A review of the advances in cyber security benchmark datasets for evaluating data-driven based intrusion detection systems

Cybercrime has led to the loss of billions of dollars, the malfunctioning of computer systems, the destruction of critical information, the compromising of network integrity and confidentiality, etc. In view of these crimes committed on a daily basis, the security of the computer systems has becom...

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Main Authors:	Ibrahim, Adamu Abubakar, Haruna, Chiroma, Abdullahi Muaz, Sanah, Baballe Ila, Libabatu
Format:	Article
Language:	English English
Published:	Elsevier Ltd. 2015
Subjects:	Z665 Library Science. Information Science
Online Access:	http://irep.iium.edu.my/44715/ http://irep.iium.edu.my/44715/ http://irep.iium.edu.my/44715/ http://irep.iium.edu.my/44715/1/Elsevierr.pdf http://irep.iium.edu.my/44715/4/44715_A%20review%20of%20the%20advances%20in%20cyber%20security%20benchmark_Scopus.pdf

id	iium-44715
recordtype	eprints
spelling	iium-447152017-09-07T08:21:52Z http://irep.iium.edu.my/44715/ A review of the advances in cyber security benchmark datasets for evaluating data-driven based intrusion detection systems Ibrahim, Adamu Abubakar Haruna, Chiroma Abdullahi Muaz, Sanah Baballe Ila, Libabatu Z665 Library Science. Information Science Cybercrime has led to the loss of billions of dollars, the malfunctioning of computer systems, the destruction of critical information, the compromising of network integrity and confidentiality, etc. In view of these crimes committed on a daily basis, the security of the computer systems has become imperative to minimize and possibly avoid the impact of cybercrimes. In this paper, we review recent advances in the use of cyber security benchmark datasets for the evaluation of machine learning and data mining-based intrusion detection systems. It was found that the state-of-the-art cyber security benchmark datasets KDD and UNM are no longer reliable, because their datasets cannot meet the expectations of current advances in computer technology. As a result, a new ADFA Linux (ADFA-LD) cyber security benchmark dataset for the evaluation of machine learning and data mining-based intrusion detection systems was proposed in 2013 to meet the current significant advances in computer technology. ADFA-LD requires improvement in terms of full descriptions of its attributes. This review can be used by the research community as a basis for abandoning the previous state-of-the-art cyber security benchmark datasets and starting to use the newly introduced benchmark dataset for effective and robust evaluation of machine learning and data mining-based intrusion detection system Elsevier Ltd. 2015-03-05 Article PeerReviewed application/pdf en http://irep.iium.edu.my/44715/1/Elsevierr.pdf application/pdf en http://irep.iium.edu.my/44715/4/44715_A%20review%20of%20the%20advances%20in%20cyber%20security%20benchmark_Scopus.pdf Ibrahim, Adamu Abubakar and Haruna, Chiroma and Abdullahi Muaz, Sanah and Baballe Ila, Libabatu (2015) A review of the advances in cyber security benchmark datasets for evaluating data-driven based intrusion detection systems. Procedia Computer Science, 62. pp. 221-227. ISSN 1877-0509 http://www.sciencedirect.com/science/article/pii/S1877050915025788 10.1016/j.procs.2015.08.443
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	Z665 Library Science. Information Science
spellingShingle	Z665 Library Science. Information Science Ibrahim, Adamu Abubakar Haruna, Chiroma Abdullahi Muaz, Sanah Baballe Ila, Libabatu A review of the advances in cyber security benchmark datasets for evaluating data-driven based intrusion detection systems
description	Cybercrime has led to the loss of billions of dollars, the malfunctioning of computer systems, the destruction of critical information, the compromising of network integrity and confidentiality, etc. In view of these crimes committed on a daily basis, the security of the computer systems has become imperative to minimize and possibly avoid the impact of cybercrimes. In this paper, we review recent advances in the use of cyber security benchmark datasets for the evaluation of machine learning and data mining-based intrusion detection systems. It was found that the state-of-the-art cyber security benchmark datasets KDD and UNM are no longer reliable, because their datasets cannot meet the expectations of current advances in computer technology. As a result, a new ADFA Linux (ADFA-LD) cyber security benchmark dataset for the evaluation of machine learning and data mining-based intrusion detection systems was proposed in 2013 to meet the current significant advances in computer technology. ADFA-LD requires improvement in terms of full descriptions of its attributes. This review can be used by the research community as a basis for abandoning the previous state-of-the-art cyber security benchmark datasets and starting to use the newly introduced benchmark dataset for effective and robust evaluation of machine learning and data mining-based intrusion detection system
format	Article
author	Ibrahim, Adamu Abubakar Haruna, Chiroma Abdullahi Muaz, Sanah Baballe Ila, Libabatu
author_facet	Ibrahim, Adamu Abubakar Haruna, Chiroma Abdullahi Muaz, Sanah Baballe Ila, Libabatu
author_sort	Ibrahim, Adamu Abubakar
title	A review of the advances in cyber security benchmark datasets for evaluating data-driven based intrusion detection systems
title_short	A review of the advances in cyber security benchmark datasets for evaluating data-driven based intrusion detection systems
title_full	A review of the advances in cyber security benchmark datasets for evaluating data-driven based intrusion detection systems
title_fullStr	A review of the advances in cyber security benchmark datasets for evaluating data-driven based intrusion detection systems
title_full_unstemmed	A review of the advances in cyber security benchmark datasets for evaluating data-driven based intrusion detection systems
title_sort	review of the advances in cyber security benchmark datasets for evaluating data-driven based intrusion detection systems
publisher	Elsevier Ltd.
publishDate	2015
url	http://irep.iium.edu.my/44715/ http://irep.iium.edu.my/44715/ http://irep.iium.edu.my/44715/ http://irep.iium.edu.my/44715/1/Elsevierr.pdf http://irep.iium.edu.my/44715/4/44715_A%20review%20of%20the%20advances%20in%20cyber%20security%20benchmark_Scopus.pdf
first_indexed	2023-09-18T21:03:34Z
last_indexed	2023-09-18T21:03:34Z
_version_	1777410784036388864

A review of the advances in cyber security benchmark datasets for evaluating data-driven based intrusion detection systems

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