An Intrusion Detection Model for Detecting Type of Attack Using Data Mining

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.