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Application of Artificial Intelligence and Machine Learning in Producing Actionable Cyber Threat Intelligence

Reza Montasari Orcid Logo, Fiona Carroll, Stuart Macdonald Orcid Logo, Hamid Jahankhani, Amin Hosseinian-Far, Alireza Daneshkhah

Digital Forensic Investigation of Internet of Things (IoT) Devices, Pages: 47 - 64

Swansea University Authors: Reza Montasari Orcid Logo, Stuart Macdonald Orcid Logo

Abstract

Cyber Threat Intelligence (CTI) can be used by organisations to assist their security teams in safeguarding their networks against cyber-attacks. This can be achieved by including threat data feeds into their networks or systems. However, despite being an effective Cyber Security (CS) tool, many org...

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Published in: Digital Forensic Investigation of Internet of Things (IoT) Devices
ISBN: 978-3-030-60424-0 978-3-030-60425-7
Published: Springer 2021
Online Access: https://www.springer.com/gp/book/9783030604240
URI: https://cronfa.swan.ac.uk/Record/cronfa54803
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spelling v2 54803 2020-07-25 Application of Artificial Intelligence and Machine Learning in Producing Actionable Cyber Threat Intelligence e420369ac98aaaa7f39248e39a847af1 0000-0001-7136-6753 Reza Montasari Reza Montasari true false 933e714a4cc37c3ac12d4edc277f8f98 0000-0002-7483-9023 Stuart Macdonald Stuart Macdonald true false 2020-07-25 CSSP Cyber Threat Intelligence (CTI) can be used by organisations to assist their security teams in safeguarding their networks against cyber-attacks. This can be achieved by including threat data feeds into their networks or systems. However, despite being an effective Cyber Security (CS) tool, many organisations do not sufficiently utilise CTI. This is due to a number of reasons such as not fully understanding how to manage a daily flood of data filled with extraneous information across their security systems. This adds an additional layer of complexity to the tasksperformed by their security teams who might not have the appropriate tools or sufficient skills to determine what information to prioritise and what information to disregard. Therefore, to help address the stated issue, this paper aims firstly to provide an in-depth understanding of what CTI is and how it can benefit organisations, and secondly to deliver a brief analysis of the application of Artificial Intelligence and Machine Learning in generating actionable CTI. The key contribution of this paper is that it assists organisations in better understanding their approachto CTI, which in turn will enable them to make informed decisions in relation to CTI. Book chapter Digital Forensic Investigation of Internet of Things (IoT) Devices 47 64 Springer 978-3-030-60424-0 978-3-030-60425-7 1 1 2021 2021-01-01 https://www.springer.com/gp/book/9783030604240 COLLEGE NANME Criminology, Sociology and Social Policy COLLEGE CODE CSSP Swansea University 2023-09-18T07:59:20.6072287 2020-07-25T19:11:13.8093830 Faculty of Humanities and Social Sciences Hilary Rodham Clinton School of Law Reza Montasari 0000-0001-7136-6753 1 Fiona Carroll 2 Stuart Macdonald 0000-0002-7483-9023 3 Hamid Jahankhani 4 Amin Hosseinian-Far 5 Alireza Daneshkhah 6
title Application of Artificial Intelligence and Machine Learning in Producing Actionable Cyber Threat Intelligence
spellingShingle Application of Artificial Intelligence and Machine Learning in Producing Actionable Cyber Threat Intelligence
Reza Montasari
Stuart Macdonald
title_short Application of Artificial Intelligence and Machine Learning in Producing Actionable Cyber Threat Intelligence
title_full Application of Artificial Intelligence and Machine Learning in Producing Actionable Cyber Threat Intelligence
title_fullStr Application of Artificial Intelligence and Machine Learning in Producing Actionable Cyber Threat Intelligence
title_full_unstemmed Application of Artificial Intelligence and Machine Learning in Producing Actionable Cyber Threat Intelligence
title_sort Application of Artificial Intelligence and Machine Learning in Producing Actionable Cyber Threat Intelligence
author_id_str_mv e420369ac98aaaa7f39248e39a847af1
933e714a4cc37c3ac12d4edc277f8f98
author_id_fullname_str_mv e420369ac98aaaa7f39248e39a847af1_***_Reza Montasari
933e714a4cc37c3ac12d4edc277f8f98_***_Stuart Macdonald
author Reza Montasari
Stuart Macdonald
author2 Reza Montasari
Fiona Carroll
Stuart Macdonald
Hamid Jahankhani
Amin Hosseinian-Far
Alireza Daneshkhah
format Book chapter
container_title Digital Forensic Investigation of Internet of Things (IoT) Devices
container_start_page 47
publishDate 2021
institution Swansea University
isbn 978-3-030-60424-0
978-3-030-60425-7
publisher Springer
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str Hilary Rodham Clinton School of Law{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}Hilary Rodham Clinton School of Law
url https://www.springer.com/gp/book/9783030604240
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description Cyber Threat Intelligence (CTI) can be used by organisations to assist their security teams in safeguarding their networks against cyber-attacks. This can be achieved by including threat data feeds into their networks or systems. However, despite being an effective Cyber Security (CS) tool, many organisations do not sufficiently utilise CTI. This is due to a number of reasons such as not fully understanding how to manage a daily flood of data filled with extraneous information across their security systems. This adds an additional layer of complexity to the tasksperformed by their security teams who might not have the appropriate tools or sufficient skills to determine what information to prioritise and what information to disregard. Therefore, to help address the stated issue, this paper aims firstly to provide an in-depth understanding of what CTI is and how it can benefit organisations, and secondly to deliver a brief analysis of the application of Artificial Intelligence and Machine Learning in generating actionable CTI. The key contribution of this paper is that it assists organisations in better understanding their approachto CTI, which in turn will enable them to make informed decisions in relation to CTI.
published_date 2021-01-01T07:59:22Z
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