Traffic Classification Approach Based on Support Vector Machine and Statistic Signature

Hwang, Seonhwan; Cho, Keuchul; Kim, Junhyung; Baek, Youngmi; Yun, Jeongbae; Han, Kijun

doi:10.1007/978-3-642-40316-3_29

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Statistical knowledge of the first few packets of traffic flows (Truncated Flow) is used as a distinctive property to separate applications from each other, since it captures the applications’ negotiation phases .…”

Section: State Of the Artmentioning

confidence: 99%

Traffic classification for managing Applications’ networking profiles

Alizadeh

Zúquete

2016

Security Comm Networks

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Along with the growing number of applications and end‐users, online network attacks and advanced generations of malware have continuously proliferated. Many studies have addressed the issue of intrusion detection by inspecting aggregated network traffic with no knowledge of the responsible applications/services. Such systems fail to detect intrusions in applications whenever their abnormal traffic fits into the network normality profiles. We address the problem of detecting intrusions in (known) applications when their traffic exhibits anomalies. Building traffic profiles for each separate application is the main challenge of this problem. This paper surveys traffic classification methodologies, within a taxonomy framework, to find out the best possible traffic classification methodologies that could help us answer the following question: given a traffic sample, generated by a particular application, does it conforms to the expected application's traffic? The key requirements for a practical solution are discussed. Then, the referred traffic classification methodologies are assessed in terms of their capabilities, limitations and challenges for being used as a part of this solution. The approaches based on “multiple sub‐flows” have shown the potential to be used for building robust and practical per‐application profiles in near real‐time. An overview of a blend of real‐time approaches is also described. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: State Of the Artmentioning

confidence: 99%

Traffic classification for managing Applications’ networking profiles

Alizadeh

Zúquete

2016

Security Comm Networks

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“…However, it has high complexity of algorithm. Hwang et al [5] proposed support vector machine (SVM) method to recognize traffic, which may be effective for traffic classification.…”

Section: Introductionmentioning

confidence: 99%

Multiple methods for wechat identification

Tian¹,

Zhang²,

Sun³

2016

Proceedings of the 2016 6th International Conference on Advanced Design and Manufacturing Engineering (ICADME 2016)

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Abstract. Wechat is a popular social platform developed in 2011 by Tencent. In this paper wechat traffic is analyzed by a novel hybrid method, which combines statistical method, payload-based method, SVM, CRC and deep learning. Firstly, the statistical method is utilized to extract features from wechat packets header, which can classify different wechat applications and functions, e.g. texts, images and voice, and so on. Secondly, payload-based method is used to identify the traffic, which is corresponding to the above functions and application protocols. Thirdly, SVM is applied to categorize the texts based on their attributes. CRC method is used to classify the images, which effectively protects the user's privacy. Finally, deep learning is presented to extract features of wechat app in order to check the malicious software. Experimental results show that, the proposed method has high accuracy for wechat traffic. It not only identifies wechat app, but also detects the specific functions of app. It even discriminates texts, images, voice and malicious software effectively.

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Traffic Classification Approach Based on Support Vector Machine and Statistic Signature

Cited by 2 publications

References 7 publications

Traffic classification for managing Applications’ networking profiles

Traffic classification for managing Applications’ networking profiles

Multiple methods for wechat identification

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