UB ScholarWorks

Machine Learning Based Feature Reduction for Network Intrusion Detection

Show simple item record

dc.contributor.author Abuzneid, Abdelshakour A.
dc.contributor.author Faezipour, Miad
dc.contributor.author Abdulhammed, Razan
dc.contributor.author Abu Mallouh, Arafat
dc.contributor.author Musafer, Hassan
dc.date.accessioned 2019-05-04T18:50:08Z
dc.date.available 2019-05-04T18:50:08Z
dc.date.issued 2019-03-29
dc.identifier.uri https://scholarworks.bridgeport.edu/xmlui/handle/123456789/4134
dc.description.abstract The security of networked systems has become a critical universal issue. The rate of attacks against networked systems has increased dramatically, and the tactics used by the attackers are continuing to evolve. Intrusion detection is one of the solutions against these attacks. A common and effective approach for designing Intrusion Detection Systems (IDS) is Machine Learning. The performance of an IDS is significantly improved when the features are more discriminative and representative. This study uses two feature dimensionality reduction approaches: i) Auto-Encoder (AE): an instance of deep learning, for dimensionality reduction, and ii) Principle Component Analysis (PCA). The resulting low-dimensional features from both techniques are then used to build various classifiers such as Random Forest (RF), Bayesian Network, Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) for designing an IDS. The experimental findings with low-dimensional features in binary and multi-class classification show better performance in terms of Detection Rate (DR), F-Measure, False Alarm Rate (FAR), and Accuracy. This research effort is able to reduce the CICIDS2017 dataset's feature dimensions from 81 to 10, while maintaining a high accuracy of 99.6%. Furthermore, we propose a Multi-Class Combined performance metric CombinedMc with respect to class distribution to compare various multi-class and binary classification systems through incorporating FAR, DR, Accuracy, and class distribution parameters. In addition, we developed a uniform distribution based balancing approach to handle the imbalanced distribution of the minority class instances in the CICIDS2017 network intrusion dataset. en_US
dc.language.iso en_US en_US
dc.subject Machine learning en_US
dc.subject Network intrusion detection en_US
dc.title Machine Learning Based Feature Reduction for Network Intrusion Detection en_US
dc.type Other en_US
dc.institute.department School of Engineering en_US
dc.institute.name University of Bridgeport en_US
dc.event.location Bridgeport, CT en_US
dc.event.name Faculty Research Day en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search ScholarWorks

Advanced Search


My Account