Enhancing Manet Cybersecurity Through a Hardware-Driven AI-Based Defense System

MANETs need heightened security measures because to their decentralised and ever-changing nature, which renders them susceptible to cyberattacks. In order to enhance MANET cybersecurity, this study presents an Intrusion Detection and Prevention System (IDPS) that is powered by hardware and uses RF and CNN models. It safeguards privacy, optimises resource use, responds in real time, and identifies network attacks and anomalies. In terms of identifying attack traffic, CNN outperforms RF in terms of accuracy and precision in tests. It appears that the suggested IDPS reduces cyber risks, with an 83% rate of detecting network attacks and a 91% rate of identifying anomalies. For real-time MANET security applications, hardware-accelerated AI processing is the way to go since it enhances detection accuracy and minimises latency. Research on federated and reinforcement learning has the potential to boost efficiency and performance in computers. By strengthening MANET security, this study makes it easier to communicate mission-critical information.