An Intelligent Secure Fault Classification and Identification Scheme for Mining Valuable Information in IIoT
文献类型：期刊论文
| 作者 | Zhang, Ying1,2; Zhang, Wenyuan3; Jiang, Xiaoyu3; Sun, Yuzhong2; Feng, Baiming4; Xiong, Naixue5; Wo, Tianyu1,2 |
| 刊名 | IEEE SYSTEMS JOURNAL
 |
| 出版日期 | 2024-08-14 |
| 页码 | 12 |
| 关键词 | Industrial Internet of Things Vectors Pattern matching Fault diagnosis Fault detection Feature extraction Source coding Abnormal detection Industrial Internet of Things (IIoT) log mining security S-Kmeans |
| ISSN号 | 1932-8184 |
| DOI | 10.1109/JSYST.2024.3437185 |
| 英文摘要 | As a pivotal component of Industry 4.0, the Industrial Internet of Things has significantly propelled the intelligent evolution of industrial systems. However, this advancement has led to increased system complexity and scale, consequently increasing the likelihood of operational failures and potential security threats. Performing an effective analysis of log information and accurately identifying system fault categories has become a substantial challenge for system administrators. To extract valuable insights from edge device logs more efficiently and ensure system security, we propose an intelligent method for system fault detection and localization. Our approach begins with an analysis of the system's source code to extract message and fault classification templates. Subsequently, real-time preprocessing of the log stream occurs, employing techniques, such as pattern matching and statistical grouping, to construct a feature vector-matrix. The detection and identification module then discerns abnormal feature vectors, using a fast classification algorithm to categorize these anomalies and determine fault types. The proposed methodology undergoes testing on our edge cloud platform. The experimental results demonstrate that the method achieves a fault detection and localization accuracy that exceeds 98%. |
| 资助项目 | Ministry of Industry and Information Technology[2105-370171-07-02-860873] ; Taiji Group Corporation Innovation Fund[HT-WB-2023-0099] |
| WOS研究方向 | Computer Science ; Engineering ; Operations Research & Management Science ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:001292762700001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/39653]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Zhang, Wenyuan; Xiong, Naixue |
| 作者单位 | 1.Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Beijing 100191, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100080, Peoples R China 3.Tianjin Univ, Coll Intelligence Comp, Tianjin 300350, Peoples R China 4.Northwest Normal Univ, Sch Comp Sci & Engn, Lanzhou 730070, Peoples R China 5.Sul Ross State Univ, Dept Comp Sci & Math, Alpine, TX 79830 USA |
| 推荐引用方式 GB/T 7714 | Zhang, Ying,Zhang, Wenyuan,Jiang, Xiaoyu,et al. An Intelligent Secure Fault Classification and Identification Scheme for Mining Valuable Information in IIoT[J]. IEEE SYSTEMS JOURNAL,2024:12. |
| APA | Zhang, Ying.,Zhang, Wenyuan.,Jiang, Xiaoyu.,Sun, Yuzhong.,Feng, Baiming.,...&Wo, Tianyu.(2024).An Intelligent Secure Fault Classification and Identification Scheme for Mining Valuable Information in IIoT.IEEE SYSTEMS JOURNAL,12. |
| MLA | Zhang, Ying,et al."An Intelligent Secure Fault Classification and Identification Scheme for Mining Valuable Information in IIoT".IEEE SYSTEMS JOURNAL (2024):12. |
入库方式： OAI收割
来源：计算技术研究所
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