An Ensemble Method for Intrusion Detection with Conformity to Data Variability

MWITONDI, Kassim, SAID, R and ZARGARI, Shahrzad (2017). An Ensemble Method for Intrusion Detection with Conformity to Data Variability. In: 8th Annual International Conference on ICT : Big Data, Cloud and Security (ICT-BDCS 2017, Singapore, 21-22 August 2017. (Unpublished)

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The high volume of traffic across modern networks entails use of accurate and reliable automated tools for intrusion detection. The capacity for data mining and machine learning algorithms to learn rules from data are typically constrained by the random nature of training and test data; diversity and disparity of models and related parameters and limitations in data sharing. We propose an ensemble method for intrusion detection which conforms to variability in data. Trained on a high-dimensional 82332x27 data attributes cyber-attack data variables for classification by Decision Trees (DT). Its novelty derives from iterative training and testing several DT models on multiple high-dimensional samples aimed at separating the types of attacks. Unlike Random Forests, the number of variables, p, isn’t altered to enable identification of the importance of predictor variables. It also minimises the influence of multicollinearity and strength of individual trees. Results show that the ensemble model conforms to data variability and yields more insightful predictions on multinomial targets.

Item Type: Conference or Workshop Item (Paper)
Research Institute, Centre or Group - Does NOT include content added after October 2018: Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
Departments - Does NOT include content added after October 2018: Faculty of Science, Technology and Arts > Department of Computing
Depositing User: Kassim Mwitondi
Date Deposited: 22 Aug 2017 09:19
Last Modified: 18 Mar 2021 05:54

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