Applying Machine Learning Techniques for Early Detection of Cyber Attacks on IoT Devices

This research designs, implements, and evaluates a machine learning-based framework for the early detection of cyber attacks targeting Internet of Things (IoT) devices, with a specific focus on the context and challenges present in Iraq. The study conducts a comparative analysis of three supervised learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Networks (DNN)—using a combination of benchmark datasets (NSL-KDD, CIC-IDS-2017, Bot-IoT) and a synthesized dataset adapted to simulate the Iraqi threat landscape. Key performance metrics, including accuracy, precision, recall, and F1-score, were used for evaluation. The proposed Random Forest model demonstrated superior performance, achieving an accuracy of 99.54% in detecting a range of attacks, including DDoS and data exfiltration, outperforming baseline models by a significant margin. The results confirm that machine learning offers a viable and highly effective solution for enhancing IoT security. This study’s primary contribution lies in developing and validating a tailored cybersecurity strategy that addresses the specific needs of Iraq’s burgeoning digital infrastructure, particularly within its critical sectors such as energy and finance, thereby providing a practical pathway toward greater national cyber resilience.