The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
The Emerging Threat of Ai-driven Cyber Attacks: A Review
Open accessAbstract
Cyberattacks are becoming more sophisticated and ubiquitous. Cybercriminals are inevitably adopting Artificial Intelligence (AI) techniques to evade the cyberspace and cause greater damages without being noticed. Researchers in cybersecurity domain have not researched the concept behind AI-powered cyberattacks enough to understand the level of sophistication this type of attack possesses. This paper aims to investigate the emerging threat of AI-powered cyberattacks and provide insights into malicious used of AI in cyberattacks. The study was performed through a three-step process by selecting only articles based on quality, exclusion, and inclusion criteria that focus on AI-driven cyberattacks. Searches in ACM, arXiv Blackhat, Scopus, Springer, MDPI, IEEE Xplore and other sources were executed to retrieve relevant articles. Out of the 936 papers that met our search criteria, a total of 46 articles were finally selected for this study. The result shows that 56% of the AI-Driven cyberattack technique identified was demonstrated in the access and penetration phase, 12% was demonstrated in exploitation, and command and control phase, respectively; 11% was demonstrated in the reconnaissance phase; 9% was demonstrated in the delivery phase of the cybersecurity kill chain. The findings in this study shows that existing cyber defence infrastructures will become inadequate to address the increasing speed, and complex decision logic of AI-driven attacks. Hence, organizations need to invest in AI cybersecurity infrastructures to combat these emerging threats.
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“researchers have warned about the potential misuse of such technologies to generate mis/disinformation and harmful content (Bommasani et al., 2021;Yamin et al., 2021;Guembe et al., 2022;Goldstein et al., 2023).”
From Anatomy of an AI-powered malicious social botnet (2024) · cites this paper as Guembe et al., 2022
In the citing paper
“Therefore, LLMs have the potential to reshape the landscape of cyber-social security dramatically, a concern shared by many researchers (Bommasani et al., 2021;Yamin et al., 2021;Guembe et al., 2022;Goldstein et al., 2023;Kucharavy et al., 2023).”
From Anatomy of an AI-powered malicious social botnet (2024) · cites this paper as Guembe et al., 2022
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Cites
Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2010) · International Journal of Surgery · doi:10.1016/j.ijsu.2010.02.007 · Cited by 12,156
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
Xu, W., Evans, D., & Qi, Y. (2018) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2018.23198 · Cited by 2,204
August, W. U., Moore, D., Voelker, G. M., & Savage, S. (2004) · IEEE Security & Privacy · doi:10.1109/msp.2004.9 · Cited by 1,519
13th USENIX Workshop on Offensive Technologies, WOOT 2019
Gantman, A., & Maurice, C. (2019) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 934
The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation
Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Filar, B., et al. (2018) · Apollo - University of Cambridge Repository · doi:10.17863/cam.22520 · Cited by 849
The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation
Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Filar, B., et al. (2018) · arXiv · doi:10.48550/arxiv.1802.07228 · Cited by 849
PassGAN: A Deep Learning Approach for Password Guessing
Hitaj, B., Gasti, P., Ateniese, G., & Pérez‐Cruz, F. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21568-2_11 · Cited by 374
The ai-based cyber threat landscape: A survey
Kaloudi, N., & Li, J. (2021) · ACM Computing Surveys · doi:10.1145/3372823 · Cited by 310
Anderson, H. S., Woodbridge, J., & Filar, B. (2016) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/2996758.2996767 · Cited by 209
Bringing a GAN to a Knife-Fight: Adapting Malware Communication to Avoid Detection
Rigaki, M., & García, S. (2018) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw.2018.00019 · Cited by 186
Research works printed but not linked: 6
- Bahnsen, A. C., I. Torroledo, L. Camacho, and S. Villegas. 2018. DeepPhish: Simulating malicious AI. In APWG Symposium on Electronic Crime Research, London, United Kingdom, 1–8.
- Bursztein, E., J. Aigrain, A. Moscicki, and J. C. Mitchell. 2014. The end is nigh: generic solving of text-based CAPTCHAs. 8th Usenix workshop on Offensive Technologies WOOT ‘14, San Diego, CA, USA.
- 10.24507/icicelb.11.12.1115 · DOI
- Hu W. and Y. Tan. 2021. Generating adversarial malware examples for black-box attacks based on GAN.https://arxiv.org/abs/1702.05983 (accessed August 12 2021).
- John, S., and T. Philip. 2018. Generative models for spear phishing posts on social media. NIPS Workshop On Machine Deception, California, USA. arXiv:1802.05196 · arXiv
- Trieu, K., and Y. Yang. 2018. Artificial intelligence-based password brute force attacks. Proceedings of Midwest Association for Information Systems Conference, St. Louis, Missouri, USA, 13(39).
Cited by
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Cyber security: State of the art, challenges and future directions
Admass, W. S., Munaye, Y. Y., & Diro, A. A. (2023) · Cyber Security and Applications · doi:10.1016/j.csa.2023.100031 · Cited by 302
A Bibliometric Review of Large Language Models Research from 2017 to 2023
Fan, L., Li, L., Ma, Z., Lee, S., Yu, H., & Hemphill, L. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3664930 · Cited by 277
Saleh, A. M. S. (2024) · Blockchain Research and Applications · doi:10.1016/j.bcra.2024.100193 · Cited by 180
Cybersecurity of Smart Inverters in the Smart Grid: A Survey
Yuanliang, L., & Yan, J. (2022) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2022.3206239 · Cited by 143
Malatji, M., & Tolah, A. (2024) · AI and Ethics · doi:10.1007/s43681-024-00427-4 · Cited by 168
Metadata
- Authors
- Guembe, B., Azeta, A., Misra, S., Osamor, V. C., Fernandez-Sanz, L., & Pospelova, V.
- Affiliations
- Covenant University (NG)
- Namibia University of Science and Technology (NA)
- Østfold University of Applied Sciences (NO)
- University of Alcala
- Universidad de Alcalá (ES)
- Corresponding author
- Misra, Sanjay
- ORCID
- Venue
- Applied Artificial Intelligence
- Publisher
- Informa UK Limited
- Published
- 2022-03-04
- Volume
- 36
- Issue
- 1
- ISSN
- 0883-9514 (ISSN-L)
- 1087-6545
- OpenAlex
- W4214938478
- Cited by
- 267
- References
- 29
- Citations by year
- 2026: 48 · 2025: 96 · 2024: 83 · 2023: 35 · 2022: 5
- FWCI
- 18.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Applied Artificial Intelligence (publishedVersion)
- Covenant University Repository (Covenant University) (acceptedVersion)
- e_Buah (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
- Funders
- Universidad de Alcalá (Q2818018J)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Keywords
- AI-driven cyberattacks, adversarial learning, cyber threats, attack phase, cyber defense
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