Unveiling Skype encrypted tunnels using GP

Riyad Alshammari, A. N. Zincir-Heywood
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引用次数: 34

Abstract

The classification of Encrypted Traffic, namely Skype, from network traffic represents a particularly challenging problem. Solutions should ideally be both simple — therefore efficient to deploy — and accurate. Recent advances to team-based Genetic Programming provide the opportunity to decompose the original problem into a subset of classifiers with non-overlapping behaviors. Thus, in this work we have investigated the identification of Skype encrypted traffic using Symbiotic Bid-Based (SBB) paradigm of team based Genetic Programming (GP) found on flow features without using IP addresses, port numbers and payload data. Evaluation of SBB-GP against C4.5 and AdaBoost — representing current best practice — indicates that SBB-GP solutions are capable of providing simpler solutions in terms number of features used and the complexity of the solution/model without sacrificing accuracy.
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揭开Skype加密隧道使用GP
从网络流量中对加密流量(即Skype)进行分类是一个特别具有挑战性的问题。理想情况下,解决方案应该既简单——因此部署效率高——又准确。基于团队的遗传规划的最新进展提供了将原始问题分解为具有不重叠行为的分类器子集的机会。因此,在这项工作中,我们研究了Skype加密流量的识别,使用基于团队的遗传规划(GP)的共生出价(SBB)范式,发现流量特征,而不使用IP地址,端口号和有效载荷数据。SBB-GP与C4.5和AdaBoost(代表当前最佳实践)的对比表明,SBB-GP解决方案能够在不牺牲精度的情况下提供更简单的解决方案,包括所使用的特征数量和解决方案/模型的复杂性。
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