Mkuzangwe, Nenekazi NPNelwamondo, Fulufhelo V2018-04-122018-04-122017-11Mkuzangwe, N.N.P. and Nelwamondo, F.V. 2017. Ensemble of classifiers based network intrusion detection system performance bound. 4th International Conference on Systems and Informatics (ICSAI 2017), 11-13 November 2017, Hangzhou, China978-1-5386-1107-49781538611081http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8233022http://ieeexplore.ieee.org/document/8248426/http://hdl.handle.net/10204/10181Copyright: 2017 IEEE. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website.This paper provides a performance bound of a network intrusion detection system (NIDS) that uses an ensemble of classifiers. Currently researchers rely on implementing the ensemble of classifiers based NIDS before they can determine the performance of such NIDS. Therefore the knowledge of this bound would help researchers estimate the performance of their ensemble of classifiers based network intrusion detection systems (NIDSs) before they even implement them. The performance bound is defined in terms of the average information gain associated with the features used in building the ensemble and is obtained by Adaboosting a decision stump which is the weak classifier in the ensemble. Different proportions of the NSL KDD dataset that was filtered for Neptune and normal connections were used as different datasets in this study for observing the performance behaviour of the ensemble. The bound is based on the performance of this ensemble in classifying the normal and Neptune connections. Classification accuracy was used as the performance measure in this study. From the experimental results, we therefore deduce that, if the average information gain value amongst features used in the ensemble lies between 0.045651 and 0.25615 then the classification accuracy of the ensemble will be at most 0.9.enNetwork intrusion detection systemNetwork intrusion detection system performance boundAdaBoostEnsembleIntrusion detectionConference PresentationMkuzangwe, N. N., & Nelwamondo, F. V. (2017). Ensemble of classifiers based network intrusion detection system performance bound. IEEE. http://hdl.handle.net/10204/10181Mkuzangwe, Nenekazi NP, and Fulufhelo V Nelwamondo. "Ensemble of classifiers based network intrusion detection system performance bound." (2017): http://hdl.handle.net/10204/10181Mkuzangwe NN, Nelwamondo FV, Ensemble of classifiers based network intrusion detection system performance bound; IEEE; 2017. http://hdl.handle.net/10204/10181 .TY - Conference Presentation AU - Mkuzangwe, Nenekazi NP AU - Nelwamondo, Fulufhelo V AB - This paper provides a performance bound of a network intrusion detection system (NIDS) that uses an ensemble of classifiers. Currently researchers rely on implementing the ensemble of classifiers based NIDS before they can determine the performance of such NIDS. Therefore the knowledge of this bound would help researchers estimate the performance of their ensemble of classifiers based network intrusion detection systems (NIDSs) before they even implement them. The performance bound is defined in terms of the average information gain associated with the features used in building the ensemble and is obtained by Adaboosting a decision stump which is the weak classifier in the ensemble. Different proportions of the NSL KDD dataset that was filtered for Neptune and normal connections were used as different datasets in this study for observing the performance behaviour of the ensemble. The bound is based on the performance of this ensemble in classifying the normal and Neptune connections. Classification accuracy was used as the performance measure in this study. From the experimental results, we therefore deduce that, if the average information gain value amongst features used in the ensemble lies between 0.045651 and 0.25615 then the classification accuracy of the ensemble will be at most 0.9. DA - 2017-11 DB - ResearchSpace DP - CSIR KW - Network intrusion detection system KW - Network intrusion detection system performance bound KW - AdaBoost KW - Ensemble KW - Intrusion detection LK - https://researchspace.csir.co.za PY - 2017 SM - 978-1-5386-1107-4 SM - 9781538611081 T1 - Ensemble of classifiers based network intrusion detection system performance bound TI - Ensemble of classifiers based network intrusion detection system performance bound UR - http://hdl.handle.net/10204/10181 ER -