Securing IOT via Lightweight, AI-Based Intrusion Detection

The rapid emergence of the Internet of Things (IoT) has introduced tremendous security challenges, particularly in identifying and preventing cyber-attacks. Traditional intrusion detection systems (IDS) are typically too resource intensive for IoT devices with low processing capacity. This work proposes a lightweight, artificial intelligence-based intrusion detection system (LIDS) that employs machine learning (ML) techniques to enhance IoT security with low resource consumption. Objective: The primary of this research is to design an efficient IDS tailored to IoT networks that is well-balanced in terms of security, precision, and computational complexity. Methodology: The proposed LIDS utilizes a hybrid machine learning approach based on feature selection techniques and light-weight classifiers. We tested various ML models, including decision trees, support vector machines, and deep learning-based anomaly detection. We trained and tested the system on a benchmark IoT dataset, measuring its detection accuracy, false-positive rate, and processing overhead. Results: The experimental results indicate that the proposed AI-based IDS boasts high detection efficiency (>95%) with low false-positive rates and low computational overhead, and hence the system is feasible to implement in IoT devices with resource constraints. Conclusion: This work validates the feasibility of a lightweight, AI-based intrusion detection technique for IoT networks. Feature selection optimization and computationally light ML models, our approach offers enhanced security with no loss of IoT device performance. Real-time deployment and adaptive learning for improved immunity against constantly evolving cyber-attacks will be the focus of future research.