Detecting Malicious Nodes in IoT Networks Using Machine Learning and Artificial Neural Networks

Abstract

Thanks to a relatively new technology known as the Internet of Things, devices can now easily and wirelessly share data with one another over the internet or other networked systems (IoT). Despite these benefits, IoT systems are now more vulnerable to hacker attacks, which could have neg-ative consequences. This is due to the ongoing expansion of the IoT ecosystem. These incursions have the potential to cause fi-nancial and physical harm. The IoT is a network that config-ures itself automatically. This network is susceptible to a varie-ty of attacks, all of which can be started by rogue nodes. For instance, during a denial of service attack, a malicious node bombards a targeted node with a large number of packets. For the purpose of locating these malicious nodes in a network, a threshold-based procedure utilising cutting-edge machine learning techniques is launched. By checking the path latency and alerting on it if it exceeds a set threshold value, the sug-gested method can help identify an attacker node. The NS2 programme will be used to mimic the suggested method. We evaluate the suggested methodology and demonstrate that our system performs well in terms of a number of measures, such as throughput, latency, and packet loss.