Open Access
Survey of Artificial Intelligence-Driven Zero-Day Vulnerability Detection Techniques in Cloud Computing Systems
4
Assistant Professor, Shri Manilal Kadakia College of Commerce, Management,Science and Computer Studies, Ankleshwar, India
Abstract
The distributed design, virtualization, and multi-tenancy of cloud computing pose substantial cybersecurity concerns; however, it has become an essential technology for providing scalable and on-demand computing services. In particular, zero-day vulnerabilities pose a serious threat as they take advantage of undiscovered software defects prior to the release of security fixes, rendering traditional signature-based defense techniques useless. By facilitating intelligent threat detection, anomaly identification, and automated reaction, artificial intelligence (AI) has arisen as a potential solution to tackle these difficulties. A thorough examination of AI-driven methods for detecting zero-day vulnerabilities in cloud computing settings is provided in this article. It takes a look at cloud security basics, zero-day vulnerability lifecycle and effect, and new machine learning (ML), deep learning (DL), and hybrid AI detection methods. In addition, the report analyses current research, compares current approaches, and finds important gaps in understanding how to scale, protect privacy, implement in real-time, and generalize to other types of attacks. The findings indicate that although AI-based approaches have significantly improved detection performance, further research is required to develop interpretable, robust, and scalable cybersecurity solutions capable of mitigating evolving zero-day threats in dynamic cloud environments.
Keywords
Cloud Computing,
Zero-Day Vulnerabilities,
Cybersecurity
References
F. Khoda Parast, C. Sindhav, S. Nikam, H. Izadi Yekta, K. B. Kent, and S. Hakak, “Cloud computing security: A survey of service-based models,” Comput. Secur., vol. 114, p. 102580, Mar. 2022, doi: 10.1016/j.cose.2021.102580.
S. Singh, “Advancing Network Security in 5G: Leveraging the 5G-NIDD Dataset for Intrusion Detection and Mitigation,” in 2025 IEEE 12th International Conference on Cyber Security and Cloud Computing (CSCloud), IEEE, Nov. 2025, pp. 1–6. doi: 10.1109/CSCloud66326.2025.00055.
A. Arun, A. S. Nair, and A. G. Sreedevi, “Zero Day Attack Detection and Simulation through Deep Learning Techniques,” in 2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence), IEEE, Jan. 2024, pp. 852–857. doi: 10.1109/Confluence60223.2024.10463429.
Y. Guo, “A review of Machine Learning-based zero-day attack detection: Challenges and future directions,” 2023. doi: 10.1016/j.comcom.2022.11.001.
R. Kaur, D. Gabrijelčič, and T. Klobučar, “Artificial intelligence for cybersecurity: Literature review and future research directions,” Inf. Fusion, vol. 97, p. 101804, Sep. 2023, doi: 10.1016/j.inffus.2023.101804.
S. Kumar, M. Dwivedi, M. Kumar, and S. S. Gill, “A comprehensive review of vulnerabilities and AI-enabled defense against DDoS attacks for securing cloud services,” Comput. Sci. Rev., vol. 53, no. C, Aug. 2024, doi: 10.1016/j.cosrev.2024.100661.
A. A. Soni, M. Parikh, R. N. K. Dhenia, J. A. Soni, A. R. Jha, and S. M. Shah, “Reinforcement Learning for Dynamic Workflow Optimization in CI/CD Pipelines,” in 2025 IEEE 17th International Conference on Computational Intelligence and Communication Networks (CICN), IEEE, Dec. 2025, pp. 638–644. doi: 10.1109/CICN67655.2025.11367872.
V. K. Sharma, “Cloud Computing IoT: 5G Focused IoT with Cloud Solutions,” Int. J. AI, Big Data, Comput. Manag. Stud., vol. 6, no. 3, 2025, doi: 10.63282/3050-9416.IJAIBDCMS-V6I3P103.
Y. Muni, “Understanding The Evolution Of Internet Protocols: An In-Depth Review Of Ipv4 And Ipv6: A Comparative Review Of Transition Challenges And Solutions,” Int. J. Adv. Res. Comput. Sci., vol. 16, no. 4, pp. 109–117, Aug. 2025, doi: 10.26483/ijarcs.v16i4.7307.
E. Hashmi, M. M. Yamin, and S. Y. Yayilgan, “Securing tomorrow: a comprehensive survey on the synergy of Artificial Intelligence and information security,” AI Ethics, vol. 5, no. 3, pp. 1911–1929, Jun. 2025, doi: 10.1007/s43681-024-00529-z.
L. Bilge and T. Dumitras, “Before we knew it: An empirical study of zero-day attacks in the real world,” in Proceedings of the ACM Conference on Computer and Communications Security, 2012. doi: 10.1145/2382196.2382284.
A. Armijos and E. Cuenca, “Zero-day attacks: review of the methods used based on intrusion detection and prevention systems,” in 1st IEEE Colombian Caribbean Conference, C3 2023, 2023. doi: 10.1109/C358072.2023.10436218.
J. B. Mehta, “Autonomous Patch Validation For Zero-Day Exploits In Enterprise Clouds,” Int. J. Appl. Math., vol. 38, no. 4s, pp. 1270–1285, Oct. 2025, doi: 10.12732/ijam.v38i4s.685.
S. Singamsetty, “AI-powered Satellite Image Processing for Global Air Traffic Surveillance Techniques Using NCNN–EGSA Optimization Techniques,” in Machine Learning Based Air Traffic Surveillance System Using Image Processing, Emerald Publishing Limited, 2026, pp. 179–197. doi: 10.1108/978-1-80592-062-520251010.
S. Gupta, “Artificial Intelligence in Cyber Threat Detection: A Survey of Predictive Security Systems,” vol. 4, pp. 19–34, 2025.
A. Waheed, B. Seegolam, M. F. Jowaheer, C. L. X. Sze, E. T. F. Hua, and S. R. Sindiramutty, “Zero-Day Exploits in Cybersecurity: Case Studies and Countermeasure,” Jul. 2024. doi: 10.20944/preprints202407.2338.v1.
S. F. Ahmed et al., “Deep learning modeling techniques: current progress, applications, advantages, and challenges,” Artif. Intell. Rev., vol. 56, no. 11, pp. 13521–13617, Nov. 2023, doi: 10.1007/s10462-023-10466-8.
M. H. Hamza Afzal, “Securing AI Systems Against Adversarial Attacks: A Framework for Building Robust and Trustworthy Machine Learning Models,” Comput. Fraud Secur., no. 5, pp. 65–74, May 2024, doi: 10.52710/cfs.867.
D. Paul, S. A. Devanira Poovaiah, B. Nurullayeva, A. Kishore, V. S. Kumar Tankani, and S. Meylikulov, “SHO-Xception: An Optimized Deep Learning Framework for Intelligent Intrusion Detection in Network Environments,” in 2025 International Conference on Innovations in Intelligent Systems: Advancements in Computing, Communication, and Cybersecurity (ISAC3), IEEE, Jul. 2025, pp. 1–6. doi: 10.1109/ISAC364032.2025.11156610.
Y. Roumani, “Patching zero-day vulnerabilities: an empirical analysis,” J. Cybersecurity, vol. 7, no. 1, Nov. 2021, doi: 10.1093/cybsec/tyab023.
A. Sharma and U. K. Singh, “Cloud Computing Security Through Detection & Mitigation of Zero‐Day Attack Using Machine Learning Techniques,” in Natural Language Processing for Software Engineering, Wiley, 2025, pp. 357–388. doi: 10.1002/9781394272464.ch25.
M. Ahmadi, M. Chizari, M. Eslami, M. J. Golkar, and M. Vali, “Access control and user authentication concerns in cloud computing environments,” in 2015 1st International Conference on Telematics and Future Generation Networks (TAFGEN), IEEE, May 2015, pp. 39–43. doi: 10.1109/TAFGEN.2015.7289572.
S. V, G. V, S. G, N. S. P, and S. K, “Ransomware Detection and Prevention,” in 2025 6th International Conference for Emerging Technology (INCET), IEEE, May 2025, pp. 1–8. doi: 10.1109/INCET64471.2025.11140093.
[24] H. Abusaimeh, “Distributed Denial of Service Attacks in Cloud Computing,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 6, 2020, doi: 10.14569/IJACSA.2020.0110621.
S. Kanungo, “Data Privacy and Compliance Issues in Cloud Computing: Legal and Regulatory Perspectives,” Int. J. ofINTELLIGENT Syst. Appl. Eng., pp. 1721–1734, 2024.
N. Ahmad, “The Structural Modeling of Significant Factors for Sustainable Cloud Migration,” Int. J. Intell. Eng. Syst., vol. 14, no. 2, pp. 1–10, Apr. 2021, doi: 10.22266/ijies2021.0430.01.
S. Mohamed, S. Kamal, and S. Mostafa, “Advanced Persistent Threats: Detection and Mitigation,” 2024, doi: 10.5281/zenodo.20066260.
R. Kaur and M. Singh, “A survey on zero-day polymorphic worm detection techniques,” IEEE Commun. Surv. Tutorials, 2014, doi: 10.1109/SURV.2014.022714.00160.
[29] J. B. Mehta, “Securing Test Automation in Zero Trust Architectures: A Framework for Continuous Verification,” in 2025 International Conference on Computer and Applications (ICCA), IEEE, Dec. 2025, pp. 1–5. doi: 10.1109/ICCA66035.2025.11430950.
N. Adik, “The Rise of Open and Free Networks: A Community-Driven Paradigm for Decentralized Connectivity,” in 2025 IEEE First International Conference on Innovations in Engineering and Next-Generation Technologies for Sustainability (ICINVENTS), Coimbatore, India: IEEE, 2025, pp. 1–9, November. doi: 10.1109/ICINVENTS64613.2025.11402224.
R. Zaib and K. Q. Zhou, “Zero-Day Vulnerabilities: Unveiling the Threat Landscape in Network Security,” Mesopotamian J. CyberSecurity, 2022, doi: 10.58496/MJCS/2022/007.
P Archana, S Kumaravel, and K Gayathri Devi, “Artificial Intelligence–Based Zero-Day Attack Detection and Proactive Mitigation Strategies: A Literature Review,” Int. Res. J. Adv. Eng. Hub, vol. 4, no. 02, pp. 474–480, Feb. 2026, doi: 10.47392/IRJAEH.2026.0064.
D. Mirza and J. Hudson, “Emerging Cybersecurity Threats and the Role of Artificial Intelligence in Modern Organizations,” 2022. doi: 10.13140/RG.2.2.10500.64640.
S. Irfan, “Enhancing Email Security Through Accurate Phishing Detection Using Deep Transformer Models,” in 2026 World Conference on Computational Science and Technology (WcCST), 2026, pp. 239–244. doi: 10.1109/WcCST67302.2026.11495864.
L. Hasimi, D. Zavantis, E. Shakshuki, and A. Yasar, “Cloud Computing Security and Deep Learning: An ANN approach,” Procedia Comput. Sci., vol. 231, pp. 40–47, 2024, doi: 10.1016/j.procs.2023.12.155.
L. D. Cherng En, A. Alleessa Angelly, S. S. Qistina, N. Kumar Puppala, N. B. Nagaram, and N. Chawla, “Next-Generation Cloud-Native Cyber Defense Using Deep Learning Techniques,” in 2026 Innovations in Machine, Engineering, and Digital Conference (IMED), IEEE, Mar. 2026, pp. 1–6. doi: 10.1109/IMED68921.2026.11484443.
M. Malkawi and R. Alhajj, “AI-Powered Vulnerability Detection and Patch Management in Cybersecurity: A Systematic Review of Techniques, Challenges, and Emerging Trends,” Mach. Learn. Knowl. Extr., vol. 8, no. 1, p. 19, Jan. 2026, doi: 10.3390/make8010019.
L. Manjula and E. Saravanakumar, “Zero-Day Attack Detection in Cloud Security: A Survey of Machine Learning and Deep Learning Approaches,” in 2025 3rd International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), IEEE, Aug. 2025, pp. 2055–2062. doi: 10.1109/ICSCDS65426.2025.11166929.
K. Prabu, V. A, R. Radhakrishnan, R. P, E. Thenmozhi, and S. P. Ramesh, “Enhancing Cloud Security with AI-Driven Anomaly Detection for Zero-Day Threats,” in 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N), 2024, pp. 1087–1092. doi: 10.1109/ICAC2N63387.2024.10895696.
F. Deldar and M. Abadi, “Deep Learning for Zero-day Malware Detection and Classification: A Survey,” ACM Comput. Surv., 2024, doi: 10.1145/3605775.
N. Peppes, T. Alexakis, E. Adamopoulou, and K. Demestichas, “The Effectiveness of Zero-Day Attacks Data Samples Generated via GANs on Deep Learning Classifiers,” Sensors, 2023, doi: 10.3390/s23020900.
A.-T. Tran, T. D. Luong, X. S. Pham, and T. L. Tran, “Deep Models with Differential Privacy for Distributed Web Attack Detection,” in 2022 14th International Conference on Knowledge and Systems Engineering (KSE), IEEE, Oct. 2022, pp. 1–6. doi: 10.1109/KSE56063.2022.9953788.
Most read articles by the same author(s)
- Dr. Mariam Al-Falasi, Dr. Tao Zhang, AUGMENTING SIEM WITH THREAT INTELLIGENCE FOR PREDICTIVE CYBER DEFENSE: A PROACTIVE THREAT HUNTING APPROACH , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 2 No. 03 (2025): Volume 02 Issue 03
- Haruto Nakamura, Yui Takahashi, Adaptive Authentication Framework for Agentic AI Ecosystems: Protocol Design, Trust Evaluation, and Security Analysis , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Nguyen Minh Khoa, Tran Thi Lan Anh, Strategic Business Transformation through Data Analytics, Sustainable Practices, and Innovation Management , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Dr. Tanvi Das, James D. Walker, A FEDERATED MULTI-MODAL SYSTEM FOR INSIDER THREAT DETECTION IN ENERGY INFRASTRUCTURE USING BIOMETRIC AND CYBER DATA , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 2 No. 01 (2025): Volume 02 Issue 01
- Prof. Emily Zhang, Luca Romano, DEFENDING AGAINST EVOLVING CYBER THREATS: A HYBRID FRAMEWORK FOR ATTACK PATTERN ANALYSIS AND INTELLIGENCE INTEGRATION , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 2 No. 04 (2025): Volume 02 Issue 04
- Chinedu Okafor, Amina Yusuf Bello, A Strategic HR Framework for Reducing Employee Attrition and Enhancing Talent Acquisition in the Indian Banking Sector , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Julia H. Whitaker, PROACTIVE CYBER THREAT HUNTING AND PREDICTIVE INTELLIGENCE IN CLOUD-ENABLED CRITICAL INFRASTRUCTURE: AN INTEGRATED FRAMEWORK FOR RESILIENT DIGITAL ECOSYSTEMS , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 3 No. 02 (2026): Volume 03 Issue 02
- Dr. Amara Ndlovu, Dr. Faisal Khan, CYBERSECURITY IN VIRTUAL GATHERINGS: RISKS AND REMEDIAL STRATEGIES FOR VIDEO CONFERENCING SOFTWARE , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 2 No. 04 (2025): Volume 02 Issue 04
- Ms. Shivani Jain, A Survey on Deep Learning Approaches for Malware Detection and Classification , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Dr. Alistair C. Finch, From Reactive to Predictive: A Framework for Integrating Threat Intelligence with SIEM for Proactive Threat Hunting , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 2 No. 10 (2025): Volume 02 Issue 10
Similar Articles
- Ms. Shivani Jain, A Survey on Deep Learning Approaches for Malware Detection and Classification , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Dr. Hemant N. Patel, A Survey on Ransomware Detection and Prevention Using Machine Learning Models , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Hiroshi Tanaka, Yuki Nakamura, Improving JWT Security Through Dynamic Token Binding and Behavioral Context Analysis , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Chinedu Okafor, Amina Yusuf Bello, A Strategic HR Framework for Reducing Employee Attrition and Enhancing Talent Acquisition in the Indian Banking Sector , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Dr. Nguyen Van Minh, Dr. Tran Thi Lan, Cross-Layer Protocol Design and Integration Strategies for IoT and IoRT Convergence: An Analytical Review of Enabling Technologies, Challenges, and Emerging Solutions , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Dr. Arjun Mehta, Dr. Priya Nair, Comprehensive Review of Modern Health Monitoring Technologies and Digital Healthcare Services , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 3 No. 07 (2026): Volume 03 Issue 07
- Dr. Alistair C. Finch, From Reactive to Predictive: A Framework for Integrating Threat Intelligence with SIEM for Proactive Threat Hunting , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 2 No. 10 (2025): Volume 02 Issue 10
- Dr. Arjun Pratap Singh, Dr. Neha Verma, Research on Unusual Transmission Pattern Recognition in Telecommunication Infrastructure Using Fuzzy Equation Approach , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Dr. Dmitry V. Sokolov, Synergizing Generative AI and Explainable Machine Learning in Security Operations Centers: Mitigating Alert Fatigue and Enhancing Analyst Performance , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 2 No. 10 (2025): Volume 02 Issue 10
- Dr. Mateo Alvarez-Ruiz, From Reactive to Predictive Security: Integrating Threat Intelligence with SIEM for Proactive Threat Hunting , International Journal of Cyber Threat Intelligence and Secure Networking: Vol. 3 No. 01 (2026): Volume 03 Issue 01
You may also start an advanced similarity search for this article.