Machine Learning for APT Detection

Abdullah Said AL-Aamri, Rawad Abdulghafor (), Sherzod Turaev (), Imad Al-Shaikhli, Akram Zeki and Shuhaili Talib
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Abdullah Said AL-Aamri: Department of Computer Science, Faculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia
Rawad Abdulghafor: Department of Computer Science, Faculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia
Sherzod Turaev: Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates
Imad Al-Shaikhli: Department of Computer Science, Faculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia
Akram Zeki: Department of Computer Science, Faculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia
Shuhaili Talib: Department of Computer Science, Faculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia

Sustainability, 2023, vol. 15, issue 18, 1-16

Abstract: 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 effectiveess.

Keywords: APT; attacks; artificial intelligence; CDT (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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