A master's thesis at the College of Computer Science and Information Technology at Kirkuk University discussed an improved machine learning model for detecting botnet attacks in smart home Internet of Things (IoT) networks. The thesis, presented by student Iman Amer Mohammed, aimed to develop a lightweight and efficient detection model for botnet attacks targeting smart home devices. This was achieved by minimizing the number of features used while maintaining the highest detection accuracy, using the Particle Swarm Optimization (PSO) algorithm in conjunction with a decision tree model. The results showed the superiority of the proposed model over other comparable models, achieving 99.93% accuracy on an N-BaIoT dataset after reducing the features to a small, specific set, with reduced memory consumption.
