Semantic Tabular-to-Image Conversion and Contrastive Learning for Lightweight Intrusion Detection

Abstract

With the proliferation of IoT emphasizing the need for high-performance Intrusion Detection Systems (IDS) in edge environments, deep learning-based IDS research is actively pursued; However, existing approaches that directly utilize tabular data as input for deep learning models are limited in their ability to capture the complex inherent relationships of network traffic. To address this, we propose a 3-stage lightweight IDS framework that converts tabular data into images to leverage the CNN. (1) First, feature selection based on Shapley Additive exPlanations (SHAP) is performed to compress data by retaining only critical features. (2) The selected data is transformed into images using the LLM-categorized Vortex Feature Positioning (LVFP) technique, which reconstructs tabular data into CNN-optimized spatial patterns by assigning semantically categorized feature groups to RGB channels and rearranging them through vortex feature allocation. (3) Finally, we construct a lightweight CNN encoder and pre-train it on the converted images via contrastive learning to establish generalizable feature representations. As downstream tasks, evaluations on 6 IDS & IoT benchmark datasets demonstrate that the proposed model outperforms existing models while using a minimal number of parameters.