基于平滑特征函数的深度域自适应网络加密流分类

IF 4.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Van Tong;Cuong Dao;Hai-Anh Tran;Duc Tran;Huynh Thi Thanh Binh;Thang Hoang-Nam;Truong X. Tran
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引用次数: 0

摘要

随着HTTPS和QUIC等协议的广泛采用,加密网络流分类已成为一项关键任务。基于深度学习的方法已被证明在识别流量模式方面是有效的,即使在加密数据流中也是如此。然而,这些方法在面对不属于原始训练集的新应用时面临着重大挑战。为了解决这个问题,从现有模型转移的知识经常被用来适应新的应用。随着网络流量复杂性的增加,特别是在更高的协议层,由于域差异,学习特征的可移植性降低。最近的研究探索了深度适应网络(DAN)作为一种解决方案,它扩展了深度卷积神经网络,通过减轻这些差异来更好地适应目标域。尽管有潜力,但差异度量的计算复杂性,如最大平均差异,限制了DAN的可扩展性,特别是在应用于大型数据集时。在本文中,我们提出了一种新的DAN架构,该架构结合了平滑特征函数(SCF),特别是SCF- unnorm(非标准化SCF)和SCF- pinverse(伪逆SCF)。这些功能旨在增强特定任务层的特征可转移性,有效解决领域差异和计算复杂性带来的限制。所提出的机制为新应用程序提供了一种有效处理具有有限标记数据或完全未标记数据的情况的方法。目的是通过结合源和目标分布之间的域差异以及源误差来限制目标误差。两个统计类,SCF-unNorm和SCF-pInverse,用于最小化流量分类中的域差异。实验结果表明,我们提出的机制在准确性方面优于现有基准,实现了网络系统的实时流量分类。具体来说,在考虑的场景中,我们以仅3毫秒的执行时间实现了高达99%的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Encrypted Traffic Classification Through Deep Domain Adaptation Network With Smooth Characteristic Function
Encrypted network traffic classification has become a critical task with the widespread adoption of protocols such as HTTPS and QUIC. Deep learning-based methods have proven to be effective in identifying traffic patterns, even within encrypted data streams. However, these methods face significant challenges when confronted with new applications that were not part of the original training set. To address this issue, knowledge transfer from existing models is often employed to accommodate novel applications. As the complexity of network traffic increases, particularly at higher protocol layers, the transferability of learned features diminishes due to domain discrepancies. Recent studies have explored Deep Adaptation Networks (DAN) as a solution, which extends deep convolutional neural networks to better adapt to target domains by mitigating these discrepancies. Despite its potential, the computational complexity of discrepancy metrics, such as Maximum Mean Discrepancy, limits DAN’s scalability, especially when applied to large datasets. In this paper, we propose a novel DAN architecture that incorporates Smooth Characteristic Functions (SCFs), specifically SCF-unNorm (Unnormalized SCF) and SCF-pInverse (Pseudo-inverse SCF). These functions are designed to enhance feature transferability in task-specific layers, effectively addressing the limitations posed by domain discrepancies and computational complexity. The proposed mechanism provides a means to efficiently handle situations with limited labeled data or entirely unlabeled data for new applications. The aim is to limit the target error by incorporating a domain discrepancy between the source and target distributions along with the source error. Two statistics classes, SCF-unNorm and SCF-pInverse, are used to minimize this domain discrepancy in traffic classification. The experimental results demonstrate that our proposed mechanism outperforms existing benchmarks in terms of accuracy, enabling real-time traffic classification in network systems. Specifically, we achieve up to 99% accuracy with an execution time of only three milliseconds in the considered scenarios.
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来源期刊
IEEE Transactions on Network and Service Management
IEEE Transactions on Network and Service Management Computer Science-Computer Networks and Communications
CiteScore
9.30
自引率
15.10%
发文量
325
期刊介绍: IEEE Transactions on Network and Service Management will publish (online only) peerreviewed archival quality papers that advance the state-of-the-art and practical applications of network and service management. Theoretical research contributions (presenting new concepts and techniques) and applied contributions (reporting on experiences and experiments with actual systems) will be encouraged. These transactions will focus on the key technical issues related to: Management Models, Architectures and Frameworks; Service Provisioning, Reliability and Quality Assurance; Management Functions; Enabling Technologies; Information and Communication Models; Policies; Applications and Case Studies; Emerging Technologies and Standards.
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