A cost-sensitive deep learning based approach for network traffic classification
Files
(Published version)
Date
2022
Authors
Telikani, A.
Gandomi, A.H.
Choo, K.K.R.
Shen, J.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
IEEE Transactions on Network and Service Management, 2022; 19(1):661-670
Statement of Responsibility
Conference Name
Abstract
Network traffic classification (NTC) plays an important role in cyber security and network performance, for example in intrusion detection and facilitating a higher quality of service. However, due to the unbalanced nature of traffic datasets, NTC can be extremely challenging and poor management can degrade classification performance. While existing NTC methods seek to re-balance data distribution through resampling strategies, such approaches are known to suffer from information loss, overfitting, and increased model complexity. To address these challenges, we propose a new cost-sensitive deep learning approach to increase the robustness of deep learning classifiers against the imbalanced class problem in NTC. First, the dataset is divided into different partitions, and a cost matrix is created for each partition by considering the data distribution. Then, the costs are applied to the cost function layer to penalize classification errors. In our approach, costs are diverse in each type of misclassification because the cost matrix is specifically generated for each partition. To determine its utility, we implement the proposed cost-sensitive learning method in two deep learning classifiers, namely: stacked autoencoder and convolution neural networks. Our experiments on the ISCX VPN-nonVPN dataset show that the proposed approach can obtain higher classification performance on low-frequency classes, in comparison to three other NTC methods
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright 2021 IEEE
Access Condition Notes: Accepted manuscript is available open access