Universiti Teknologi Malaysia Institutional Repository

Distributed CESVM-DR anomaly detection for wireless sensor network

Mohd. Zamry, Nurfazrina and Zainal, Anazida and A. Rassam, Murad (2019) Distributed CESVM-DR anomaly detection for wireless sensor network. International Journal of Innovative Computing, 9 (1). pp. 23-30. ISSN 2180-4370


Official URL: https://dx.doi.org/10.11113/ijic.v9n1.218


Nowadays, the advancement of the sensor technology, has introduced the smart living community where the sensor is communicating with each other or to other entities. This has introduced the new term called internet-of-things (IoT). The data collected from sensor nodes will be analyzed at the endpoint called based station or sink for decision making. Unfortunately, accurate data is not usually accurate and reliable which will affect the decision making at the base station. There are many reasons constituted to the inaccurate and unreliable data like the malicious attack, harsh environment as well as the sensor node failure itself. In a worse case scenario, the node failure will also lead to the dysfunctional of the entire network. Therefore, in this paper, an unsupervised one-class SVM (OCSVM) is used to build the anomaly detection schemes in recourse constraint Wireless Sensor Networks (WSNs). Distributed network topology will be used to minimize the data communication in the network which can prolong the network lifetime. Meanwhile, the dimension reduction has been providing the lightweight of the anomaly detection schemes. In this paper Distributed Centered Hyperellipsoidal Support Vector Machine (DCESVM-DR) anomaly detection schemes is proposed to provide the efficiency and effectiveness of the anomaly detection schemes.

Item Type:Article
Uncontrolled Keywords:Anomaly detection, support vector machines, unsupervised anomaly detection, dimension reduction
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ID Code:85231
Deposited By: Fazli Masari
Deposited On:17 Mar 2020 08:10
Last Modified:17 Mar 2020 08:10

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