TICNN—A Hybrid Light-Weight CNN for Large Imbalanced Multi-Class Datasets on Intrusion Detection System in IoT

The swift growth in ubiquitous connectivity, such as 5G and 6G, cloud computing, which enables data-driven automation and increases efficiency across industries, gave rise to the Internet of Things. This proliferation significantly expands the network attack surface, making IoT-based networks increasingly vulnerable to a multitude of attacks. To mitigate the attacks, Intrusion detection systems are developed. While developing the IDS, a primary challenge is the data collection of attack patterns and their training. The data collected is often massive, heterogeneous, and, critically, imbalanced. Training conventional models on such imbalanced data typically leads to poor performance, particularly in overlooking minority attack classes during multi-class classification. Our proposed system directly addresses this challenge of data imbalance. We introduce a hybrid technique, the Transformed Image Generative Adversarial Network (TIGAN), that generates high-fidelity attack samples and is then used to augment underrepresented attack classes, complemented by downsampling of overrepresented ones. The balanced data allows our lightweight deep learning model, Transformed Image Convolutional Neural Network (TICNN), to achieve robust attack detection. The experimental TICNN-based IDS delivers a faster detection time and detection accuracy of 99.56% at the macro level, which showcases its strong capability to identify diverse attacks, sporadic minority classes, in challenging IoT settings.