Machine learning for APT detection

Nowadays, countries face a multitude of electronic threats that have permeated almost all business sectors, be it private corporations or public institutions. Among these threats, advanced persistent threats (APTs) stand out as a well-known example. APTs are highly sophisticated and stealthy compute...

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Main Authors: Al-Aamri, Abdullah Said Ali, Abdulghafor, Rawad Abdulkhaleq Abdulmolla, Turaev, Sherzod, Al-Shaikhli, Imad Fakhri Taha, Zeki, Akram M., Talib, Shuhaili
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Language:English
English
English
Published: MDPI 2023
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Online Access:http://irep.iium.edu.my/108265/1/108265_Machine%20learning%20for%20APT%20detection.pdf
http://irep.iium.edu.my/108265/7/108265_Machine%20learning%20for%20APT%20detection_SCOPUS.pdf
http://irep.iium.edu.my/108265/13/108265_Machine%20Learning%20for%20APT%20Detection%20_%20WOS.pdf
http://irep.iium.edu.my/108265/
https://www.mdpi.com/2071-1050/15/18/13820
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Institution: Universiti Islam Antarabangsa Malaysia
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spelling my.iium.irep.1082652024-01-17T00:58:17Z http://irep.iium.edu.my/108265/ Machine learning for APT detection Al-Aamri, Abdullah Said Ali Abdulghafor, Rawad Abdulkhaleq Abdulmolla Turaev, Sherzod Al-Shaikhli, Imad Fakhri Taha Zeki, Akram M. Talib, Shuhaili T10.5 Communication of technical information Nowadays, countries face a multitude of electronic threats that have permeated almost all business sectors, be it private corporations or public institutions. Among these threats, advanced persistent threats (APTs) stand out as a well-known example. APTs are highly sophisticated and stealthy computer network attacks meticulously designed to gain unauthorized access and persist undetected threats within targeted networks for extended periods. They represent a formidable cybersecurity challenge for governments, corporations, and individuals alike. Recognizing the gravity of APTs as one of the most critical cybersecurity threats, this study aims to reach a deeper understanding of their nature and propose a multi-stage framework for automated APT detection leveraging time series data. Unlike previous models, the proposed approach has the capability to detect real-time attacks based on stored attack scenarios. This study conducts an extensive review of existing research, identifying its strengths, weaknesses, and opportunities for improvement. Furthermore, standardized techniques have been enhanced to enhance their effectiveness in detecting APT attacks. The learning process relies on datasets sourced from various channels, including journal logs, traceability audits, and systems monitoring statistics. Subsequently, an efficient APT detection and prevention system, known as the composition-based decision tree (CDT), has been developed to operate in complex environments. The obtained results demonstrate that the proposed approach consistently outperforms existing algorithms in terms of detection accuracy and effectiveness. MDPI 2023-09-16 Article PeerReviewed application/pdf en http://irep.iium.edu.my/108265/1/108265_Machine%20learning%20for%20APT%20detection.pdf application/pdf en http://irep.iium.edu.my/108265/7/108265_Machine%20learning%20for%20APT%20detection_SCOPUS.pdf application/pdf en http://irep.iium.edu.my/108265/13/108265_Machine%20Learning%20for%20APT%20Detection%20_%20WOS.pdf Al-Aamri, Abdullah Said Ali and Abdulghafor, Rawad Abdulkhaleq Abdulmolla and Turaev, Sherzod and Al-Shaikhli, Imad Fakhri Taha and Zeki, Akram M. and Talib, Shuhaili (2023) Machine learning for APT detection. Sustainability (Switzerland), 15 (18). pp. 1-16. E-ISSN 2071-1050 https://www.mdpi.com/2071-1050/15/18/13820 10.3390/su151813820
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
English
topic T10.5 Communication of technical information
spellingShingle T10.5 Communication of technical information
Al-Aamri, Abdullah Said Ali
Abdulghafor, Rawad Abdulkhaleq Abdulmolla
Turaev, Sherzod
Al-Shaikhli, Imad Fakhri Taha
Zeki, Akram M.
Talib, Shuhaili
Machine learning for APT detection
description Nowadays, countries face a multitude of electronic threats that have permeated almost all business sectors, be it private corporations or public institutions. Among these threats, advanced persistent threats (APTs) stand out as a well-known example. APTs are highly sophisticated and stealthy computer network attacks meticulously designed to gain unauthorized access and persist undetected threats within targeted networks for extended periods. They represent a formidable cybersecurity challenge for governments, corporations, and individuals alike. Recognizing the gravity of APTs as one of the most critical cybersecurity threats, this study aims to reach a deeper understanding of their nature and propose a multi-stage framework for automated APT detection leveraging time series data. Unlike previous models, the proposed approach has the capability to detect real-time attacks based on stored attack scenarios. This study conducts an extensive review of existing research, identifying its strengths, weaknesses, and opportunities for improvement. Furthermore, standardized techniques have been enhanced to enhance their effectiveness in detecting APT attacks. The learning process relies on datasets sourced from various channels, including journal logs, traceability audits, and systems monitoring statistics. Subsequently, an efficient APT detection and prevention system, known as the composition-based decision tree (CDT), has been developed to operate in complex environments. The obtained results demonstrate that the proposed approach consistently outperforms existing algorithms in terms of detection accuracy and effectiveness.
format Article
author Al-Aamri, Abdullah Said Ali
Abdulghafor, Rawad Abdulkhaleq Abdulmolla
Turaev, Sherzod
Al-Shaikhli, Imad Fakhri Taha
Zeki, Akram M.
Talib, Shuhaili
author_facet Al-Aamri, Abdullah Said Ali
Abdulghafor, Rawad Abdulkhaleq Abdulmolla
Turaev, Sherzod
Al-Shaikhli, Imad Fakhri Taha
Zeki, Akram M.
Talib, Shuhaili
author_sort Al-Aamri, Abdullah Said Ali
title Machine learning for APT detection
title_short Machine learning for APT detection
title_full Machine learning for APT detection
title_fullStr Machine learning for APT detection
title_full_unstemmed Machine learning for APT detection
title_sort machine learning for apt detection
publisher MDPI
publishDate 2023
url http://irep.iium.edu.my/108265/1/108265_Machine%20learning%20for%20APT%20detection.pdf
http://irep.iium.edu.my/108265/7/108265_Machine%20learning%20for%20APT%20detection_SCOPUS.pdf
http://irep.iium.edu.my/108265/13/108265_Machine%20Learning%20for%20APT%20Detection%20_%20WOS.pdf
http://irep.iium.edu.my/108265/
https://www.mdpi.com/2071-1050/15/18/13820
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