WMSNs Data Hidden Anomaly Detection Based on OH-KFJLT Bloom Filter
With the rapid development of the Industrial Internet of Things (IIoT), the volume and size of data handled by Wireless Multimedia Sensor Networks(WMSNs) have increased dramatically. Sensors suffer from damage due to continuous high-load usage and wear, leading to anomalies in sensor data collected...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10747336/ |
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| author | Chenkai Xiao Zhongsheng Li Yanming Wu |
| author_facet | Chenkai Xiao Zhongsheng Li Yanming Wu |
| author_sort | Chenkai Xiao |
| collection | DOAJ |
| description | With the rapid development of the Industrial Internet of Things (IIoT), the volume and size of data handled by Wireless Multimedia Sensor Networks(WMSNs) have increased dramatically. Sensors suffer from damage due to continuous high-load usage and wear, leading to anomalies in sensor data collected and recorded. To solve the problem, this paper proposes an anomaly detection algorithm that is based on the Bloom Filter model, combined with the Optimal Hyperplane-based Kronecker Fast Johnson-Lindenstrauss Transform (OH-KFJLT) mapping and Reciprocal Competition Strategy(RCS), called Optimal Hyperplane Kronecker Fast Johnson-Lindenstrauss Transform Bloom Filter Anomaly Detection(OFBFAD). Firstly, the data is hashed by using the OH-KFJLT mapping method based on optimal hyperplane. Then, the RCS is adopted to de-noise the data. Finally, the Bloom Filter is constructed by 0-1 coding. In the simulation experiments conducted on the NAB, SMD, and COCO benchmark datasets, the False Alarm Rate (FAR) of the OFBFAD algorithm is all lower than 5%. The experimental results show that the OFBFAD has a higher Detection Rate (DR) and lower FAR than the current mainstream anomaly detection algorithm, and can be effectively applied to the anomaly detection of WMSNs data. |
| format | Article |
| id | doaj-art-b1259c8751e7473a9a09ebf9761eeeb0 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b1259c8751e7473a9a09ebf9761eeeb02025-01-16T00:01:38ZengIEEEIEEE Access2169-35362024-01-011217451917452610.1109/ACCESS.2024.349426410747336WMSNs Data Hidden Anomaly Detection Based on OH-KFJLT Bloom FilterChenkai Xiao0https://orcid.org/0000-0002-1094-2064Zhongsheng Li1Yanming Wu2Information Engineering Department, Minxi Vocational and Technical College, Longyan, ChinaSchool of Environment, Tsinghua University, Beijing, ChinaZijin Intelligent Automation (Xiamen) Technological Company Ltd., Xiamen, ChinaWith the rapid development of the Industrial Internet of Things (IIoT), the volume and size of data handled by Wireless Multimedia Sensor Networks(WMSNs) have increased dramatically. Sensors suffer from damage due to continuous high-load usage and wear, leading to anomalies in sensor data collected and recorded. To solve the problem, this paper proposes an anomaly detection algorithm that is based on the Bloom Filter model, combined with the Optimal Hyperplane-based Kronecker Fast Johnson-Lindenstrauss Transform (OH-KFJLT) mapping and Reciprocal Competition Strategy(RCS), called Optimal Hyperplane Kronecker Fast Johnson-Lindenstrauss Transform Bloom Filter Anomaly Detection(OFBFAD). Firstly, the data is hashed by using the OH-KFJLT mapping method based on optimal hyperplane. Then, the RCS is adopted to de-noise the data. Finally, the Bloom Filter is constructed by 0-1 coding. In the simulation experiments conducted on the NAB, SMD, and COCO benchmark datasets, the False Alarm Rate (FAR) of the OFBFAD algorithm is all lower than 5%. The experimental results show that the OFBFAD has a higher Detection Rate (DR) and lower FAR than the current mainstream anomaly detection algorithm, and can be effectively applied to the anomaly detection of WMSNs data.https://ieeexplore.ieee.org/document/10747336/Industrial Internet of Things (IIoT)wireless multimedia sensor networks (WMSNs)anomaly detectionoptimal hyperplane-based Kronecker Fast Johnson-Lindenstrauss transform (OH-KFJLT)reciprocal competition strategy(RCS) |
| spellingShingle | Chenkai Xiao Zhongsheng Li Yanming Wu WMSNs Data Hidden Anomaly Detection Based on OH-KFJLT Bloom Filter IEEE Access Industrial Internet of Things (IIoT) wireless multimedia sensor networks (WMSNs) anomaly detection optimal hyperplane-based Kronecker Fast Johnson-Lindenstrauss transform (OH-KFJLT) reciprocal competition strategy(RCS) |
| title | WMSNs Data Hidden Anomaly Detection Based on OH-KFJLT Bloom Filter |
| title_full | WMSNs Data Hidden Anomaly Detection Based on OH-KFJLT Bloom Filter |
| title_fullStr | WMSNs Data Hidden Anomaly Detection Based on OH-KFJLT Bloom Filter |
| title_full_unstemmed | WMSNs Data Hidden Anomaly Detection Based on OH-KFJLT Bloom Filter |
| title_short | WMSNs Data Hidden Anomaly Detection Based on OH-KFJLT Bloom Filter |
| title_sort | wmsns data hidden anomaly detection based on oh kfjlt bloom filter |
| topic | Industrial Internet of Things (IIoT) wireless multimedia sensor networks (WMSNs) anomaly detection optimal hyperplane-based Kronecker Fast Johnson-Lindenstrauss transform (OH-KFJLT) reciprocal competition strategy(RCS) |
| url | https://ieeexplore.ieee.org/document/10747336/ |
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