Version 1
: Received: 13 July 2024 / Approved: 15 July 2024 / Online: 15 July 2024 (11:02:17 CEST)
How to cite:
CHRISTOPHER, G.; Arefin, S. Understanding APT Detection Using Machine Learning Algorithms: Is Superior Accuracy a Thing. Preprints2024, 2024071152. https://doi.org/10.20944/preprints202407.1152.v1
CHRISTOPHER, G.; Arefin, S. Understanding APT Detection Using Machine Learning Algorithms: Is Superior Accuracy a Thing. Preprints 2024, 2024071152. https://doi.org/10.20944/preprints202407.1152.v1
CHRISTOPHER, G.; Arefin, S. Understanding APT Detection Using Machine Learning Algorithms: Is Superior Accuracy a Thing. Preprints2024, 2024071152. https://doi.org/10.20944/preprints202407.1152.v1
APA Style
CHRISTOPHER, G., & Arefin, S. (2024). Understanding APT Detection Using Machine Learning Algorithms: Is Superior Accuracy a Thing. Preprints. https://doi.org/10.20944/preprints202407.1152.v1
Chicago/Turabian Style
CHRISTOPHER, G. and Sydul Arefin. 2024 "Understanding APT Detection Using Machine Learning Algorithms: Is Superior Accuracy a Thing" Preprints. https://doi.org/10.20944/preprints202407.1152.v1
Abstract
Advanced Persistent Threats (APTs) are sophisticated cyberattacks aimed at stealing sensitive information or causing damage over an extended period. Detecting APTs is crucial for maintaining cybersecurity, and machine learning (ML) has emerged as a powerful tool in this domain. This paper explores the role of ML algorithms in detecting APTs, comparing their accuracy and effectiveness. We evaluate various algorithms, discuss the challenges in achieving superior accuracy, and suggest strategies for improvement. Our findings highlight the potential and limitations of ML in APT detection, emphasizing the need for continuous advancements in this field.
Keywords
Background on Advanced Persistent Threats (APTs)Advanced Persistent Threats (APTs) represent a significant cybersecurity challenge due to their stealthy nature and prolonged duration. Unlike traditional cyberattacks, APTs are characterized by their persistence, as attackers remain undetected within networks for extended periods, often months or years. This persistence allows attackers to steal sensitive data, disrupt operations, or cause significant damage.
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
