@article{ART003317576},
author={Jun Yeong Park and Kunwoo Kang and Hoin Lee and Seungeun Lee and Yu Rang Park},
title={Semantic Tabular-to-Image Conversion and Contrastive Learning for Lightweight Intrusion Detection},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2026},
volume={31},
number={3},
pages={21-35}
TY - JOUR
AU - Jun Yeong Park
AU - Kunwoo Kang
AU - Hoin Lee
AU - Seungeun Lee
AU - Yu Rang Park
TI - Semantic Tabular-to-Image Conversion and Contrastive Learning for Lightweight Intrusion Detection
JO - Journal of The Korea Society of Computer and Information
PY - 2026
VL - 31
IS - 3
PB - The Korean Society Of Computer And Information
SP - 21
EP - 35
SN - 1598-849X
AB - 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.
KW - Intrusion Detection System;Tabular-to-Image;Lightweight Model;LLM;Contrastive Learning
DO -
UR -
ER -
Jun Yeong Park, Kunwoo Kang, Hoin Lee, Seungeun Lee and Yu Rang Park. (2026). Semantic Tabular-to-Image Conversion and Contrastive Learning for Lightweight Intrusion Detection. Journal of The Korea Society of Computer and Information, 31(3), 21-35.
Jun Yeong Park, Kunwoo Kang, Hoin Lee, Seungeun Lee and Yu Rang Park. 2026, "Semantic Tabular-to-Image Conversion and Contrastive Learning for Lightweight Intrusion Detection", Journal of The Korea Society of Computer and Information, vol.31, no.3 pp.21-35.
Jun Yeong Park, Kunwoo Kang, Hoin Lee, Seungeun Lee, Yu Rang Park "Semantic Tabular-to-Image Conversion and Contrastive Learning for Lightweight Intrusion Detection" Journal of The Korea Society of Computer and Information 31.3 pp.21-35 (2026) : 21.
Jun Yeong Park, Kunwoo Kang, Hoin Lee, Seungeun Lee, Yu Rang Park. Semantic Tabular-to-Image Conversion and Contrastive Learning for Lightweight Intrusion Detection. 2026; 31(3), 21-35.
Jun Yeong Park, Kunwoo Kang, Hoin Lee, Seungeun Lee and Yu Rang Park. "Semantic Tabular-to-Image Conversion and Contrastive Learning for Lightweight Intrusion Detection" Journal of The Korea Society of Computer and Information 31, no.3 (2026) : 21-35.
Jun Yeong Park; Kunwoo Kang; Hoin Lee; Seungeun Lee; Yu Rang Park. Semantic Tabular-to-Image Conversion and Contrastive Learning for Lightweight Intrusion Detection. Journal of The Korea Society of Computer and Information, 31(3), 21-35.
Jun Yeong Park; Kunwoo Kang; Hoin Lee; Seungeun Lee; Yu Rang Park. Semantic Tabular-to-Image Conversion and Contrastive Learning for Lightweight Intrusion Detection. Journal of The Korea Society of Computer and Information. 2026; 31(3) 21-35.
Jun Yeong Park, Kunwoo Kang, Hoin Lee, Seungeun Lee, Yu Rang Park. Semantic Tabular-to-Image Conversion and Contrastive Learning for Lightweight Intrusion Detection. 2026; 31(3), 21-35.
Jun Yeong Park, Kunwoo Kang, Hoin Lee, Seungeun Lee and Yu Rang Park. "Semantic Tabular-to-Image Conversion and Contrastive Learning for Lightweight Intrusion Detection" Journal of The Korea Society of Computer and Information 31, no.3 (2026) : 21-35.
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