Leveraging Feature Engineering and Machine Learning for Internet of Things (IoT) Cyberattack Detection: A Scalable Approach

Internet of Things (IoT) devices are a fast-growing market, but they also possess a great challenge in terms of cyber-security, and therefore need a strong intrusion detection system (IDS). We present a machine learning-based IDS that improves attack classification through effective Feature Engineering and optimized Model Selection. Whereas traditional methods rely on static and well-known features, our approach offers novel ones tcp. payload and tcp. options to improve your detection of injection and vulnerability attacks. Our results demonstrate that Random Forest outperforms the convolutional models while achieving the highest accuracy of 99.07% (6-class) and 98.53% (I5-class) in five models evaluated on the Edge-IIoTset dataset. Using SMOTE to handle class im-balance and incorporating network features related to behavior, the method is able to better detect minority class general attacks. Building upon that foundation, we utilized behavioral feature extraction, data balancing, and deep learning optimization techniques that top centralized IDS models use to improve accuracy and resilience across our framework.