IEDL-IDS: An Image-Enhanced Encoder-Based Deep Learning Scheme for Intrusion Detection Systems

As networks expand and evolve, their increasing complexity introduces significant security challenges, necessitating robust Intrusion Detection Systems (IDS). Traditional IDS often struggle to detect sophisticated cyberattacks due to their reliance on raw network data and primitive feature extraction techniques. To address these limitations, we propose an Image-enhanced Encoder-based Deep Learning scheme for Intrusion Detection Systems (IEDL-IDS), which combines image-based transformation and encoder-based feature extraction to detect complex intrusion patterns in network traffic. Technically, IEDL-IDS consists of three sequential modules. The preprocessing module transforms raw network traffic into RGB images to reveal temporal and spatial patterns. Thereafter, the encoder module processes the RGB images to extract latent features. Finally, the classifier module utilizes the latent features for high-accuracy intrusion detection. Notably, IEDL-IDS is highly flexible, as its built-in classifier can be easily replaced with any neural network-based model. This feature highlights the adaptability of IEDL-IDS in balancing detection performance with resource constraints, thereby meeting the diverse needs of network security applications. Our experimental results demonstrate that IEDL-IDS outperforms the state-of-the-art IDS schemes. On the CICIoT dataset, IEDL-IDS achieves a classification accuracy of 99. 91% for binary classification and 95. 66% for multi-class classification. Similarly, it attains 99. 61% and 98. 25% accuracy on the NSL-KDD dataset, and 99. 27% and 96. 42% on the ToNIoT dataset, for binary and multi-class tasks, respectively. Notably, despite its high detection performance, IEDL-IDS maintains a competitive computational footprint, making it a practical and scalable solution for real-world intrusion detection deployments.