$BitCoding$ : Network Traffic Classification Through Encoded Bit Level Signatures

Hubballi, Neminath; Swarnkar, Mayank

doi:10.1109/tnet.2018.2868816

Cited by 38 publications

(5 citation statements)

References 25 publications

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“…IP, TCP header along with payload is inspected in deep packet inspection. Usually, payloads are used to generate signatures and then these are used to find match in traffic [7]. DPI method also has few limitations: (1) no relevant and transparent specifications [8] for new applications and protocols, (2) this method increase burden on processor which reduce computer speed.…”

Section: Deep Packet Inspection Methods (Dpi)mentioning

confidence: 99%

Classification and Analysis of Malicious Traffic with Multi-layer Perceptron Model

Khedkar¹,

Ramalingam²

2021

ISI

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Traffic classification is very important field of computer science as it provides network management information. Classification of traffic become complicated due to emerging technologies and applications. It is used for Quality of Service (QoS) provisioning, security and detecting intrusion in a system. In the past used of port, inspecting packet, and machine learning algorithms have been used widely, but due to the sudden changes in the traffic, their accuracy was diminished. In this paper a Multi-Layer Perceptron model with 2 hidden layers is proposed for traffic classification and target traffic classify into different categories. The experimental results indicate that proposed classifier efficiently classifies traffic and achieves 99.28% accuracy without feature engineering.

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Section: Deep Packet Inspection Methods (Dpi)mentioning

confidence: 99%

Classification and Analysis of Malicious Traffic with Multi-layer Perceptron Model

Khedkar¹,

Ramalingam²

2021

ISI

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“…The application is classified to whose PCDA has most occurrence probability exceeding the threshold probability for that application 20 . In a different study, encoded bit‐level signatures are used which is based on DPI signatures generated from bit‐level content gathered from the payload for classification 21 …”

Section: Related Workmentioning

confidence: 99%

“…It is able to achieve high accuracy and efficiency on over 500 different protocols. However, prior information on conventions must be set up in nDPI 21 . Another study done proposes RDClass in which keywords extracted from payload along with the relative distance is taken into consideration.…”

Section: Related Workmentioning

confidence: 99%

Application layer classification of Internet traffic using ensemble learning models

2020

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Summary Accurate application layer classification of Internet traffic has been a necessary requirement for various regulatory, control, and operational purposes of Internet service provider (ISP). Due to the dynamic and ever evolving nature of Internet applications generating a diverse mixture of Internet traffic, it has been necessary to apply deep packet inspection (DPI) techniques for traffic classification. DPI methods offer accuracy but degrade overall network throughput and thus cause problems in ensuring quality of service (QoS) and maintaining service‐level agreements. Moreover, Internet traffic is mostly end to end encrypted. This in turn limits the applicability of DPI techniques and renders them useless, unless the encryption tunnel is broken by the service provider which would risk violating user privacy. To address these trade‐offs between classification accuracy, performance, and user privacy, we resort to machine learning (ML)‐based algorithms. In this article, we apply three ensemble ML algorithms and report their performance metrics in the application layer classification of Internet traffic.

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“…Было замечено, что некоторые проприетарные протоколы передают информацию на уровне битов, что привело к созданию инструментов, работающих и на этом уровне. Генерируемые маски содержат значения 0, 1 и * [3] или вероятность единицы в данном бите [4]. При всех своих достоинствах метод DPI сталкивается с существенными проблемами в своей работе.…”

Section: глубокий анализ пакетовunclassified

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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$BitCoding$ : Network Traffic Classification Through Encoded Bit Level Signatures

Cited by 38 publications

References 25 publications

Classification and Analysis of Malicious Traffic with Multi-layer Perceptron Model

Classification and Analysis of Malicious Traffic with Multi-layer Perceptron Model

Application layer classification of Internet traffic using ensemble learning models

A Survey of Network Traffic Classification

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