Incremental Hybrid Intrusion Detection Using Ensemble of Weak Classifiers
نویسندگان
چکیده
It is important to increase the detection rate for known intrusions and detect unknown intrusions. It is also important to incrementally learn new unknown intrusions. Most current intrusion detection systems employ either misuse detection or anomaly detection. In order to employ these techniques, we propose incremental hybrid intrusion detection system. This framework combines incremental misuse detection and incremental anomaly detection. Our framework can learn new class of intrusions that not exist in previous data which used for training incremental misuse detection. The framework has lower computational complexity so it is suitable for real-time or on-line learning. Experimental evaluation of KddData also presented.
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