Intrusion Detection System to Detect Anomalies using Convolution Neural Network in IOT

Abstract

The Internet of Things (IoT) is a network for communication that is linked together by wired or wireless networks. It has progressed magnificently in today's world, whether in smart homes, where all electronics and gadgets, such as tube lights, are linked to the internet, or in the medical, educational, or government industries. As the use of the IoT is increasing rapidly, so are the security concerns. Security is the most crucial aspect to look after as the quantity of IoT devices spreads. Several attacks, such as replay, DoS, Distributed Denial of Service, and spoofing, will result in substantial data loss. To overcome these challenges, a very popular system, Intrusion Detection System (IDS) is being proposed. In this paper, deep learning Intrusion Detection System (DL-IDS) is used to check the accuracy, precision, and recall of the Convolutional Neural Network (CNN) algorithm to be detecting the attacks.