Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine

Zamry, Nurfazrina and Zainal, Anazida and Rassam, Murad and Alkhammash, Eman and Ghaleb, Fuad and Saeed, Faisal (2021) Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine. Sensors, 21 (23). p. 8017. ISSN 1424-8220

sensors-21-08017-v2 (1).pdf - Published Version
Available under License Creative Commons Attribution.

Download (3MB)


Wireless Sensors Networks have been the focus of significant attention from research and development due to their applications of collecting data from various fields such as smart cities, power grids, transportation systems, medical sectors, military, and rural areas. Accurate and reliable measurements for insightful data analysis and decision-making are the ultimate goals of sensor networks for critical domains. However, the raw data collected by WSNs usually are not reliable and inaccurate due to the imperfect nature of WSNs. Identifying misbehaviours or anomalies in the network is important for providing reliable and secure functioning of the network. However, due to resource constraints, a lightweight detection scheme is a major design challenge in sensor networks. This paper aims at designing and developing a lightweight anomaly detection scheme to improve efficiency in terms of reducing the computational complexity and communication and improving memory utilization overhead while maintaining high accuracy. To achieve this aim, oneclass learning and dimension reduction concepts were used in the design. The One-Class Support Vector Machine (OCSVM) with hyper-ellipsoid variance was used for anomaly detection due to its advantage in classifying unlabelled and multivariate data. Various One-Class Support Vector Machine formulations have been investigated and Centred-Ellipsoid has been adopted in this study due to its effectiveness. Centred-Ellipsoid is the most effective kernel among studies formulations. To decrease the computational complexity and improve memory utilization, the dimensions of the data were reduced using the Candid Covariance-Free Incremental Principal Component Analysis (CCIPCA) algorithm. Extensive experiments were conducted to evaluate the proposed lightweight anomaly detection scheme. Results in terms of detection accuracy, memory utilization, computational complexity, and communication overhead show that the proposed scheme is effective and efficient compared few existing schemes evaluated. The proposed anomaly detection scheme achieved the accuracy higher than 98%, with O(nd) memory utilization and no communication overhead.

Item Type: Article
Identification Number:
25 November 2021Accepted
30 November 2021Published Online
Uncontrolled Keywords: anomaly detection; one-class support vector machine; principal component analysis; wireless sensors networks; sensor data analysis
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Faisal Saeed
Date Deposited: 05 Jan 2022 14:47
Last Modified: 05 Jan 2022 14:47

Actions (login required)

View Item View Item


In this section...