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Cited by 267Adversarial Robustness in Machine LearningNetwork Security and Intrusion DetectionInformation and Cyber Security

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 access
Guembe, Blessing, Azeta, Ambrose, Misra, Sanjay, Osamor, Victor Chukwudi, Fernandez-Sanz, Luis, Pospelova, Vera·2022·Applied Artificial Intelligence
DOI 10.1080/08839514.2022.2037254
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Abstract

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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Citations

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Show all 29 references

Research works printed but not linked: 6

  1. 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.
  2. 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.
  3. 10.24507/icicelb.11.12.1115 · DOI
  4. 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).
  5. 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
  6. 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).

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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
DOI
10.1080/08839514.2022.2037254
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
Licence
doi.org/10.1080/policies-text-data-mining (tdm)
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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