• Ms. Ashwini D. Motghare
• Ms. Archana A. Nikose

In modern days, security of computer network has become important in most of everyone’s lives. Intrusion detection is the way of identifying malicious, harmful and abuse of computer systems by both system insiders and external attackers. The machine learning techniques and research on neural network to improve the network security by studying the behavior of the network and also that of threats is done in the rapid force. There are several techniques for intrusion detection which exist at present to provide more security to the network, but most of these techniques are static. Many researchers used machine learning techniques for intrusion detection, but some shows less detection, some techniques takes more amount of training time. The proposed approach is to build a predictive model for intrusion detection. The proposed model will use Extreme Learning Machine (ELM) and Back propagation neural networks (BPN). The algorithm will detect Hardware and software activity. It will be evaluated by a benchmark intrusion dataset to verify its feasibility, effectiveness and to analyze the results by checking performance, execution efficiency, training time required.

External attackers, Hardware, ELM, Insider attackers, Intrusion detection, Software

"An Approach To Track Intrusion Detection In The System By Using Data Mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 7, page no.154-156, July-2020, Available :http://www.jetir.org/papers/JETIR1905M25.pdf

"An Approach To Track Intrusion Detection In The System By Using Data Mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 7, page no. pp154-156, July-2020, Available at : http://www.jetir.org/papers/JETIR1905M25.pdf