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CSCNN: Cost-Sensitive Convolutional Neural Network for Encrypted Traffic Classification
Neural Processing Letters ( IF 3.1 ) Pub Date : 2021-06-17 , DOI: 10.1007/s11063-021-10534-6
Shiva Soleymanpour , Hossein Sadr , Mojdeh Nazari Soleimandarabi

By the rapid development of the Internet and online applications, traffic classification not only has changed to an interesting topic in the field of computer networks but also plays a key role in cyber-security and network management. Although numerous studies have been conducted in recent years, encrypted traffic classification still remains a major challenge and unbalanced data is known as one of the most important problems in this field. Even though previous researches have focused on dealing with the class imbalance problem in the pre-processing step via machine learning and specifically deep learning methods, they are still confronted with some restrictions. To this end, a new traffic classification method is presented in this paper that aims to deal with the problem of unbalanced data along the training process. The proposed method utilized a Cost-Sensitive Convolution Neural Network (CSCNN) where a cost matrix was employed to assign a cost to each misclassification based on the distribution of each class. These costs were then utilized during the training process to increase the final classification accuracy. Various experiments were carried out to explore the performance of the proposed method for the tasks of traffic classification, traffic description, and application identification.‌ According to the obtained results, CSCNN achieved higher efficiency compared to both machine learning and deep learning based methods on the ISCX VPN-nonVPN dataset.



中文翻译:

CSCNN:用于加密流量分类的成本敏感卷积神经网络

随着互联网和在线应用的快速发展,流量分类不仅已成为计算机网络领域的一个有趣话题,而且在网络安全和网络管理中也发挥着关键作用。尽管近年来进行了大量研究,但加密流量分类仍然是一个重大挑战,不平衡数据被认为是该领域最重要的问题之一。尽管之前的研究侧重于通过机器学习特别是深度学习方法在预处理步骤中处理类不平衡问题,但它们仍然面临一些限制。为此,本文提出了一种新的流量分类方法,旨在解决训练过程中数据不平衡的问题。所提出的方法利用成本敏感卷积神经网络 (CSCNN),其中使用成本矩阵根据每个类别的分布为每个错误分类分配成本。然后在训练过程中利用这些成本来提高最终分类的准确性。进行了各种实验以探索所提出的方法在流量分类、流量描述和应用识别任务中的性能。根据获得的结果,与基于机器学习和深度学习的方法相比,CSCNN 实现了更高的效率。 ISCX VPN-非VPN 数据集。然后在训练过程中利用这些成本来提高最终分类的准确性。进行了各种实验以探索所提出的方法在流量分类、流量描述和应用识别任务中的性能。根据获得的结果,与基于机器学习和深度学习的方法相比,CSCNN 实现了更高的效率。 ISCX VPN-非VPN 数据集。然后在训练过程中利用这些成本来提高最终分类的准确性。进行了各种实验以探索所提出的方法在流量分类、流量描述和应用识别任务中的性能。根据获得的结果,与基于机器学习和深度学习的方法相比,CSCNN 实现了更高的效率。 ISCX VPN-非VPN 数据集。

更新日期:2021-06-18
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