Data Mining Based Intrusion Detection in Wireless Sensor Network
Asha R N1, Venkatesan S2 

1Asha R N, Department of Computer Science & Engineering, Global Academy of Technology, Bangalore, India.
2Dr. Venkatesan, Department of Computer Science & Engineering, DSCE, Bangalore, India.

Manuscript received on 10 March 2019 | Revised Manuscript received on 18 March 2019 | Manuscript published on 30 July 2019 | PP: 2612-2616 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2920078219/19©BEIESP | DOI: 10.35940/ijrte.B2920.078219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Intrusion detection is the one of the challenging task in wireless sensor network and prevents the system and network resources from being intrude or compromised. One of the ongoing strategies for recognizing any anomalous activities presented in a network is done by intrusion detection systems (IDS) and it becomes an essential part of defense system against attacker problems. The primary goal of our work is to study and analyze intrusion detection technique meant for improving the performance of Intrusion Detection using hybrid ANN based Clustering technique. To estimate the effectiveness of the proposed strategy, KDD CUP 99 dataset is utilized for testing and assessment. Based on the analysis, it is noticed that the proposed ANN clustering performs much better than other methods with respect to accuracy which attains an average high accuracy of 93.91%when compared with other methods.
Index Terms: Classification, Data Mining, Dataset, Intrusion Detection.

Scope of the Article: Classification