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Analyzing HTTPS Encrypted Traffic to Identify User Operating System, Browser and Application

Published on Mar 15, 2016in arXiv: Cryptography and Security
Jonathan Muehlstein2
Estimated H-index: 2
,
Yehonatan Zion2
Estimated H-index: 2
+ 4 AuthorsOfir Pele8
Estimated H-index: 8
Abstract
Desktops and laptops can be maliciously exploited to violate privacy. There are two main types of attack scenarios: active and passive. In this paper, we consider the passive scenario where the adversary does not interact actively with the device, but he is able to eavesdrop on the network traffic of the device from the network side. Most of the internet traffic is encrypted and thus passive attacks are challenging. In this paper, we show that an external attacker can identify the operating system, browser and application of HTTP encrypted traffic (HTTPS). To the best of our knowledge, this is the first work that shows this. We provide a large data set of more than 20000 examples for this task. Additionally, we suggest new features for this task. We run a through a set of experiments, which shows that our classification accuracy is 96.06%.
  • References (22)
  • Citations (2)
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References22
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#1Eric RescorlaH-Index: 19
This document specifies version 1.3 of the Transport Layer Security (TLS) protocol. TLS allows client/server applications to communicate over the Internet in a way that is designed to prevent eavesdropping, tampering, and message forgery. This document updates RFCs 4492, 5705, and 6066 and it obsoletes RFCs 5077, 5246, and 6961. This document also specifies new requirements for TLS 1.2 implementations.
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Support Vector Machine is one of the classical machine learning techniques that can still help solve big data classification problems. Especially, it can help the multidomain applications in a big data environment. However, the support vector machine is mathematically complex and computationally expensive. The main objective of this chapter is to simplify this approach using process diagrams and data flow diagrams to help readers understand theory and implement it successfully. To achieve this o...
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LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in deta...
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#1Riyad Alshammari (Dal: Dalhousie University)H-Index: 9
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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) pa...
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More and more security vulnerabilities are closely related to operating system (OS) information, but how to accurately identify OS versions on a real-world dynamic network in encrypted traffic is still a challenge. In this paper, we propose a comprehensive passive OS identification method based on encrypted traffic. It takes advantage of several features in TLS headers and TCP/IP headers. Moreover, we also consider flow statistic features for each session. We collect a large dataset of more than...
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#1Mao Tian (CAS: Chinese Academy of Sciences)
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The expanding volume of HTTPS traffic (both legitimate and malicious) creates even more challenges for mobile network security and management. In this work, we propose AIBMF(Application Identification Based on Multi-view Features), a fine-grained approach to classify HTTPS traffic by their application type. The key idea of AIBMF is to combine three kinds of features—payload convolution features, packet size sequence and packet content type sequence. Based on these different view features, a deep...
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