Machine Learning Approach Using Artificial Neural Networks to Detect Malicious Nodes in IoT Networks

Devices can now effortlessly and wirelessly share data with one another over the internet or other networked systems thanks to a relatively new technology called Internet of Things (IoT). Despite these advantages, IoT systems are now more vulnerable to hacker attacks, which could lead to unfavourable outcomes. This is because of the IoT ecosystem’s continual expansion. These incursions may cause potential financial and physical harm. The Internet of Things is the automatically configuring network. This network is susceptible to a variety 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 suggested 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.