Detection of Malicious Nodes in IoT Networks based on Throughput and ML

Abstract

Devices can now effortlessly and wirelessly share data through the internet or other networked systems thanks to the newly created technology known as the Internet of Things (IoT). Despite these advantages, IoT devices are more susceptible to hacker attacks now, which can have unfavourable effects. The IoT ecosystem's continual proliferation is to blame for this. These invasions could have negative financial and physical effects. A network that automatically configures itself is the Internet of Things. Rogue nodes can start any number of attacks on this network. For instance, a malicious node may launch a denial of service attack by sending a huge quantity of packets at a target node. A threshold-based approach utilising cutting-edge machine learning techniques is started to locate these malicious nodes in a network. The proposed technique can assist in locating an attacker's node by monitoring the path latency and raising an alert if it rises above a predetermined threshold value. The suggested approach will be imitated using the NS2 programme. The evaluation and demonstration provided by the proposed technique show that the system in question performs admirably on several fronts, including throughput. A test is run across a 500m x 500m area network with 250 packets sent per session and 1000-byte packet size