. However, the open and decentralized nature of MANETs makes them highly vulnerable to a number of security threats. Including routing attacks, espionage, and denial-of-service (DoS) attacks, traditional security measures are often inadequate to meet the unique challenges posed by MANETs, ​​such as limited resources. high mobility and lack of centralized control. Intrusion detection mechanisms (IDM) are important in identifying and mitigating malicious activities on the network. However, existing IDMs suffer from limitations such as a high false positive rate. Low scalability and the inefficiency of adapting to new and evolving attack strategies. This paper presents a new approach to intrusion detection in MANETs by integrating advanced machine learning algorithms and mechanisms. Adaptation Our proposed system aims to increase detection accuracy, reduce false positives, and improve the system's ability to respond to attacks in real time. While minimizing the impact on network performance and resource usage, the proposed IDM leverages supervised machine learning models such as decision trees and support vector machines. To analyze network traffic and classify it as normal or malicious, an adaptive learning algorithm has also been developed. This enables the system to continuously update its detection strategy, ensuring a timely response to emerging threats and guaranteeing long-term performance.