detection of attacks and anomalies in the internet of things system using neural networks based on training with pso algorithms, fuzzy pso, comparative pso and mutative pso

Integration and diversity of iot terminals and their applicable programs make them more vulnerable to many intrusive attacks. thus, designing an intrusion detection model that ensures the security, integrity, and reliability of iot is vital. traditional intrusion detection technology has the disadvantages of low detection rates and weak scalability that cannot adapt to the complicated and changing environment of the internet of things. hence, one of the most widely used traditional methods is the use of neural networks and also the use of evolutionary optimization algorithms to train neural networks can be an efficient and interesting method. therefore, in this paper, we use the pso algorithm to train the neural network and detect attacks and abnormalities of the iot system. although the pso algorithm has many benefits, in some cases it may reduce population diversity, resulting in early convergence. therefore,in order to solve this problem, we use the modified pso algorithm with a new mutation operator, fuzzy systems and comparative equations. the proposed method was tested with cup-kdd data set. the simulation results of the proposed model of this article show better performance and 99% detection accuracy in detecting different malicious attacks, such as dos, r2l, u2r, and prob.