• N. Devi jeyakkalyani
• D. Ravindran

The emerging trend of ubiquitous and pervasive computing aims at embedding everyday devices such as wristwatches, smart phones etc. with microprocessors and imparts them with wireless communication capability. Due to the scattering of system accessibility, the enthusiasm for system security and protection against advanced ambushes is consistently growing. Intrusion Detection System (IDS) play out a central occupation in the present system security. This paper proposes an IDS dependent on highlight determination and bunching algorithm utilizing filter and wrapper techniques. Filter and wrapper methodologies are named feature grouping based on linear correlation coefficient (FGLCC) algorithm and cuttlefish algorithm (CFA), separately. Artificial Neural Network is used as the classifier in the proposed technique. For execution confirmation, the proposed system was associated on KDD Cup 99 enormous informational indexes. The outcomes established a high accurate and detection rate with a low false positive rate variance with the current techniques in the writing.

Intrusion Detection, KDD, Artificial Neural Network, CuttleFish Algorithm (CFA), Linear Correlation Coefficient

"Intrusion Detection System by Combined Feature Selection Algorithm with Artificial Neural Network Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.371-375, June 2019, Available :http://www.jetir.org/papers/JETIR1907809.pdf

"Intrusion Detection System by Combined Feature Selection Algorithm with Artificial Neural Network Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp371-375, June 2019, Available at : http://www.jetir.org/papers/JETIR1907809.pdf