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Anomaly Detection In Wireless Sensor Network (WSN)

Authors: Lau Wai Fan and Mohd Hanafi Ahmad Hijazi


Wireless Sensor Networks (WSN) composed of a lot of randomly deployed sensor nodes, which used in signal processing and wireless communication. Besides of the communication capability, it also used to measure temperature, humidity and pressure in environments. However, the sensor nodes can becomes abnormal due to various reasons, such as limited computational and communication capability, hardware or software faults, and limited coverage areas. The worse condition is the sensor nodes are compromise by the anomalies. Due to the limitations of the sensor node, it is more easily to get attacks. An efficient anomaly detection technique is important as to detect the anomalies before it brings a huge damage to the network. In the works presented in this thesis, the Time Series Classification (TSC) technique is used to detect the anomalies in WSN. The TSC technique used is K-Nearest Neighbor (KNN) with Euclidean distance and Dynamic Time Warping (DTW). In order to apply the TSC technique, the data set that was collected is transform to the point form series. A window-based technique, which generates sliding windows, is employed to transform the data into point series form. The accuracy, sensitivity and specificity are the metrics that used to measure the performance of the classification. The KNN with Euclidean distance and DTW are comparing with the other classification approaches which Support Vector Machine (SVM) classifier, Naïve Bayes, Neural Networks and Decision Tree. The TSC technique with Euclidean distance of 1-nearest neighbor (1-nn), has achieved the best classification results with highest accuracy, sensitivity and specificity, which is 99.63%, 65.00% and 100% respectively. When compared with other approaches, in the aspect of sensitivity, the Naïve Bayes has achieved the highest which is 75.00%, in the aspect of accuracy and specificity, the TSC technique with Euclidean distance of 1-nn has the highest. The best classification results of TSC technique as mentioned above were generated with the length of sliding window 10. For KNN, the best result was achieved by using the K value of 1.


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