Laala, YoucefNasri, Ahlem2023-11-272023-11-272023http://dspace.univ-oeb.dz:4000/handle/123456789/17060The attention given to mobile ad hoc networks (MANET) is currently significant owing to their potential to significantly influence various real-world applications, including banking, medicine, and even the military sector. With the increasing use of MANETs in various applications, securing these networks against malicious intrusions has become a major concern. Intrusion detection systems (IDS) are among the best solutions to address intrusions and malicious behaviors. However, traditional intrusion detection systems often struggle to cope with the dynamic and decentralized nature of MANETs. In this thesis, we propose a new approach to intrusion detection in MANETs using reinforcement learning (RL). We leverage RL capabilities to enable the system to learn from its interactions with the environment and improve its detection accuracy over time. The obtained results demonstrate its effectiveness in detecting several categories of intrusions while minimizing false positives. Moreover, the system exhibits adaptability and robustness to changes in network conditions and attack strategies.enOther