TY - GEN
T1 - Active-Passive-Combined Anomaly Detection in Electricity-Carbon Blockchain Trading System Under Limited Bandwidth
AU - He, Tong
AU - Zhang, Shilong
AU - Xu, Zisen
AU - Ruan, Gaofei
AU - Shang, Jin'ao
AU - Luo, Zian
AU - Yang, Xinyu
AU - Liu, Yang
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - In the electricity-carbon blockchain trading system, with the rapid expansion of blockchain technology applications, intrusion events targeting blockchain nodes are becoming increasingly frequent, and security issues are becoming more prominent. The security situation of blockchain systems is increasingly complex and severe. To meet the requirements for real-time and accurate anomaly detection in the electricity-carbon blockchain trading system, this paper proposes an active-passive-combined anomaly detection method. This method comprises two parts: passive anomaly detection and active anomaly detection. Passive anomaly detection collects and analyzes blockchain nodes' network traffic and triggers active anomaly detection when passive anomaly detection identifies an anomaly. In contrast, active anomaly detection collects and evaluates log data from suspicious nodes. This framework can process and analyze large-scale network alerts and host logs under limited bandwidth. Experimental results on real blockchain nodes demonstrate the effectiveness of the proposed method.
AB - In the electricity-carbon blockchain trading system, with the rapid expansion of blockchain technology applications, intrusion events targeting blockchain nodes are becoming increasingly frequent, and security issues are becoming more prominent. The security situation of blockchain systems is increasingly complex and severe. To meet the requirements for real-time and accurate anomaly detection in the electricity-carbon blockchain trading system, this paper proposes an active-passive-combined anomaly detection method. This method comprises two parts: passive anomaly detection and active anomaly detection. Passive anomaly detection collects and analyzes blockchain nodes' network traffic and triggers active anomaly detection when passive anomaly detection identifies an anomaly. In contrast, active anomaly detection collects and evaluates log data from suspicious nodes. This framework can process and analyze large-scale network alerts and host logs under limited bandwidth. Experimental results on real blockchain nodes demonstrate the effectiveness of the proposed method.
KW - Active-Passive-Combined Anomaly Detection
KW - Electricity-carbon Blockchain Trading System
KW - Limited Band-width
UR - https://www.scopus.com/pages/publications/85213318920
U2 - 10.1109/ICNSC62968.2024.10759917
DO - 10.1109/ICNSC62968.2024.10759917
M3 - 会议稿件
AN - SCOPUS:85213318920
T3 - ICNSC 2024 - 21st International Conference on Networking, Sensing and Control: Artificial Intelligence for the Next Industrial Revolution
BT - ICNSC 2024 - 21st International Conference on Networking, Sensing and Control
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st International Conference on Networking, Sensing and Control, ICNSC 2024
Y2 - 18 October 2024 through 20 October 2024
ER -