Cluster Based detection of Attack IDS using Data Mining

  Manisha Kansra,  Pankaj Dev Chadha

One of the most important challenges to intrusion detection are the problem of misjudgment, misdetection and lack of real time reaction to the attack. In the recent years, as the second line of defense after firewall, the intrusion detection technique has got fast development. a mixture of data mining techniques such as clustering, categorization and association rule detection are being used for intrusion detection. This research proposed IDS using by integrated signature based (Snort) with abnormality based (Naive Bayes) to enhance system security to detect attacks. This research used Knowledge Discovery Data Mining (KDD) CUP 20 dataset and Waikato Environment for Knowledge Analysis (WEKA) program for testing the proposed hybrid IDS.

Keywords- IDS, Attack. Traditional IDS,J48

Unique Identification Number - IJEDR1603171
Page Number(s) - 1082-1087
Pubished in - Volume 4 | Issue 3 | September 2016
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Manisha Kansra,  Pankaj Dev Chadha,   "Cluster Based detection of Attack IDS using Data Mining", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.4, Issue 3, pp.1082-1087, September 2016, Available at :http://www.ijedr.org/papers/IJEDR1603171.pdf