• Mrs. A. Chithra
• M. Lakshimi Priya

Due to the widespread diffusion of network connectivity, the demand for network security and protection against cyber-attacks is ever increasing. Intrusion detection systems (IDS) perform an essential role in today’s network security. This paper proposes an IDS based on feature selection and clustering algorithm using filter and wrapper methods. Filter and wrapper methods are named feature grouping based on linear correlation coefficient (FGLCC) algorithm and cuttlefish algorithm (CFA), respectively. Artificial Neural Network is used as the classifier in the proposed method. For performance verification, the proposed method was applied on KDD Cup 99 large data sets. The results verified a high accuracy (95.03%) and detection rate (95.23%) with a low false positive rate (1.65%) compared to the existing methods in the literature.

Cyber Security, Intrusion Detection System, Feature Selection, Classification, CuttleFish algorithm, Linear Correlation Coefficient, Artificial Neural Network.

"Designing of Intrusion Detection System using Data Mining Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.38-43, June 2019, Available :http://www.jetir.org/papers/JETIRDB06006.pdf

"Designing of Intrusion Detection System using Data Mining Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp38-43, June 2019, Available at : http://www.jetir.org/papers/JETIRDB06006.pdf