A Novel Approach of Intrusion Detection System for IoT Against Modern Attacks Using Deep Learning

Network intrusion detection is important for protecting computer networks from malicious attacks. However, class imbalance in network traffic data can make it difficult to detect intrusions accurately. To address this challenge, we propose a novel deep learning model called the deep regularizer learning model (DRLM). DRLM uses sample similarity across categories to enhance its ability to learn from imbalanced data. To enhance the representation ability of the neural network, DRLM also employs a feature extraction encoder consisting, LayerNorm and Skip-connection units. DRLM uses a adaptive contrastive loss function to optimize the model during training. The model validation is done against IoT-23 dataset, a real-time traffic data from numerous smart home IoT devices. Experimental results showed that the DRLM outperformed existing methods, demonstrating its superior generalization ability and its ability to handle class imbalance without additional pre-training or fine-tuning.