Earticle

초록

영어

Anomaly detection systems for Industrial Control System (ICS) cybersecurity are designed to identify irregularities in network packets or operational data. However, they cannot detect attacks like Stuxnet, which physically injects malicious control logic. While existing studies on control logic modulation address this issue, they rely on separate storage and produce false positives. To overcome these limitations, this paper proposes an anomaly detection method that embeds PLC control logic, preserving its structure. By training the model on this embedded control logic, it learns to detect anomalies effectively. Experiments using the PLC control logic from a power plant's water treatment system confirmed that the proposed method successfully detects anomalous control logic.

목차

Abstract
I. INTRODUCTION
II. BACKGROUND AND RELATED WORKS
A. Background
B. Related works
III. PLC CONTROL LOGIC EMBEDDING AND ANOMALY DETECTION
A. Overview
B. Control logic IL Code Conversion
C. Control Logic Embedding
D. Control Logic Learning and Anomaly Detection
IV. EXPERIMENT AND EVALUATION
A. Experimental Setting
B. Dataset Description
C. Experimental Setup
D. Control Logic Anomaly Detection Experiment
E. Comparative study
V. CONCLUSION AND FUTURE WORK
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Ju Hyeon Lee [ dept. Information Security Gachon University ]
• Seungho Jeon [ dept.Computer Engeneering (Smart Security) Gachon University ]
• Jung Taek Seo [ dept.Computer Engeneering (Smart Security) Gachon University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004