A survey of IDS classification using KDD CUP 99 dataset & WEKA
نویسندگان
چکیده
Intrusion detection systems (IDSs) are based on two fundamental approaches first the recognition of anomalous activities as it turns from usual behavior and second misuse detection by observing those "signatures" of those recognized malicious assaults and classification vulnerabilities. Anomaly (behavior-based) IDSs presume the difference of normal behavior beneath attacks and achieve abnormal recognition evaluated with predefined system or user behavior reference model. This paper is to provide a detailed survey of intrusion detection techniques. It represents a study of Intrusion Detection and data mining techniques to classify different Intrusion attacks. This survey also focuses on WEKA (Waikato Environment for Knowledge Analysis) Tool and its various algorithms of classification. Lastly In this survey we tend to explain the mostly used dataset in network security research KDDCUP 99 and its various components. Finally we conclude our survey with few real research proposals which will be open issues for searchers.
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