
Data Mining Techniques for Providing Network Security through Intrusion Detection Systems: a Survey
Author(s) -
Prabhu Kavin B,
Sannasi Ganapathy
Publication year - 2018
Publication title -
international journal of advances in applied sciences
Language(s) - English
Resource type - Journals
eISSN - 2722-2594
pISSN - 2252-8814
DOI - 10.11591/ijaas.v7.i1.pp7-12
Subject(s) - intrusion detection system , computer science , data mining , anomaly based intrusion detection system , anomaly detection , cluster analysis , network security , misuse detection , artificial intelligence , machine learning , computer security
Intrusion Detection Systems are playing major role in network security in this internet world. Many researchers have been introduced number of intrusion detection systems in the past. Even though, no system was detected all kind of attacks and achieved better detection accuracy. Most of the intrusion detection systems are used data mining techniques such as clustering, outlier detection, classification, classification through learning techniques. Most of the researchers have been applied soft computing techniques for making effective decision over the network dataset for enhancing the detection accuracy in Intrusion Detection System. Few researchers also applied artificial intelligence techniques along with data mining algorithms for making dynamic decision. This paper discusses about the number of intrusion detection systems that are proposed for providing network security. Finally, comparative analysis made between the existing systems and suggested some new ideas for enhancing the performance of the existing systems.