USE OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES AGAINST CYBER THREATS IN THE PUBLIC SECTOR
DOI:
https://doi.org/10.5281/zenodo.20643563Abstract
The article addresses the application of artificial intelligence and machine learning technologies
for protecting public-sector information systems against cyber threats. The inadequacy of classical signaturebased
tools against modern attacks is analyzed. The use of ML models for intrusion detection, malware
classification, phishing protection, and user behavior analytics is reviewed. New risks linked to the use of AI
tools by attackers themselves are discussed.
Keywords
artificial intelligence, cybersecurity, public sector, machine learning, intrusion detection, phishing, deepfake.References
ENISA. ENISA Threat Landscape 2023. - European Union Agency for Cybersecurity, 2023. - 144 p.
Saranya T., Sridevi S., Deisy C. et al. Performance analysis of machine learning algorithms in intrusion
detection system: A review // Procedia Computer Science. - Elsevier, 2020. - Vol. 171. - P. 1251-1260.
Sarker I. H., Kayes A. S. M., Badsha S. et al. Cybersecurity data science: an overview from machine
learning perspective // Journal of Big Data. - Springer, 2020. - Vol. 7, No. 41. - P. 1-29.
Buczak A. L., Guven E. A survey of data mining and machine learning methods for cyber security
intrusion detection // IEEE Communications Surveys & Tutorials. – IEEE, 2016. - Vol. 18, No. 2. - P. 1153-1176.
Chandola V., Banerjee A., Kumar V. Anomaly detection: A survey // ACM Computing Surveys. - ACM,
- Vol. 41, No. 3. - P. 1-58.
Xin Y., Kong L., Liu Z. et al. Machine learning and deep learning methods for cybersecurity // IEEE
Access. - IEEE, 2018. - Vol. 6. - P. 35365-35381
Perez F., Ribeiro I. Ignore previous prompt: Attack techniques for language models // NeurIPS ML Safety
Workshop, 2022. - P. 1-10.
Ucci D., Aniello L., Baldoni R. Survey of machine learning techniques for malware analysis // Computers
& Security. - Elsevier, 2019. - Vol. 81. - P. 123-147.
Jain A. K., Gupta B. B. A survey of phishing attack techniques, defense mechanisms and open research
challenges // Enterprise Information Systems. - Taylor & Francis, 2022. - Vol. 16, No. 4. - P. 527-565.
Salem M., Hershkop S., Stolfo S. J. A survey of insider attack detection research // Insider Attack and
Cyber Security. - Springer, 2008. - P. 69-90.
Biggio B., Roli F. Wild patterns: Ten years after the rise of adversarial machine learning // Pattern
Recognition. - Elsevier, 2018. - Vol. 84. - P. 317-331.
Mirsky Y., Demontis A., Kotak J. et al. The threat of offensive AI to organizations // Computers &
Security. - Elsevier, 2023. - Vol. 124. - P. 1-28.
Arrieta A. B., Diaz-Rodriguez N., Del Ser J. et al. Explainable artificial intelligence (XAI): Concepts,
taxonomies, opportunities and challenges toward responsible AI // Information Fusion. - Elsevier, 2020. - Vol.
- P. 82-115.
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