Detection Model for Cyber Attacks-Application of Data Mining

Intrusion detection systems (IDS) are important elements in network defenses to help protect against increasingly sophisticated cyber attacks. This project objective presents a novel anomaly detection technique that can be used to detect previously unknown attacks on a network by identifying attack features. This effects-based feature identification method uniquely combines k-means clustering; NaiveBayes feature selection and C4.5 decision tree classification for finding cyber attacks with a high degree of accuracy and it used KDD99CUP dataset as input. Basically it detects whether the attacks are there or not, like IPSWEEP, NEPTUNE, SMURF.

Autorentext

La profesora Amruta Surana tiene un máster en ingeniería informática. Actualmente está cursando su doctorado en Ingeniería Informática en la Universidad Poornima de Jaipur. Tiene más de 9 años de experiencia docente. Actualmente está asociada con el Instituto de Ingeniería y Tecnología Nutan Maharashtra, Talegaon Dabhade.