Detection of black hole in Wireless Sensor Network based on Data Mining

Wireless Sensor Network is fabricated by the deployment of many miniature devices (sensors) which are capable of sensing, communicating and computing. These networks are susceptible to security threats due to unattended deployment of sensors. There are many attacks on information in transit, out of all Black hole attack or packet dropper is a denial of service attack in which an intruder persuades other nodes that it has the best route to the destination, instead it drops or absorbs all the packets preventing them to reach the destination. In this paper an effective Mutable Black hole Unearthing Mechanism is proposed by observing the behavioural changes of the nodes using Data Mining.

[1]  M. Singh,et al.  Detection of Malicious Node in Wireless Sensor Network Based on Data Mining , 2012, 2012 International Conference on Computing Sciences.

[2]  Neeraj Bhargava,et al.  Decision Tree Analysis on J48 Algorithm for Data Mining , 2013 .

[3]  Amit Singla,et al.  Comparative Analysis & Evaluation of Euclidean Distance Function and Manhattan Distance Function Using K-means Algorithm , 2012 .

[4]  M. P. S Bhatia,et al.  Data clustering with modified K-means algorithm , 2011, 2011 International Conference on Recent Trends in Information Technology (ICRTIT).

[5]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[6]  Avita Katal,et al.  Detection and prevention mechanism for Blackhole attack in Wireless Sensor Network , 2013, 2013 International Conference on Communication and Signal Processing.