LAUSR

Internet of Things (IoT) has evolved for building smart environments in a distributed system, where the data produced by IoT devices are transmitted through Edge computing devices to streamline the flow of traffic from IoT devices to a distributed network. In such a scenario, the attacker introduces many attacks to the edge before forwarding them to distributed servers. This necessitates intrusion detection systems for such environments to mitigate security attacks. This paper has projected a basis for characterization of intrusive behaviors in a distributed system based on the functional link neural nets response weighted-average and teaching–learning metaheuristic with elitism on weight-space. The proposed technique makes use of teaching–learning metaheuristic optimization to obtain suitable parameters for the functional link neural net. Furthermore, the processing of duplicate parameters is successfully avoided by using mutation operation. In addition to this, in this paper the proposed method is found to be more efficient in terms of computational burden.