Data Mining Approach to Analyzing Intrusion Detection of Wireless Sensor Network

Shamim H Ripon, Shamse Tasnim Cynthia


Wireless Sensor Network (WSN) is a collection of distributed wireless sensor nodes and a base station where the dispersed nodes are used to monitor and record the physical conditions of the environment and then these data are organized into the base. Its application has been reached out from critical military application such as battlefield surveillance to traffic, health, industrial areas, intruder detection, security and surveillance. Due to various features in WSN it is very prone to various types external attacks. Preventing such attacks, intrusion detection system (IDS) is very important so that attacker cannot steal or manipulate data. Data mining is a technique that can help to discover patterns in large dataset. This paper proposed a data mining technique for different types of classification algorithms to detect four types of Denial of service (DoS) attacks, namely, Grayhole, Blackhole, Flooding and TDMA. A number of data mining techniques, such as KNN, Naïve Bayes, Logistic Regression, Support Vector Machine (SVM) and ANN algorithms are applied on the dataset and analyze their performance in detecting the attacks. The analysis reveals the applicability of these algorithms for detecting and predicting such attacks and can be recommended for network specialist and analysts.


Wireless Sensor Network (WSN); Intrusion Detection System (IDS); Class Imbalance; DoS attack;


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