An Intrusion Detection Model for Detecting Type of Attack Using Data Mining
Abstract: Intrusion detection systems (IDS) are important elements in a networks 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; NaveBayes 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.
Keywords: Clustering, Classification, Decision trees, Feature, selection, Intrusion detection
How to Cite?: Amruta Surana, Shyam Gupta, "An Intrusion Detection Model for Detecting Type of Attack Using Data Mining", Volume 3 Issue 5, May 2014, International Journal of Science and Research (IJSR), Pages: 1496-1500, https://www.ijsr.net/getabstract.php?paperid=20132180, DOI: https://dx.doi.org/10.21275/20132180