Adaptive and online data anomaly detection for wireless sensor systems
Wireless sensor networks (WSNs) are increasingly used as platforms for collecting data from unattended environments and monitoring important events in phenomena. However, sensor data is affected by anomalies that occur due to various reasons, such as, node software or hardware failures, reading erro...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
Elsevier B.V.
2014
|
| Subjects: |
| _version_ | 1796859651530162176 |
|---|---|
| author | Rassam, Murad Abdo Maarof, Mohd. Aizaini Zainal, Anazida |
| author_facet | Rassam, Murad Abdo Maarof, Mohd. Aizaini Zainal, Anazida |
| author_sort | Rassam, Murad Abdo |
| collection | ePrints |
| description | Wireless sensor networks (WSNs) are increasingly used as platforms for collecting data from unattended environments and monitoring important events in phenomena. However, sensor data is affected by anomalies that occur due to various reasons, such as, node software or hardware failures, reading errors, unusual events, and malicious attacks. Therefore, effective, efficient, and real time detection of anomalous measurement is required to guarantee the quality of data collected by these networks. In this paper, two efficient and effective anomaly detection models PCCAD and APCCAD are proposed for static and dynamic environments, respectively. Both models utilize the One-Class Principal Component Classifier (OCPCC) to measure the dissimilarity between sensor measurements in the feature space. The proposed APCCAD model incorporates an incremental learning method that is able to track the dynamic normal changes of data streams in the monitored environment. The efficiency and effectiveness of the proposed models are demonstrated using real life datasets collected by real sensor network projects. Experimental results show that the proposed models have advantages over existing models in terms of efficient utilization of sensor limited resources. The results further reveal that the proposed models achieve better detection effectiveness in terms of high detection accuracy with low false alarms especially for dynamic environmental data streams compared to some existing models |
| first_indexed | 2024-03-05T19:30:19Z |
| format | Article |
| id | utm.eprints-51718 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:30:19Z |
| publishDate | 2014 |
| publisher | Elsevier B.V. |
| record_format | dspace |
| spelling | utm.eprints-517182018-10-14T08:37:16Z http://eprints.utm.my/51718/ Adaptive and online data anomaly detection for wireless sensor systems Rassam, Murad Abdo Maarof, Mohd. Aizaini Zainal, Anazida QA76 Computer software Wireless sensor networks (WSNs) are increasingly used as platforms for collecting data from unattended environments and monitoring important events in phenomena. However, sensor data is affected by anomalies that occur due to various reasons, such as, node software or hardware failures, reading errors, unusual events, and malicious attacks. Therefore, effective, efficient, and real time detection of anomalous measurement is required to guarantee the quality of data collected by these networks. In this paper, two efficient and effective anomaly detection models PCCAD and APCCAD are proposed for static and dynamic environments, respectively. Both models utilize the One-Class Principal Component Classifier (OCPCC) to measure the dissimilarity between sensor measurements in the feature space. The proposed APCCAD model incorporates an incremental learning method that is able to track the dynamic normal changes of data streams in the monitored environment. The efficiency and effectiveness of the proposed models are demonstrated using real life datasets collected by real sensor network projects. Experimental results show that the proposed models have advantages over existing models in terms of efficient utilization of sensor limited resources. The results further reveal that the proposed models achieve better detection effectiveness in terms of high detection accuracy with low false alarms especially for dynamic environmental data streams compared to some existing models Elsevier B.V. 2014 Article PeerReviewed Rassam, Murad Abdo and Maarof, Mohd. Aizaini and Zainal, Anazida (2014) Adaptive and online data anomaly detection for wireless sensor systems. Knowledge-Based Systems, 60 . pp. 44-57. ISSN 0950-7051 http://dx.doi.org/10.1016/j.knosys.2014.01.003 |
| spellingShingle | QA76 Computer software Rassam, Murad Abdo Maarof, Mohd. Aizaini Zainal, Anazida Adaptive and online data anomaly detection for wireless sensor systems |
| title | Adaptive and online data anomaly detection for wireless sensor systems |
| title_full | Adaptive and online data anomaly detection for wireless sensor systems |
| title_fullStr | Adaptive and online data anomaly detection for wireless sensor systems |
| title_full_unstemmed | Adaptive and online data anomaly detection for wireless sensor systems |
| title_short | Adaptive and online data anomaly detection for wireless sensor systems |
| title_sort | adaptive and online data anomaly detection for wireless sensor systems |
| topic | QA76 Computer software |
| work_keys_str_mv | AT rassammuradabdo adaptiveandonlinedataanomalydetectionforwirelesssensorsystems AT maarofmohdaizaini adaptiveandonlinedataanomalydetectionforwirelesssensorsystems AT zainalanazida adaptiveandonlinedataanomalydetectionforwirelesssensorsystems |