Cost-sensitive Classification Strategy for Rebalance Large Scale Dataset of Intrusion Detection
In the present data mining research,the classification of data with imbalanced class distribution has posed a significant drawback of the performance attainable by traditional precision sensitive classifier learning algorithms.A Large Scale Dataset Stratified Pretreatment algorithm called LSDP is proposed for stratifying and synthesizing minority class datum while wiped out part of majority class datum.In view of the intrusion detection imbalanced datasets,this paper introduced the cost-sensitive mechanism into the intrusion detection system by using weight matrix to minimize the misclassification costs.At last combined the date preprocessing algorithm LSDP with the boosting algorithm MetaCost fully reflects the advantages of boosting the classification precision with weak leaner and balancing minority class datum.Comparing with the other methods,it has a certain features which can be used expendability,flexibility and independently.The main objective of the experiment is to analyze the performance of data level proposal against algorithm level proposal focusing in cost-sensitive models and versus a hybrid procedure that combines those two approaches.The experiment shows that the classifiers could not only detect various known attacks but also discover the boundaries between known and unknown attacks and the detection ability is amplified.Moreover,focusing on its specific data intrinsic characteristics,it can improve classification performance of intrusion detection system in terms of the imbalanced datasets immensely.