Anomaly detection method for cyber physical power system based on bilateral data fusion
The localized faults are easier to propagate across domains and escalate into cascading failures in cyber physical power system (CPPS) with the deep integration of cyber and physical components. As a result, the risks of CPPS have increased significantly. It is a challenge to fully quantify the comp...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | International Journal of Electrical Power & Energy Systems |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525003618 |
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| author | Tianlei Zang Shijun Wang Chuangzhi Li Yunfei Liu Yujian Xiao Zian Wang Xueying Yu |
| author_facet | Tianlei Zang Shijun Wang Chuangzhi Li Yunfei Liu Yujian Xiao Zian Wang Xueying Yu |
| author_sort | Tianlei Zang |
| collection | DOAJ |
| description | The localized faults are easier to propagate across domains and escalate into cascading failures in cyber physical power system (CPPS) with the deep integration of cyber and physical components. As a result, the risks of CPPS have increased significantly. It is a challenge to fully quantify the complex characteristics of CPPS. A cyber-physical bilateral data-driven composite model is proposed in this paper to achieve efficient and accurate anomaly detection of CPPS. The novel model can depict data decomposition and feature extraction from both cyber and physical domains. First, a sample convolution and interaction network is built to effectively capture temporal dependencies and sudden anomaly features in physical-side data. The time-sensitive patterns and unique deviations are focused on ensuring accurate detection of physical-side anomalies. Second, a transformer-based detection model is established to extract dynamic network attributes and state transition patterns in cyber-side data. By accurately tracking evolving network behaviors and subtle state transitions, robust detection of anomalies in the cyber domain is ensured. Last, the extracted features from both cyber and physical domains are integrated into a unified representation to achieve cross-domain synergy to precisely map CPPS anomalies. Case studies on the IEEE 33-bus system validate the effectiveness and superior performance of proposed method in identifying diverse anomaly states and enhancing CPPS operational safety and stability. |
| format | Article |
| id | doaj-art-a0a70b0978c04a28a67b6d920d9fcf02 |
| institution | DOAJ |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-a0a70b0978c04a28a67b6d920d9fcf022025-08-20T03:21:43ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-08-0116911081310.1016/j.ijepes.2025.110813Anomaly detection method for cyber physical power system based on bilateral data fusionTianlei Zang0Shijun Wang1Chuangzhi Li2Yunfei Liu3Yujian Xiao4Zian Wang5Xueying Yu6College of Electrical Engineering, Sichuan University, Chengdu 610065, PR ChinaCollege of Electrical Engineering, Sichuan University, Chengdu 610065, PR ChinaCollege of Electrical Engineering, Sichuan University, Chengdu 610065, PR ChinaCollege of Electrical Engineering, Sichuan University, Chengdu 610065, PR ChinaCollege of Electrical Engineering, Sichuan University, Chengdu 610065, PR ChinaCollege of Electrical Engineering, Sichuan University, Chengdu 610065, PR ChinaCorresponding author.; College of Electrical Engineering, Sichuan University, Chengdu 610065, PR ChinaThe localized faults are easier to propagate across domains and escalate into cascading failures in cyber physical power system (CPPS) with the deep integration of cyber and physical components. As a result, the risks of CPPS have increased significantly. It is a challenge to fully quantify the complex characteristics of CPPS. A cyber-physical bilateral data-driven composite model is proposed in this paper to achieve efficient and accurate anomaly detection of CPPS. The novel model can depict data decomposition and feature extraction from both cyber and physical domains. First, a sample convolution and interaction network is built to effectively capture temporal dependencies and sudden anomaly features in physical-side data. The time-sensitive patterns and unique deviations are focused on ensuring accurate detection of physical-side anomalies. Second, a transformer-based detection model is established to extract dynamic network attributes and state transition patterns in cyber-side data. By accurately tracking evolving network behaviors and subtle state transitions, robust detection of anomalies in the cyber domain is ensured. Last, the extracted features from both cyber and physical domains are integrated into a unified representation to achieve cross-domain synergy to precisely map CPPS anomalies. Case studies on the IEEE 33-bus system validate the effectiveness and superior performance of proposed method in identifying diverse anomaly states and enhancing CPPS operational safety and stability.http://www.sciencedirect.com/science/article/pii/S0142061525003618Anomaly detectionBilateral dataCyber physical power system (CPPS)Composite modelData fusion |
| spellingShingle | Tianlei Zang Shijun Wang Chuangzhi Li Yunfei Liu Yujian Xiao Zian Wang Xueying Yu Anomaly detection method for cyber physical power system based on bilateral data fusion International Journal of Electrical Power & Energy Systems Anomaly detection Bilateral data Cyber physical power system (CPPS) Composite model Data fusion |
| title | Anomaly detection method for cyber physical power system based on bilateral data fusion |
| title_full | Anomaly detection method for cyber physical power system based on bilateral data fusion |
| title_fullStr | Anomaly detection method for cyber physical power system based on bilateral data fusion |
| title_full_unstemmed | Anomaly detection method for cyber physical power system based on bilateral data fusion |
| title_short | Anomaly detection method for cyber physical power system based on bilateral data fusion |
| title_sort | anomaly detection method for cyber physical power system based on bilateral data fusion |
| topic | Anomaly detection Bilateral data Cyber physical power system (CPPS) Composite model Data fusion |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525003618 |
| work_keys_str_mv | AT tianleizang anomalydetectionmethodforcyberphysicalpowersystembasedonbilateraldatafusion AT shijunwang anomalydetectionmethodforcyberphysicalpowersystembasedonbilateraldatafusion AT chuangzhili anomalydetectionmethodforcyberphysicalpowersystembasedonbilateraldatafusion AT yunfeiliu anomalydetectionmethodforcyberphysicalpowersystembasedonbilateraldatafusion AT yujianxiao anomalydetectionmethodforcyberphysicalpowersystembasedonbilateraldatafusion AT zianwang anomalydetectionmethodforcyberphysicalpowersystembasedonbilateraldatafusion AT xueyingyu anomalydetectionmethodforcyberphysicalpowersystembasedonbilateraldatafusion |