Abstract :

The Internet of Things (IoT) network improves people's quality of life in many ways. Nowadays, the security of IoT devices is a big worry since this expanding network attracts a lot of hackers that want to attack the system. An intrusion detection system for IoT networks that can distinguish between attack and regular sessions is presented in this research. The study used a moth flame optimization genetic algorithm to choose characteristics for determining which sessions were typical of the class. K-Nearest Neighbor was used to identify the class session. Results from an experiment on a real-world dataset demonstrate that the suggested model, Moth Flame based IOT Network Security (MFIOTNS), significantly boosts productivity by fine-tuning a number of evalautin parameters. Key Words: KNN, Clustering, Genetic Algorithm, Intrusion Detection.