Autors: Tlachenska, E. A., Ivanov, K. I., Nenova, M. V., Valkova-Jarvis, Z. V., Kassev, K. M.
Title: Approaches for Implementing Artificial Intelligence in Cyber-security to Improve, Speed up and Optimize Processes
Keywords: AI, artificial intelligence, cyber security, machine learning, protection, response, threat detection, vulnerability

Abstract: This paper provides an examination of how Artificial intelligence applies in cyber security's difficult job of preventing and guarding information. Dealing with broad attack surface, big number of applications and large number of users makes the defended territory too vast to deal with [1]. All of this creates challenges for cyber security with the large amounts of data to be analyzed and understood. Traditional security methods are not enough to stop cybercriminals from breaching data and inflicting damage. Artificial Intelligence, with machine learning algorithms, continuous learning mechanisms and real-time data processing, offers fundamental tools to cybersecurity to use and enhance approaches to recognize network intrusions, data breaches, phishing and spam emails, malware attacks, and to alert security vulnerability when appears.

References

  1. A. S. Wilner, "Cybersecurity and its discontents: Artificial intelligence, the Internet of Things, and digital misinformation," International Journal-Canada's Journal of Global Policy Analysis, vol. 73, no. 2, 2018, pp. 308-316.
  2. M. Malatji, A. Tolah, "Artificial intelligence (AI) cybersecurity dimensions: a comprehensive framework for understanding adversarial and offensive AI," Journal of AI and Ethics, Springer, Feb. 2014, pp. 1-28.
  3. A. Ali et al., "The Effect of Artificial Intelligence on Cybersecurity," 2023 International Conference on Business Analytics for Technology and Security (ICBATS), Dubai, United Arab Emirates, 2023, pp. 1-7.
  4. A. D. Sontan, and S. V. Samuel, "The intersection of Artificial Intelligence and cybersecurity: Challenges and opportunities," World Journal of Advanced Research and Reviews, 2024, 21(02), pp. 1720-1736.
  5. D. Kant, A. Johannsen, "Evaluation of AI-based use cases for enhancing the cyber security defense of small and medium-sized companies (SMEs)," in Proc. of Electronic Imaging 2022 Conference, Jan. 2022, pp. 1-9.
  6. Global IP data traffic from 2016 to 2021-https://internal.statista.com/statistics/499431/global-ip-data-trafficforecast/
  7. R. Kaur, D. Gabrijelčič, T. Klobučar, "Artificial intelligence for cybersecurity: Literature review and future research directions," Journal of Information Fusion, vol. 97, Sep. 2023, pp. 1-29.
  8. K. Michael, R. Abbas and G. Roussos, "AI in Cybersecurity: The Paradox," in IEEE Transactions on Technology and Society, vol. 4, no. 2, June 2023, pp. 104-109.
  9. N. Katiyar, S. Tripathi, P. Kumar, S. Verma, A. Kumar Sahu, and S. Saxena, "AI and Cyber-Security: Enhancing threat detection and response with machine learning," kuey, vol. 30, no. 4, Apr. 2024, pp. 6273-6282.
  10. C. Polito, L. Pupillo, "Artificial Intelligence and Cybersecurity," Journal of Intereconomics, vol. 59, no. 1, 2024, pp. 10-13.
  11. M. S. Akhtar, T. Feng, "An overview of the applications of Artificial Intelligence in Cybersecurity," EAI Endorsed Transactions on Creative Technologies, vol. 8, no. 29, 2021, pp. 1-8.
  12. D. E. Denning, "An intrusion-detection model," IEEE Transactions on software engineering, no. 2, 1987, pp. 222-232.
  13. P. Garcia-Teodoro, J. Diaz-Verdejo, G. Macia-Fernandez, and E. Vazquez, "Anomaly-based network intrusion detection: Techniques, systems and challenges," Computers & Security, vol. 28, no. 1-2, 2009, pp. 18-28.
  14. E. Alpaydin, Introduction to machine learning. MIT press, 2020.
  15. M. Kjaerland "A taxonomy and comparison of computer security incidents from the commercial and government sectors," Computers & Security, vol. 25, no. 7, 2006, pp. 522-538.
  16. M. Egele, T. Scholte, E. Kirda, and C. Kruegel, "A survey on automated dynamic malware-analysis techniques and tools," ACM computing surveys (CSUR), vol. 44, no. 2, 2008, pp. 1-42.
  17. J. Hong, "The state of phishing attacks," Communications of the ACM, vol. 55, no. 1, 2012, pp. 74-81.
  18. Threatland Report-H2 2023-https://www.swascan.com/threatlandreport-h2-2023/
  19. N. Mohamed, "Current trends in AI and ML for cybersecurity: A stateof-the-art survey," Cogent Engineering, 10(2), 2023, pp. 1-30.
  20. K. Morovat and B. Panda, "A Survey of Artificial Intelligence in Cybersecurity," 2020 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 2020, pp. 109-115, doi: 10.1109/CSCI51800.2020.00026
  21. Biometric Technology Market Size, Share & Trends Analysis Report By Component, By Offering, By Authentication Type, By Application, By End-use, By Region, And Segment Forecasts, 2023-2030-https://www.grandviewresearch.com/industry-analysis/biometricsindustry
  22. N. Vemuri, N. Thaneeru and V. M. Tatikonda, "Adaptive generative AI for dynamic cybersecurity threat detection in enterprises," International Journal of Science and Research Archive, 2024, 11(01), pp. 2259-2265.
  23. R. Das and R. Sandhane, "Artificial Intelligence in Cyber Security," Journal of Physics: Conference Series, Volume 1964, Advances in Computer Science Engineering, pp. 1-10.

Issue

Proceedings of 2024 9th Junior Conference on Lighting, Lighting 2024, 2024, , https://doi.org/10.1109/Lighting62260.2024.10590694

Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus