Sensor Network Anomaly Detection Model by cascading Inverse Weight Clustering and C5.0 Decision Tree
Date
2019-11
Authors
Journal Title
Journal ISSN
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Publisher
Pulchowk Campus
Abstract
Wireless Sensor Network is a network of integrated sensors responsible for
environmental sensing, data processing and communication with other sensors and the
base station while consuming low power. At the same time WSNs are vulnerable to
security breaches, attacks and information leakage. Anomaly detection techniques are
used to detect such activities over the network that does not conform to the normal
behavior of the network communication. Anomaly detection in wireless sensor network using Inverse Weighted Clustering and
C5.0 Decision tree, a method for classifying anomalous and normal activities have
been proposed. The IWC clustering method is first used to partition the training
instances into k clusters using Euclidean distance similarity. On each cluster,
representing a density region of normal or anomaly instances, decision trees are built
using C5.0 decision tree algorithm. The decision tree on each cluster refined the
decision boundaries by learning the subgroups within the cluster. The experiment was
carried out on three datasets (University of North Carolina Greensboro (UNCG), Intel
Berkeley Research Lab (IBRL) and Bharatpur Airport WSN). The results show that
proposed method achieved detection rate of 98.9% at false alarm-rate of 0.31% on
IBRL; detection rate of 99.57 % at false alarm-rate of 0.35% on Bharatpur Airport.
Description
Wireless Sensor Network is a network of integrated sensors responsible for
environmental sensing, data processing and communication with other sensors and the
base station while consuming low power.
Keywords
WSN, C5.0 decision tree, IWC clustering, Anomaly detection