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

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
Open this paper in CoChat — free accountOpen PDF

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.

Integrity signals

Registry

Crossref · 10.1080/08839514.2022.2037254

Cites

30 of 30 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Researchers have warned about the potential misuse of LLMs to generate mis/disinformation and harmful content.

    “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

  • LLMs have the potential to reshape the landscape of cyber-social security dramatically.

    “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

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review