Optimized detection of cyber-attacks on IoT networks via hybrid deep learning models
This work addresses security for IoT networks by improving detection of known and emerging threats, though it appears incremental as it combines existing methods.
The paper tackles the problem of detecting cyber-attacks on IoT networks by proposing a hybrid deep learning model combining Self-Organizing Maps, Deep Belief Networks, and Autoencoders, achieving up to 99.99% accuracy and MCC values over 99.50% on datasets like NSL-KDD and CICIoT2023.
The rapid expansion of Internet of Things (IoT) devices has increased the risk of cyber-attacks, making effective detection essential for securing IoT networks. This work introduces a novel approach combining Self-Organizing Maps (SOMs), Deep Belief Networks (DBNs), and Autoencoders to detect known and previously unseen attack patterns. A comprehensive evaluation using simulated and real-world traffic data is conducted, with models optimized via Particle Swarm Optimization (PSO). The system achieves an accuracy of up to 99.99% and Matthews Correlation Coefficient (MCC) values exceeding 99.50%. Experiments on NSL-KDD, UNSW-NB15, and CICIoT2023 confirm the model's strong performance across diverse attack types. These findings suggest that the proposed method enhances IoT security by identifying emerging threats and adapting to evolving attack strategies.