Intrusion Detection in IoT based Smart Networks using Fuzzy Brain Storm Optimization Technique

Abstract

The Internet of Things (IoT) is characterized as an approach where objects are outfitted with sensors, processors, and actuators which include design of hardware board and development, protocols, web APIs, and software systems, which combined to make an associated architecture of embedded systems. This connected environment enables technologies to get associated with different networks, platforms, and devices, making a web of communication which is reforming the manner in which we communicate with the world digitally. These connected embedded systems are changing behaviour and interactions with our environment, networks, and homes, and also with our own bodies in terms of smart devices. Security and privacy are the most significant consideration in the field of real-world communication and mainly on IoTs. With the evolution of IoT the network layer security in the IoT has drawn greater focus. The security vulnerabilities in the IoT system could make security risks based on any application. Therefore there is an essential requirement for IDS for the IoT based systems for avoiding security attacks based on security vulnerabilities. This paper proposed a fuzzy c-means clustering with brain storm optimization algorithm (FBSO) for IDS based on IoT system. The NSL-KDD dataset is utilized to evaluate and simulate the proposed algorithm. The results demonstrate that the proposed technique efficiently recognize intrusion attacks and decrease the network difficulties