Designing an Optimized Algorithm for Cyberattack Detection in IoT Systems

Noor Jihad Kadhim

Noor Jihad Kadhim College of Administration and Economics, Al-Qadissiya University

Keywords: IoT Security, Spoofing Attack Detection, Random Forest, Cybersecurity, Intrusion Detection System

Abstract

The Internet of Things (IoT) connects billions of devices, which makes them helpful in many areas. But the fast growth of linked nodes is a big security risk, especially spoofing attacks, which modify the IDs of Pong Humans devices and make the network less trustworthy. This research provides a novel and better way to uncover spoofing attacks in IoT environments using the Random Forest (RF) model. We use fake datasets that act like IoT traffic and threats to train and test the system. We assess the suggested model using performance metrics including accuracy, precision, recall, and F1-score. We compare our work to various classifiers, like K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). The results show that the optimized RF model is better than the others because it has a 96.8% accuracy rate and can find more spoofing attempts. The study introduces a lightweight and scalable method that performs well in IoT settings with less resources.

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