FUZZY-PSO FOR CLASSIFICATION OF TRUSTED IOT DEVICES

Abstract

IT industry associated security threats increase the vulnerability of IoT devices, and increased
organizational assets pose a high risk in the past decade. The company is not aware of IoT devices
connected to its network. Network security and integrity could pose a severe threat to the
communication of the network. In this paper proposed a Fuzzy Logic Control (FLC) with PSO
(Particle Swarm Optimization) for identifying trustworthy and untrustworthy devices in the IoT
networks. Fuzzy Logic is used in this paper to classify network traffic to identify reliable home
connected devices accurately. The current research into Fuzzy Logic Control (FLC) for device
classification has made inadequate in identifying IoT device classification during its communication
in the Network. To achieve a better classification of IoT devices, FLC conducts an operation on an
iterative basis. The FLC membership function is a squash function, which normalizes vector size
instead of the usual use of scalar elements. The activation function outputs help to locate the trusted
IoT devices that are formally trained with different concepts. The FLC identifies IoT devices and
categorizes trustworthy and untrustworthy devices based on network traffic data. The simulation is
carried out by calculating labeled network data from the IoT network