Feature-based systematic analysis of advanced persistent threats
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Authors
Miguez, M.
Sarrafpour, Bahman
Sarrafpour, Bahman
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Date
2023-05-22
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Type
Journal Article
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
advanced persistent threat (APT)
targeted attack (TA)
artificial intelligence (AI)
cyber attacks
cyber espionage
targeted attack (TA)
artificial intelligence (AI)
cyber attacks
cyber espionage
ANZSRC Field of Research Code (2020)
Citation
Miguez, M., & Sassani (Sarrafpour), B. (2023). Feature-based systematic analysis of advanced persistent threats
Threats. AI, Computer Science and Robotics Technology, 2023(2), 1–41. https://doi:10.5772/acrt.21
Abstract
Advanced Persistent Threats (APT) and Targeted Attacks (TA) targeting high-value organizations continue to become more common. These slow (sometimes carried on over the years), fragmented, distributed, seemingly unrelated, very sophisticated, highly adaptable, and, above all, stealthy attacks have existed since the large-scale popularization of computing in the 1990s and have intensified during the 2000s. The aim of attackers has expanded from espionage to attaining financial gain, creating disruption, and hacktivism. These activities have a negative impact on the targets, many times costing significant amounts of money and destabilizing organizations and governments. The resounding goal of this research is to analyze previous academic and industrial research of 72 major APT attacks between 2008 and 2018, using 12 features, and propose a categorization based on the targeted platform, the time elapsed to discovery, targets, type, purpose, propagation methods, and derivative attacks. This categorization provides a view of the effort of the attackers. It aims to help focus the design of intelligent detection systems on increasing the percentage of discovered and stopped attacks.
Publisher
InTech
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DOI
http://doi:10.5772/acrt.21
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Attribution 4.0 International