Intrusion Detection using Data Mining Technique
Stuti Singh1, Roshan Srivastava2

1Stuti Singh, Department of Computer Science and Engineering, Phagwara (Punjab), India.
2Roshan Srivastava, Department of Computer Science and Engineering, Phagwara (Punjab), India.
Manuscript received on 12 March 2013 | Revised Manuscript received on 21 March 2013 | Manuscript Published on 30 March 2013 | PP: 126-129 | Volume-2 Issue-4, March 2013 | Retrieval Number: D0543032413/13©BEIESP
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Abstract: In reality it is not possible to prevent security breaches completely using the existing security technologies. The intrusion detection plays an important role in network security and information system. However, many current intrusion detection systems (IDSs) are signature based systems. The signature based IDS also known as misuse detection looks for a specific signature to match, and identify an intrusion. When the signatures or patterns are provided, they can detect all known attack patterns, but there are some problems for unknown attacks. The rate of false positives is very low but these types of systems are poor at detecting new attacks, variation of known attacks or attacks that act as normal behavior. Statistical Based Intrusion detection System (SBIDS) can overcome many of the aforementioned limitations of signature based intrusion detection systems. Statistical based intrusion detection systems performs better than signature based intrusion detection system for novelty detection i.e. detection of new attack is very important for intrusion detection system. Researchers have implemented various classification algorithms for intrusion detection. This dissertation evaluates a decision tree classifier over a benchmark dataset. It will help intrusion detection system in novelty detection i.e. detection of new attacks. KDD99 dataset is used as the training data set.
Keywords: Data Mining, Decision Tree, Intrusion Detection System, KDD99 Dataset.

Scope of the Article: Data Mining