An Intrusion Detection Model for Detecting Type of Attack Using Data MiningJournal: International Journal of Science and Research (IJSR) (Vol.3, No. 5)
Publication Date: 2014-05-15
Authors : Amruta Surana; Shyam Gupta;
Page : 1496-1500
Keywords : Clustering; Classification; Decision trees; Feature; selection; Intrusion detection;
Intrusion detection systems (IDS) are important elements in a network’s defenses to help protect against increasingly sophisticated cyber attacks. This project objective presents a novel anomaly detection technique that can b e u s e d to detect previously unknown attacks on a network by identifying attack features. This effects -based feature identification method uniquely combines k-means clustering; NaïveBayes feature selection and 4.5 d e c i s i o n tree classification for finding cyber attacks with a high degree of accuracy and it used KDD99CUP dataset as input. Basically it detect whether this attacks are there or not like IPSWEEP, NEPTUNE, SMURF. Conclusions: Give brief concluding remarks on outcomes of what attacks are present and how to find.
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Last modified: 2014-07-03 16:49:33