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

Department of Cybersecurity and Computing

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

15 Citations (Scopus)

260 Downloads (Pure)

Abstract

With the increasing number of network threats it is essential to have a knowledge of existing and new network threats 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 (Cyber SA)
Publisher	IEEE
Chapter	16
Number of pages	4
ISBN (Electronic)	9781538645659
ISBN (Print)	9781538645666
DOIs	https://doi.org/10.1109/CyberSA.2018.8551386
Publication status	Published - 29 Nov 2018
Event	2018 International Conference On Cyber Situational Awareness, Data Analytics And Assessment: 2018 International Conference On Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA) - Grand Central Hotel, Glasgow, United Kingdom Duration: 11 Jun 2018 → 12 Jun 2018

Conference

Conference	2018 International Conference On Cyber Situational Awareness, Data Analytics And Assessment
Abbreviated title	Cyber SA
Country/Territory	United Kingdom
City	Glasgow
Period	11/06/18 → 12/06/18

Keywords

Taxonomy
Intrusion detection
Computer crime
Malware

Access to Document

10.1109/CyberSA.2018.8551386

Bellekens_ATaxonomyOfMaliciousTrafficForIntrusion_Author_2018Accepted author manuscript, 409 KB

Intrusion Detection Systems using Machine Learning and Deep Learning techniques
Hindy, H. (Author), Coull, N. (Supervisor), Bayne, E. (Supervisor), Elsayed, S. H. (Supervisor) & Bellekens, X. (Supervisor), 7 Sept 2021
Student thesis: Doctoral Thesis

File

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 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.",

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

year = "2018",

month = nov,

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doi = "10.1109/CyberSA.2018.8551386",

language = "English",

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booktitle = "2018 International Conference On Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA)",

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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 (Cyber SA). IEEE , 2018 International Conference On Cyber Situational Awareness, Data Analytics And Assessment, Glasgow, United Kingdom, 11/06/18. https://doi.org/10.1109/CyberSA.2018.8551386

TY - GEN

T1 - A taxonomy of malicious traffic for intrusion detection systems

AU - Hindy, Hanan

AU - Hodo, Elike

AU - Bayne, Ethan

AU - Seeam, Amar

AU - Atkinson, Robert

AU - Bellekens, Xavier

PY - 2018/11/29

Y1 - 2018/11/29

N2 - With the increasing number of network threats it is essential to have a knowledge of existing and new network threats 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 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.

U2 - 10.1109/CyberSA.2018.8551386

DO - 10.1109/CyberSA.2018.8551386

M3 - Conference contribution

SN - 9781538645666

BT - 2018 International Conference On Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA)

PB - IEEE

T2 - 2018 International Conference On Cyber Situational Awareness, Data Analytics And Assessment

Y2 - 11 June 2018 through 12 June 2018

ER -

A taxonomy of malicious traffic for intrusion detection systems

Abstract

Conference

Keywords

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Student theses

Intrusion Detection Systems using Machine Learning and Deep Learning techniques

Cite this