Earticle

초록

영어

With advanced internet of things (IoT) and cloud/edge computing, industrial control systems (ICSs) are evolving. However, there are critical concerns and challenges about the cybersecurity of the IoT-enabled ICSs against cyber-attacks. To reduce the risk of cyber-attacks, an intrusion detection system (IDS) is required. In general, IDS utilizes signature-based or behavior-based methods to detect potential harmful anomalies. In this paper, we propose a hybrid intrusion detection approach deploying a statistical filtering method and a composite autoencoder to effectively detect anomalous behaviors caused by cyber-attacks. The proposed method is validated by experimental data acquired from a real water treatment system as a case study of cyberattack on ICSs.

목차

Abstract
I. INTRODUCTION
II. RELATED WORKS
A. Water Treatment ICS
B. Composite Autoencoder
III. PROPOSED METHOD
IV. IMPLEMENTATION AND VALIDATION
V. CONCLUSION AND FUTURE WORK
ACKNOWLEDGMENT
REFERENCES

저자

• Hee-Yong Kwon [ Department of Electrical and Computer Engineering Inha University ]
• Taesic Kim [ Department of Electrical Engineering and Computer Science Texas A&M University-Kingsville TX, USA ]
• Mun-Kyu Lee [ Department of Electrical and Computer Engineering Inha University ]

참고문헌