Ensemble Pruning of Internet Traffic Classifiers for Security Applications
نویسندگان
چکیده
Internet Traffic classification is vital for various network activities such as detection of malware. Security is major issue of concern which accounts for the reputation and reliability of system. Malware effects the system adversely results in data loss or abnormal functioning. Hence, detection and as well as removal of malware is crucial. Combining set of classifiers called as ensembling proved to be an efficient approach for malware detection. But ensembling is accompanied with high costs of data transfer and high processing requirements. To repress this problem a method of ensemb1e pruning has been proposed that reduces the ensemble size and increase the predictive performance.
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