{"id":12943,"date":"2026-10-09T14:26:11","date_gmt":"2026-10-09T06:26:11","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=12943"},"modified":"2026-10-09T21:44:48","modified_gmt":"2026-10-09T13:44:48","slug":"jase-202701-36-003","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202701-36-003","title":{"rendered":"Behavior Analysis-Based Security Auditing and Data Leakage Prevention for Cloud-Edge Collaborative Intelligent Operation and Maintenance"},"content":{"rendered":"\n
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<\/i> 2027<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 36<\/a><\/p>\n<\/div>\n\n\n\n

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Fan Jia<\/i><\/a>, Yong Wang, Chun Dong, Fuming Cheng, and Longchuan Li<\/p>\n\n\n\n

Operation and Maintenance Center, State Grid Digital Technology Holding Co., Ltd., Xiong’an New Area, Hebei Province 071708, P. R. China<\/p>\n<\/div>\n\n\n\n

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Received: July 21, 2026
Accepted: August 18, 2026
Publication Date: October 09, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Scalability and throughput under different edge-node counts<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202701_36.003<\/a> <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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In cloud-edge collaborative environments, intelligent O&M logs are multi-source, heterogeneous, highly concurrent, cross-node correlated, and temporally evolving, challenging traditional centralized auditing for detecting progressive data leakage risks like abnormal logins, unauthorized access, batch exports, and external uploads. We propose BA-CESADL, a behavior-analysis framework for cloud-edge collaborative security auditing and data leakage prevention. Its five-layer architecture (data acquisition, edge analysis, cloud correlation, leakage tracing, response control) unifies login, command, database, API, and network-traffic logs with work-order context: the edge extracts lightweight features and detects local anomalies, uploading behavior summaries to the cloud, where a cross-node correlation graph identifies multi-stage leakage chains (e.g., abnormal login \u2192 permission change \u2192 sensitive access \u2192 batch export \u2192 external transmission) and triggers risk-level responses including alarms, secondary authentication, export restrictions, and session blocking. Simulations yield precision 0.936, recall 0.914, F1 0.925, and AUC 0.962, surpassing rule-based audit, Isolation Forest, LSTM-AE, graph-based audit, cloud-only, and edge-only methods, with a complete leakage-chain detection rate of 90.8% (6.4% false positives) and per-stage rates of 95.1%, 94.6%, and 94.3% for sensitive access, abnormal login, and external transmission. As edge nodes scale from 10 to 200, throughput rises from 1,850 to 18,420 log s\/s while F1 drops only from 0.929 to 0.921, showing that BA-CESADL balances detection accuracy, chain tracing, and scalability for large-scale intelligent O&M security. These results are simulation-validated; they demonstrate the mechanism’s scalability under controlled, rule-generated conditions and are not yet evidence of full-scale industrial deployment.<\/p>\n\n\n\n

Keywords: cloud-edge collaboration, behavior analysis, intelligent operation and maintenance, security auditing, data leakage prevention, scalable information systems, attention heatmap<\/em><\/p>\n\n\n\n

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