An Intrusion Detection Model for Detecting Type of Attack Using Data Mining
作者： Shyam Gupta , Amruta Surana
DOI:
关键词: Feature selection 、 Anomaly-based intrusion detection system 、 Feature (computer vision) 、 Identification (information) 、 Intrusion detection system 、 Data mining 、 Cluster analysis 、 Anomaly detection 、 Naive Bayes classifier 、 Engineering
摘要: Intrusion detection systems (IDS) are important elements in a network's defenses to help protect against increasingly sophisticated cyber attacks. This project objective presents novel anomaly technique that cans b e u s d detect previously unknown attacks on network by identifying attack features. effects -based feature identification method uniquely combines k- means clustering; Naive Bayes selection and 4.5 c i o n tree classification for finding with high degree of accuracy it used KDD99CUP dataset as input. Basically whether this there or not like IPSWEEP, NEPTUNE, SMURF. Conclusions: Give brief concluding remarks outcomes what present how find.
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