Machine Learning for Traffic Analysis: A Review

Nour Alqudah; Qussai Yaseen

doi:10.1016/j.procs.2020.03.111

Machine Learning for Traffic Analysis: A Review

Nour Alqudah
, Qussai Yaseen

Jordan University of Science and Technology

Research output: Contribution to journal › Conference article › peer-review

57 Scopus citations

Abstract

Traffic analysis has many purposes such as evaluating the performance and security of network operations and management. Therefore, network traffic analysis is considered vital for improving networks operation and security. This paper discusses different machine learning approaches for traffic analysis. Increased network traffic and the development of artificial intelligence require new ways to detect intrusions, analyze malware behavior, categorize Internet traffic and other security aspects. Machine learning (ML) shows effective capabilities in solving network problems. A review of the techniques used in the traffic analysis is presented in this paper.

Original language	English
Pages (from-to)	911-916
Number of pages	6
Journal	Procedia Computer Science
Volume	170
DOIs	https://doi.org/10.1016/j.procs.2020.03.111
State	Published - 2020
Externally published	Yes
Event	11th International Conference on Ambient Systems, Networks and Technologies, ANT 2020 / 3rd International Conference on Emerging Data and Industry 4.0, EDI40 2020 / Affiliated Workshops - Warsaw, Poland Duration: 6 Apr 2020 → 9 Apr 2020

Keywords

machine learning
network security
traffic analysis

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10.1016/j.procs.2020.03.111

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@article{08c8ca7d12d3416389eb671f25ff1ac2,

title = "Machine Learning for Traffic Analysis: A Review",

abstract = "Traffic analysis has many purposes such as evaluating the performance and security of network operations and management. Therefore, network traffic analysis is considered vital for improving networks operation and security. This paper discusses different machine learning approaches for traffic analysis. Increased network traffic and the development of artificial intelligence require new ways to detect intrusions, analyze malware behavior, categorize Internet traffic and other security aspects. Machine learning (ML) shows effective capabilities in solving network problems. A review of the techniques used in the traffic analysis is presented in this paper.",

keywords = "machine learning, network security, traffic analysis",

author = "Nour Alqudah and Qussai Yaseen",

note = "Publisher Copyright: {\textcopyright} 2020 The Authors. Published by Elsevier B.V.All rights reserved.; 11th International Conference on Ambient Systems, Networks and Technologies, ANT 2020 / 3rd International Conference on Emerging Data and Industry 4.0, EDI40 2020 / Affiliated Workshops ; Conference date: 06-04-2020 Through 09-04-2020",

year = "2020",

doi = "10.1016/j.procs.2020.03.111",

language = "English",

volume = "170",

pages = "911--916",

journal = "Procedia Computer Science",

issn = "1877-0509",

publisher = "Elsevier B.V.",

}

TY - JOUR

T1 - Machine Learning for Traffic Analysis

T2 - 11th International Conference on Ambient Systems, Networks and Technologies, ANT 2020 / 3rd International Conference on Emerging Data and Industry 4.0, EDI40 2020 / Affiliated Workshops

AU - Alqudah, Nour

AU - Yaseen, Qussai

PY - 2020

Y1 - 2020

N2 - Traffic analysis has many purposes such as evaluating the performance and security of network operations and management. Therefore, network traffic analysis is considered vital for improving networks operation and security. This paper discusses different machine learning approaches for traffic analysis. Increased network traffic and the development of artificial intelligence require new ways to detect intrusions, analyze malware behavior, categorize Internet traffic and other security aspects. Machine learning (ML) shows effective capabilities in solving network problems. A review of the techniques used in the traffic analysis is presented in this paper.

AB - Traffic analysis has many purposes such as evaluating the performance and security of network operations and management. Therefore, network traffic analysis is considered vital for improving networks operation and security. This paper discusses different machine learning approaches for traffic analysis. Increased network traffic and the development of artificial intelligence require new ways to detect intrusions, analyze malware behavior, categorize Internet traffic and other security aspects. Machine learning (ML) shows effective capabilities in solving network problems. A review of the techniques used in the traffic analysis is presented in this paper.

KW - machine learning

KW - network security

KW - traffic analysis

UR - https://www.scopus.com/pages/publications/85085547571

U2 - 10.1016/j.procs.2020.03.111

DO - 10.1016/j.procs.2020.03.111

M3 - Conference article

AN - SCOPUS:85085547571

SN - 1877-0509

VL - 170

SP - 911

EP - 916

JO - Procedia Computer Science

JF - Procedia Computer Science

Y2 - 6 April 2020 through 9 April 2020

ER -

Machine Learning for Traffic Analysis: A Review

Abstract

Keywords

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