An Efficient Machine Learning and Data Mining Method for Finding Anomalies in a Cyber Security Intrusion Detection System
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2017 by IJETT Journal|
|Year of Publication : 2017|
|Authors : Marpu Gowtami, Mula Sudhakar
Marpu Gowtami, Mula Sudhakar " An Efficient Machine Learning and Data Mining Method for Finding Anomalies in a Cyber Security Intrusion Detection System ", International Journal of Engineering Trends and Technology (IJETT), V43(6),312-316 January 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Now a day’s network security is one of the most important concerns in modern era. With the rapid development of technology and most usage of internet will increase daily. So that one of the vulnerability is network security have become important issue in the network. Intrusion detection system is used to identify unauthorized users and also unusual attacks over the secured networks. Over the past years, many studies have been conducted on the intrusion detection system. However, in order to understand the current status of implementation of machine learning techniques for solving the intrusion detection problems. An Intrusion Detection System (IDS) is designed to detect system attacks and classify system activities into normal and abnormal form. Machine learning techniques have been applied to intrusion detection systems which have an important role in detecting Intrusions. In this paper we are implementing classifier algorithms for finding unauthorized users and also overcome attacks on secured networks. This paper also presents the system design of an Intrusion detection system to reduce false alarm rate and improve accuracy to detect intrusion.
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intrusion detection, classification, Anomaly, Prior Probability.