An Efficient Intrusion Detection System by Using Behaviour Profiling and Statistical Approach Model

An Efficient Intrusion Detection System by Using Behaviour Profiling and Statistical Approach Model

Rajagopal Devarajan and Padmanabhan Rao

PG and Research Department of Computer Science and Applications, Vivekanandha College of Arts and Sciences for Women (Autonomous), India

Abstract: Unauthorized access in a personal computer or single system of a network for tracking the system access or theft the information is called attack/ hacking. An Intrusion detection System defined as an effective security technology, it detect, prevent and possibly react to computer related malicious activities. For protecting computer systems and networks from abuse used mechanism named Intrusion detection system. The aim of the study is to know the possibilities of Intrusion detection and highly efficient and effective prevent technique. Using this model identified the efficient algorithm for intrusion detection Behaviour Profiling Algorithm and to perform dynamic analysis using Statistical Approach model using log file which provides vital information about systems and the activities on them. The proposed algorithm implemented model it produced above 90%, 96% and 98% in the wired, wireless and cloud network respectively. This study concluded that, the efficient algorithm to detect the intrusion is behaviour profiling algorithm, while join with the statistical approach model, it produces efficient result. In further research, possibility to identify which programming technique used to store the activity log into the database. Next identify which algorithm is opt to implement the intrusion detection and prevention system by using big data even the network is wired, wireless or cloud network.

Keywords: IDS, IPS, behaviour profiling algorithm, statistical approach model, NIDS, HIDS.

Received September 12, 2019; accepted May 9, 2020

https://doi.org/10.34028/iajit/18/1/13

Full Text  

Last modified on Thursday, 24 December 2020 05:40
Share:
Top
We use cookies to improve our website. By continuing to use this website, you are giving consent to cookies being used. More details…