A taxonomy of malicious traffic for intrusion detection systems

Hanan Hindy; Elike Hodo; Ethan Bayne; Amar Seeam; Robert Atkinson; Xavier Bellekens

doi:10.1109/CyberSA.2018.8551386

A taxonomy of malicious traffic for intrusion detection systems

Hanan Hindy, Elike Hodo, Ethan Bayne, Amar Seeam, Robert Atkinson, Xavier Bellekens

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

14 Citations (Scopus)

24 Downloads (Pure)

Abstract

With the increasing number of network threats it is essential to have a knowledge of existing and new network threats in order to design better intrusion detection systems. In this paper we propose a taxonomy for classifying network attacks in a consistent way, allowing security researchers to focus their efforts on creating accurate intrusion detection systems and targeted datasets.

Original language	English
Title of host publication	2018 International Conference on Cyber Situational Awareness, Data Analytics and Assessment, CyberSA 2018
Place of Publication	Piscataway, N.J.
Number of pages	4
ISBN (Electronic)	9781538645659
DOIs	https://doi.org/10.1109/CyberSA.2018.8551386
Publication status	Published - 28 Nov 2018
Event	2018 International Conference on Cyber Situational Awareness, Data Analytics and Assessment, CyberSA 2018 - Glasgow, United Kingdom Duration: 11 Jun 2018 → 12 Jun 2018

Conference

Conference	2018 International Conference on Cyber Situational Awareness, Data Analytics and Assessment, CyberSA 2018
Country/Territory	United Kingdom
City	Glasgow
Period	11/06/18 → 12/06/18

Keywords

network threats
detection system
network attacks

Access to Document

10.1109/CyberSA.2018.8551386

Hindy-etal-CyberSA-2018-A-taxonomy-of-malicious-traffic-for-intrusionAccepted author manuscript, 283 KB

Cite this

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title = "A taxonomy of malicious traffic for intrusion detection systems",

abstract = "With the increasing number of network threats it is essential to have a knowledge of existing and new network threats in order to design better intrusion detection systems. In this paper we propose a taxonomy for classifying network attacks in a consistent way, allowing security researchers to focus their efforts on creating accurate intrusion detection systems and targeted datasets.",

keywords = "network threats, detection system, network attacks",

author = "Hanan Hindy and Elike Hodo and Ethan Bayne and Amar Seeam and Robert Atkinson and Xavier Bellekens",

note = "{\textcopyright} 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; 2018 International Conference on Cyber Situational Awareness, Data Analytics and Assessment, CyberSA 2018 ; Conference date: 11-06-2018 Through 12-06-2018",

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doi = "10.1109/CyberSA.2018.8551386",

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Hindy, H, Hodo, E, Bayne, E, Seeam, A, Atkinson, R & Bellekens, X 2018, A taxonomy of malicious traffic for intrusion detection systems. in 2018 International Conference on Cyber Situational Awareness, Data Analytics and Assessment, CyberSA 2018., 8551386, Piscataway, N.J., 2018 International Conference on Cyber Situational Awareness, Data Analytics and Assessment, CyberSA 2018, Glasgow, United Kingdom, 11/06/18. https://doi.org/10.1109/CyberSA.2018.8551386

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AU - Hodo, Elike

AU - Bayne, Ethan

AU - Seeam, Amar

AU - Atkinson, Robert

AU - Bellekens, Xavier

N1 - © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2018/11/28

Y1 - 2018/11/28

N2 - With the increasing number of network threats it is essential to have a knowledge of existing and new network threats in order to design better intrusion detection systems. In this paper we propose a taxonomy for classifying network attacks in a consistent way, allowing security researchers to focus their efforts on creating accurate intrusion detection systems and targeted datasets.

AB - With the increasing number of network threats it is essential to have a knowledge of existing and new network threats in order to design better intrusion detection systems. In this paper we propose a taxonomy for classifying network attacks in a consistent way, allowing security researchers to focus their efforts on creating accurate intrusion detection systems and targeted datasets.

KW - network threats

KW - detection system

KW - network attacks

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U2 - 10.1109/CyberSA.2018.8551386

DO - 10.1109/CyberSA.2018.8551386

M3 - Conference contribution book

AN - SCOPUS:85059979455

BT - 2018 International Conference on Cyber Situational Awareness, Data Analytics and Assessment, CyberSA 2018

CY - Piscataway, N.J.

T2 - 2018 International Conference on Cyber Situational Awareness, Data Analytics and Assessment, CyberSA 2018

Y2 - 11 June 2018 through 12 June 2018

ER -

A taxonomy of malicious traffic for intrusion detection systems

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