Mobile Ad Hoc Networks Supporting Adaptive Threat Detection through Intrusion Detection Effective Use of Machine Learning for Cyber Defense

Due to the ever-changing nature of MANETs, novel methods of adaptive threat identification are required to ensure user safety. In this study, we investigate how machine learning may be used to improve MANET intrusion detection. To this end, we use a wide variety of machine learning models, such as Multilayer Perceptron (MLP) Neural Networks, Support Vector Machines (SVM), and Random Forests, and compare how well they can spot harmful activity in network data. The findings illustrate the advantages of the MLP Neural Network, giving heightened accuracy and flexibility. In addition, we develop a reinforcement learning-inspired adaptive learning technique to improve real-time intrusion detection by allowing the model to quickly adjust to changing network circumstances. Our results pave the way for future cross-domain applications and have far-reaching consequences for MANET network security. This study contributes to a more secure cyberspace by outlining the next steps for more adaptive and resilient protection of vital infrastructure, military networks, and emergency response networks.