Intrusion Detection System Using Fuzzy Clustering Algorithm

  Bhujbal Harishchandra J.,  Shinde Nandkumar P.,  Walkunde Kiran B.

Nowadays Intrusion Detection System (IDS) which is increasingly a key element of system security is used to identify the malicious activities in a computer system and network. There are different approaches being employed in intrusion detection systems, but unluckily each of the technique so far is not entirely ideal. The prediction process may produce false alarms in many anomaly based intrusion detection systems. To achieve that, this paper proposes IDS model based on Fuzzy Logic. Proposed model consists of three parts Client side model which include simple bank application, IDS model in which previously defined testing set and training set are defined with fuzzy algorithm,apriori algorithm and Admin model which are define some rule for user and show system result. Also IDS model contain Artificial Neural Network algorithm which is useful for self intrusion detection system.

Keywords- Intrusion detection, self Intrusion Detection System, Fuzzy algorithm, Artificial neural network.

Unique Identification Number - IJEDR1401173
Page Number(s) - 960-962
Pubished in - Volume 2 | Issue 1 | March 2014
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Bhujbal Harishchandra J.,  Shinde Nandkumar P.,  Walkunde Kiran B.,   "Intrusion Detection System Using Fuzzy Clustering Algorithm", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.2, Issue 1, pp.960-962, March 2014, Available at :http://www.ijedr.org/papers/IJEDR1401173.pdf