A Novel Features Set for Internet Traffic Classification using Burstiness

Oudah, Hussein; Ghita, Bogdan; Bakhshi, Taimur

doi:10.5220/0007384203970404

Cited by 9 publications

(7 citation statements)

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“…From an efficiency perspective, it should be noted that the statistical approach is appropriate for traffic classification as it can deal with encrypted traffic, which nowadays has become dominant, and it can adapt to real-time traffic. In our previous studies [8,9], we proposed a set of attributes based on burstiness and idle time; six applications were used to evaluate our features with the aid of C5.0 method, the results showed high accuracy in identifying six applications over 97%.…”

Section: Traffic Classification Techniquesmentioning

confidence: 99%

“…In our earlier studies [8,9], data collection was based on controlled application usage, with users being given instructions of what to do, which applications to use, and for how long. e users were asked to browse these applications separately.…”

Section: Splitting Trafficmentioning

confidence: 99%

“…e proposed features were extracted from real data, which were collected from a group of 20 users at the University of Plymouth for two months, using tcptrace tool and evaluated with the C5.0 algorithm [7]. e data were labelled based on DNS queries and IP addresses for each examined application, which was identified from our previous studies [8,9]. e rest of the paper is organised as follows: Section 2 discusses the state-of-the-art traffic classification approaches in more detail to provide a review of the limitations of present techniques.…”

Section: Introductionmentioning

confidence: 99%

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Using Burstiness for Network Applications Classification

Oudah

Ghita

Bakhshi

et al. 2019

Journal of Computer Networks and Communications

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Network traffic classification is a vital task for service operators, network engineers, and security specialists to manage network traffic, design networks, and detect threats. Identifying the type/name of applications that generate traffic is a challenging task as encrypting traffic becomes the norm for Internet communication. Therefore, relying on conventional techniques such as deep packet inspection (DPI) or port numbers is not efficient anymore. This paper proposes a novel flow statistical-based set of features that may be used for classifying applications by leveraging machine learning algorithms to yield high accuracy in identifying the type of applications that generate the traffic. The proposed features compute different timings between packets and flows. This work utilises tcptrace to extract features based on traffic burstiness and periods of inactivity (idle time) for the analysed traffic, followed by the C5.0 algorithm for determining the applications that generated it. The evaluation tests performed on a set of real, uncontrolled traffic, indicated that the method has an accuracy of 79% in identifying the correct network application.

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Section: Traffic Classification Techniquesmentioning

confidence: 99%

Section: Splitting Trafficmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using Burstiness for Network Applications Classification

Oudah

Ghita

Bakhshi

et al. 2019

Journal of Computer Networks and Communications

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“…2) Классификация по приложениям, генерирующим интернет-трафик (Skype, Torrent, браузер и т.д.) [10,11]. Это определяет активность пользователя и позволяет строить его профиль, ограничивать деятельность конкретных приложений, решать маркетинговые задачи.…”

Section: типы классификацииunclassified

“…• C5.0 [10,21] -оптимизация алгоритма C4.0, дающая преимущество по скорости работы и используемой памяти, строящая деревья, сравнимые по эффективности, но меньшего размера.…”

Section: дерево принятия решенийunclassified

A Survey of Network Traffic Classification

Getman¹,

Иконникова²

2020

Proceedings of ISP RAS

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This survey is dedicated to the task of network traffic classification, particularly to the use of machine learning algorithms in this task. The survey begins with the description of the task, its variations and possible uses in real-world problems. It then proceeds to the description of the methods used historically to solve this task, their limitations and evolution of traffic making machine learning the main way to solve the problem. Then the most popular machine learning algorithms used in this task are described, with the examples of research papers, providing the insight into their advantages and disadvantages in relation to this field. The task of feature selection is discussed, followed by the more global problem of acquiring the suitable dataset to use in the research; some examples of such popular datasets and their descriptions are provided. The paper concludes with the outline of the current problems in this research area to be solved.

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