ANDE: Detect the Anonymity Web Traffic With Comprehensive Model

IF 4.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yunlong Deng;Tao Peng;Bangchao Wang;Gan Wu
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引用次数: 0

Abstract

The escalating growth of network technology and users poses critical challenges to network security. This paper introduces ANDE, a novel framework designed to enhance the classification accuracy of anonymity networks. ANDE incorporates both raw data features and statistical features extracted from network traffic. Raw data features are transformed into images, enabling recognition and classification using robust image domain models. ANDE combines an enhanced Squeeze-and-Excitation (SE) ResNet with Multilayer Perceptrons (MLP), facilitating concurrent learning and classification of both feature types. Extensive experiments on two publicly available datasets demonstrate the superior performance of ANDE compared to traditional machine learning and deep learning methods. The comprehensive evaluation underscores ANDE’s effectiveness in accurately classifying network traffic within anonymity networks. Additionally, this study empirically validates the efficacy of the SE block in augmenting the classification capabilities of the proposed framework, establishing ANDE as a promising solution for network traffic classification in the realm of network security.
ANDE: 利用综合模型检测匿名网络流量
网络技术和用户的不断增长对网络安全提出了严峻的挑战。本文介绍了一种用于提高匿名网络分类精度的新框架ANDE。ANDE结合了从网络流量中提取的原始数据特征和统计特征。将原始数据特征转换为图像,使用鲁棒图像域模型实现识别和分类。ANDE结合了增强的压缩激励(SE) ResNet和多层感知器(MLP),促进了两种特征类型的并发学习和分类。在两个公开可用的数据集上进行的大量实验表明,与传统的机器学习和深度学习方法相比,ANDE具有优越的性能。综合评估强调了ANDE在匿名网络中准确分类网络流量的有效性。此外,本研究通过实证验证了SE块在增强所提出框架的分类能力方面的有效性,确立了ANDE作为网络安全领域中网络流量分类的一个有前途的解决方案。
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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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