Book chapter 749 views
Application of Artificial Intelligence and Machine Learning in Producing Actionable Cyber Threat Intelligence
Reza Montasari 
,
Fiona Carroll,
Stuart Macdonald ,
Hamid Jahankhani,
Amin Hosseinian-Far,
Alireza Daneshkhah
,
Fiona Carroll,
Stuart Macdonald ,
Hamid Jahankhani,
Amin Hosseinian-Far,
Alireza Daneshkhah
Digital Forensic Investigation of Internet of Things (IoT) Devices, Pages: 47 - 64
Swansea University Authors:
Reza Montasari , Stuart Macdonald
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...
| 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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| 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 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. |
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| College: |
Faculty of Humanities and Social Sciences |
| Start Page: |
47 |
| End Page: |
64 |