Prevention and Detection of Intrusion Using Machine Learning in Mobile Ad Hoc Networks

Mobile ad hoc networks are advantageous in unusual situations because security is now a significant concern. There are several methods to use key management, trust and reputation management, intrusion detection systems (IDSs), and ad hoc network security. Implementing an intrusion detection system is one of our top priorities. There are several approaches that may be used to build and boost MANET security. The self-organizing nature of an ad hoc network and wireless connectivity make it more susceptible to intrusions and assaults than a standard system. In a black hole attack, malicious nodes reveal that they are a component of the route to the target; this is a significant routing disruption attack. We have simulated a black hole attack in an ad hoc network environment and collected information on important components in order to categorize attack behaviors. Since then, a broad range of machine learning methods have been used to classify beneficial and harmful packet data. It provides a novel method for applying machine learning methods in ad hoc networks for certain characteristics, crucial data collecting, and intrusion detection. We have compared the outcomes of several machine learning methods. Experiments demonstrate these actions in a simulated black hole assault, and different machine learning approaches accurately identify them. With a 97.4% detection rate and a 3.6% false alert rate, MLP has consistently outperformed other classifiers in terms of producing better outcomes. Our findings suggest that this strategy may be extended to include various incursions and used with a variety of classifiers.