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Journal article
Applying Multi-CLASS Support Vector Machines: One-vs.-One vs. One-vs.-All on the UWF-ZeekDataFall22 Dataset
Electronics (Basel), Vol.13(19), 3916
10/03/2024
Web of Science ID: WOS:001331965300001
Abstract
This study investigates the technical challenges of applying Support Vector Machines (SVM) for multi-class classification in network intrusion detection using the UWF-ZeekDataFall22 dataset, which is labeled based on the MITRE ATT&CK framework. A key challenge lies in handling imbalanced classes and complex attack patterns, which are inherent in intrusion detection data. This work highlights the difficulties in implementing SVMs for multi-class classification, particularly with One-vs.-One (OvO) and One-vs.-All (OvA) methods, including scalability issues due to the large volume of network traffic logs and the tendency of SVMs to be sensitive to noisy data and class imbalances. SMOTE was used to address class imbalances, while preprocessing techniques were applied to improve feature selection and reduce noise in the data. The unique structure of network traffic data, with overlapping patterns between attack vectors, posed significant challenges in achieving accurate classification. Our model reached an accuracy of over 90% with OvO and over 80% with OvA, demonstrating that despite these challenges, multi-class SVMs can be effectively applied to complex intrusion detection tasks when combined with appropriate balancing and preprocessing techniques.
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Details
- Title
- Applying Multi-CLASS Support Vector Machines
- Publication Details
- Electronics (Basel), Vol.13(19), 3916
- Resource Type
- Journal article
- Publisher
- MDPI
- Copyright
- © 2024 by the authors.
- Identifiers
- WOS:001331965300001; 99380579440606600
- Academic Unit
- Hal Marcus College of Science and Engineering ; Computer Science; Mathematics and Statistics
- Language
- English