Comparative Study of Quantum-Enhanced AI and Traditional AI in Cybersecurity

• Autorzy: Sindayigaya Jean Marie Vianney

Identyfikatory

DOI

10.15199/13.2025.2.3

Warianty tytułu

PL

Porównanie sztucznej inteligencji, tradycyjnej i wspomaganej kwantowo, w cyberbezpieczeństwie

• Języki publikacji: EN

Abstrakty

EN

The advancement of quantum computing and artificial intelligence (AI) has presented new challenges and opportunities in cybersecurity. This study compares the effectiveness of Quantum-Enhanced AI and Traditional AI in dealing with cyber threats, especially in terms of computing power, encryption, threat detection, and strategic applications. The research uses literature analysis from 25 significant studies on AI-based cybersecurity and quantum technology. The analysis results show that Quantum-Enhanced AI has significant advantages in faster data processing, Quantum Key Distribution (QKD)-based encryption, and real-time threat detection. This technology is also more adaptive to generative AI attacks and more efficient in securing IoT systems, financial infrastructure, and drone and satellite communications. However, the main challenges include infrastructure limitations, high implementation costs, and the lack of regulations supporting this technology’s widespread adoption. Considering the benefits and challenges, this study emphasizes the importance of investing in Quantum AI research and developing quantum-based cybersecurity standards. Quantum AI is expected to be the leading solution in dealing with cyber threats in the era of quantum computing.

PL

Postęp obliczeń kwantowych i sztucznej inteligencji (AI) stworzył nowe wyzwania i możliwości w zakresie cyberbezpieczeństwa. W tym badaniu porównano skuteczność sztucznej inteligencji wspomaganej kwantowo i tradycyjnej AI w radzeniu sobie z zagrożeniami cybernetycznymi, zwłaszcza pod względem mocy obliczeniowej, szyfrowania, wykrywania zagrożeń i strategicznych zastosowań. Analizowano literaturę z badań na temat cyberbezpieczeństwa opartego na AI i technologii kwantowej. Wyniki analizy pokazują, że sztuczna inteligencja wspomagana kwantowo ma znaczące zalety w szybszym przetwarzaniu danych, szyfrowaniu opartym na dystrybucji klucza kwantowego (QKD) i wykrywaniu zagrożeń w czasie rzeczywistym. Technologia ta jest również bardziej dostosowana do generatywnych ataków AI i bardziej wydajna w zabezpieczaniu systemów IoT, infrastruktury finansowej oraz komunikacji dronów i satelitarnej. Jednak główne wyzwania obejmują ograniczenia infrastruktury, wysokie koszty wdrożenia i brak przepisów wspierających powszechne przyjęcie tej technologii. Biorąc pod uwagę korzyści i wyzwania, niniejsze badanie podkreśla znaczenie inwestowania w badania nad sztuczną inteligencją kwantową i opracowywania standardów cyberbezpieczeństwa opartych na kwantach. Oczekuje się, że sztuczna inteligencja kwantowa będzie wiodącym rozwiązaniem w radzeniu sobie z zagrożeniami cybernetycznymi w erze komputerów kwantowych.

Słowa kluczowe

EN

quantum-enhanced AI; traditional AI; cybersecurity; quantum computing; threat detection

PL

kwantowe technologie informacyjne; dystrybucja klucza kwantowego; cyberbezpieczeństwo

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Elektronika : konstrukcje, technologie, zastosowania
• Rocznik: 2025
• Tom: Vol. 66, Nr 2
• Strony: 16--20
• Opis fizyczny: Bibliogr. 39 poz., tab.

Twórcy

autor

Sindayigaya Jean Marie Vianney

• Warsaw University of Technology, Poland

Bibliografia

• [1] M. J. H. Faruk, S. Tahora, M. Tasnim, H. Shahriar, and N. Sakib, “A Review of Quantum Cybersecurity: Threats, Risks and Opportunities,” in 2022 1st International Conference on AI in Cybersecurity, ICAIC 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/ICAIC53980.2022.9896970.
• [2] C. H. Ugwuishiwu, U. E. Orji, C. I. Ugwu, and C. N. Asogwa, “An overview of Quantum Cryptography and Shor’s Algorithm,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 5, pp. 7487-7495, 2020, doi: 10.30534/ijatcse/2020/82952020.
• [3] H . I. Kure, S. Islam, and H. Mouratidis, “An integrated cyber security risk management framework and risk predication for the critical infrastructure protection,” Neural Comput Appl, vol. 34, no. 18, pp. 15241–15271, 2022, doi: 10.1007/s00521-022-06959-2.
• [4] J. Kose, “Cyber Warfare: An Era of Nation-State Actors and Global Corporate Espionage,” ISSA Journal, pp. 12-15, 2021.
• [5] S . Varga, J. Brynielsson, and U. Franke, “Cyber-threat perception and risk management in the Swedish financial sector,” Comput Secur, vol. 105, 2021, doi: 10.1016/j.cose.2021.102239.
• [6] M. Malatji, A. L. Marnewick, and S. Von Solms, “Cybersecurity capabilities for critical infrastructure resilience,” Information and Computer Security, vol. 30, no. 2, pp. 255-279, 2022, doi: 10.1108/ICS-06-2021-0091.
• [7] M. Kalinin, V. Krundyshev, and P. Zegzhda, “Cybersecurity risk assessment in smart city infrastructures,” Machines, vol. 9, no. 4, 2021, doi: 10.3390/machines9040078.
• [8] K. K. Ko and E. S. Jung, “Development of Cybersecurity Technology and Algorithm Based on Quantum Computing,” Applied Sciences (Switzerland), vol. 11, no. 19, 2021, doi: 10.3390/app11199085.
• [9] P. Sharma, K. Choi, O. Krejcar, P. Blazek, V. Bhatia, and S. Prakash, “Securing Optical Networks Using Quantum-Secured Blockchain: An Overview,” Sensors, vol. 23, 2023, doi: 10.3390/s23031228.
• [10] M. G. Tehrani et al., “Stabilized quantum-enhanced SIEM architecture and speed-up through Hoeffding tree algorithms enable quantum cybersecurity analytics in botnet detection,” Sci Rep, vol. 14, no. 1, 2024, doi: 10.1038/s41598-024-51941-8.
• [11] M. A. Khan, S. Javaid, S. A. H. Mohsan, M. Tanveer, and I. Ullah, “Future-Proofing Security for UAVs With Post-Quantum Cryptography: A Review,” IEEE Open Journal of the Communications Society, 2024, doi: 10.1109/OJCOMS.2024.3486649.
• [12] R. Vadisetty and A. Polamarasetti, “Generative AI for Cyber Threat Simulation and Defense,” in 2024 12th International Conference on Control, Mechatronics and Automation (ICC MA), IEEE, 2024, pp. 272-279. doi: 10.1109/ICCMA63715.2024.10843938.
• [13] F . Raheman, “The Future of Cybersecurity in the Age of Quantum Computers,” Future Internet, vol. 14, no. 11, 2022, doi: 10.3390/fi14110335.
• [14] X. Liu et al., “Privacy and Security Issues in Deep Learning: A Survey,” IEEE Access, vol. 9, pp. 4566-4593, 2021, doi: 10.1109/ACCESS .2020.3045078.
• [15] A. Hummelholm, T. Hämäläinen, and R. Savola, “AI-based Quantum-safe Cybersecurity Automation and Orchestration for edge Intelligence in Future Networks,” in European Conference on Information Warfare and Security, ECCWS , Curran Associates Inc., 2023, pp. 696-702. doi: 10.34190/eccws.22.1.1211.
• [16] M. Krelina, “The Prospect of Quantum Technologies in Space for Defence and Security,” Space Policy, vol. 65, 2023, doi: 10.1016/j.spacepol.2023.101563.
• [17] N . M. Thomasian and E. Y. Adashi, “Cybersecurity in the Internet of Medical Things,” Sep. 01, 2021, Elsevier B.V. doi: 10.1016/j.hlpt.2021.100549.
• [18] P. B. Alipour and T. A. Gulliver, “QF-LCA dataset: Quantum field lens coding algorithm for system state simulation and strong predictions,” Data Brief, 2024, doi: 10.1016/j.dib.2024.110789.
• [19] M. T. Jafar, L. X. Yang, G. Li, and X. Yang, “The evolution of the flip-it game in cybersecurity: Insights from the past to the future,” Journal of King Saud University - Computer and Information Sciences, vol. 36, 2024, doi: 10.1016/j.jksuci.2024.102195.
• [20] A. Zineddine et al., “A systematic review of cybersecurity assessment methods for HTT PS,” Computers and Electrical Engineering, vol. 115, 2024, doi: 10.1016/j.compeleceng.2024.109137.
• [21] N . Bai, X. Hu, and S. Wang, “A survey on unmanned aerial systems cybersecurity,” Nov. 01, 2024, Elsevier B.V. doi: 10.1016/j.sysarc.2024.103282.
• [22] L . Sáez-Ortuño, R. Huertas-Garcia, S. Forgas-Coll, J. Sánchez- García, and E. Puertas-Prats, “Quantum computing for market research,” Journal of Innovation and Knowledge, vol. 9, 2024, doi: 10.1016/j.jik.2024.100510.
• [23] O. D. Okey, E. U. Udo, R. L. Rosa, D. Z. Rodríguez, and J. H. Kleinschmidt, “Investigating ChatGPT and cybersecurity: A perspective on topic modeling and sentiment analysis,” Comput Secur, vol. 135, 2023, doi: 10.1016/j.cose.2023.103476.
• [24] Y . Lu and J. Yang, “Quantum financing system: A survey on quantum algorithms, potential scenarios and open research issues,” 2024, Elsevier B.V. doi: 10.1016/j.jii.2024.100663.
• [25] S . AlDaajeh and S. Alrabaee, “Strategic cybersecurity,” Comput Secur, vol. 141, 2024, doi: 10.1016/j.cose.2024.103845.
• [26] S . K. Khan, N. Shiwakoti, A. Diro, A. Molla, I. Gondal, and M. Warren, “Space cybersecurity challenges, mitigation techniques, anticipated readiness, and future directions,” International Journal of Critical Infrastructure Protection, vol. 47, 2024, doi: 10.1016/j.ijcip.2024.100724.
• [27] B. Ramos-Cruz, J. Andreu-Perez, and L. Martínez, “The cybersecurity mesh: A comprehensive survey of involved artificial intelligence methods, cryptographic protocols and challenges for future research,” Neurocomputing, vol. 581, 2024, doi: 10.1016/j.neucom.2024.127427.
• [28] F . Muheidat, M. A. Mallouh, O. Al-Saleh, O. Al-Khasawneh, and L. A. Tawalbeh, “Applying AI and Machine Learning to Enhance Automated Cybersecurity and Network Threat Identification,” in Procedia Computer Science, Elsevier B.V., 2024, pp. 287-294. doi: 10.1016/j.procs.2024.11.112.
• [29] P. Radanliev, “Cyber diplomacy: defining the opportunities for cybersecurity and risks from Artificial Intelligence, IoT, Blockchains, and Quantum Computing,” Journal of Cyber Security Technology, 2024, doi: 10.1080/23742917.2024.2312671.
• [30] L . Sáez-Ortuño, R. Huertas-Garcia, S. Forgas-Coll, J. Sánchez- García, and E. Puertas-Prats, “Quantum Computing for Market Research,” Journal of Innovation and Knowledge, vol. 9, 2024, doi: 10.1016/j.jik.2024.100510.
• [31] F . Raza and K. Jatoi, “AI and Cybersecurity: An Ever-Evolving Landscape,” Cosmic Bulletin of Business Management, vol. 2, no. 1, 2023, doi: 10.13140/RG.2.2.18924.74885.
• [32] P. Radanliev, H. Ba, D. De Roure, and O. Santos, “Red Teaming Generative AI/NL P, the BB84 quantum cryptography protocol and the NIST-approved Quantum-Resistant Cryptographic Algorithms Short title: Red Teaming Generative AI and Quantum Cryptography.”
• [33] M. J. H. Faruk, S. Tahora, M. Tasnim, H. Shahriar, and N. Sakib, “A Review of Quantum Cybersecurity: Threats, Risks and Opportunities,” in 2022 1st International Conference on AI in Cybersecurity, ICAIC 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/ICAIC53980.2022.9896970.
• [34] A. Pandhare, “Enhancing Cybersecurity Through Quantum Computing and AxI: A Multi- Disciplinary Approach,” 2025. doi: 10.13140/RG.2.2.34797.29924.
• [35] E. Dibie, “Enhancing Cybersecurity for Renewable Energy with Quantum Algorithms and Cloud-Based AI,” Journal of Advances in Mathematics and Computer Science, vol. 39, no. 11, pp. 140-151, 2024, doi: 10.9734/jamcs/2024/v39i111944.
• [36] I. H. Sarker, “Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects,” Annals of Data Science, vol. 10, no. 6, pp. 1473-1498, 2023, doi: 10.1007/s40745-022-00444-2.
• [37] A. H. Salem, S. M. Azzam, O. E. Emam, and A. A. Abohany, “Advancing cybersecurity: a comprehensive review of AI-driven detection techniques,” J Big Data, vol. 11, no. 105, pp. 2-38, 2024, doi: 10.1186/s40537-024-00957-y.
• [38] F. S. Prity et al., “Machine learning-based cyber threat detection: an approach to malware detection and security with explainable AI insights,” Human-Intelligent Systems Integration, vol. 6, pp. 61-90, 2024, doi: 10.1007/s42454-024-00055-7.
• [39] M. Azeez et al., “Quantum AI for cybersecurity in financial supply chains: Enhancing cryptography using random security generators,” World Journal of Advanced Research and Reviews, vol. 23, no. 1, pp. 2443-2451, 2024, doi: 10.30574/wjarr.2024.23.1.2242.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).