Real-Time AI-Based Threat Intelligence for Cloud Security Enhancement

Pereira, Andrés, Ivanov, Nikolai, Wang, Zhihao

Innovative Multidisciplinary Journal of Applied Technology · 2025-yil

Annotatsiya

As cloud computing becomes the backbone of modern digital infrastructure, the escalating sophistication of cyber threats demands real-time, AI-driven security solutions. Traditional security frameworks struggle to keep pace with zero-day attacks, evolving malware, and complex multi-vector threats, necessitating a more intelligent and autonomous approach. This paper explores Real-Time AI-Based Threat Intelligence as a transformative solution for cloud security enhancement, leveraging machine learning, deep learning, and behavioral analytics to detect, analyze, and mitigate threats proactively. The proposed AI-driven framework integrates real-time data collection, anomaly detection, and predictive analytics, enabling instant threat response while reducing false positives. Supervised, unsupervised, and reinforcement learning models are evaluated for their efficacy in identifying emerging attack patterns, enhancing threat visibility, and automating security workflows. Case studies from leading cloud providers (AWS, Azure, Google Cloud) demonstrate significant improvements in threat detection accuracy, response time, and overall cloud resilience compared to traditional security methods. Additionally, this paper examines the role of federated learning in distributed threat intelligence, the impact of quantum computing on AI-driven cybersecurity, and the integration of AI with SIEM (Security Information and Event Management) systems for a holistic security approach. Challenges such as adversarial attacks, ethical concerns, and computational overhead are discussed, along with recommendations for researchers, cloud providers, and enterprises. By harnessing real-time AI-based threat intelligence, cloud security can transition from reactive defense to proactive resilience, ensuring autonomous, scalable, and adaptive protection against modern cyber threats. This research highlights the future of self-learning, AI-powered cybersecurity frameworks, paving the way for next-generation cloud security architectures.

Maqola ma’lumotlari
MualliflarPereira, Andrés, Ivanov, Nikolai, Wang, Zhihao
JurnalInnovative Multidisciplinary Journal of Applied Technology
NashriyotТДТУ Қўқон филиали
ISSN2995-486X, 2995-486x
Nashr sanasi2025-03-10
Jild3
Son3
Betlar36-54
TilIngliz
Ilmiy sohaTexnika
Indeksga qo‘shilgan2026-08-26

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APAPereira, Andrés, Ivanov, Nikolai, & Wang, Zhihao (2025). Real-Time AI-Based Threat Intelligence for Cloud Security Enhancement. Innovative Multidisciplinary Journal of Applied Technology, 3(3), 36-54. https://www.multijournals.org/index.php/innovative/article/view/3208
GOST (OAK)Pereira, Andrés, Ivanov, Nikolai, Wang, Zhihao Real-Time AI-Based Threat Intelligence for Cloud Security Enhancement // Innovative Multidisciplinary Journal of Applied Technology. – 2025. – T. 3, № 3. – P. 36-54.
MLAPereira, Andrés, Ivanov, Nikolai, and Wang, Zhihao. “Real-Time AI-Based Threat Intelligence for Cloud Security Enhancement.” Innovative Multidisciplinary Journal of Applied Technology, 3(3), 36-54, 2025. https://www.multijournals.org/index.php/innovative/article/view/3208
ChicagoPereira, Andrés, Ivanov, Nikolai, and Wang, Zhihao. “Real-Time AI-Based Threat Intelligence for Cloud Security Enhancement.” Innovative Multidisciplinary Journal of Applied Technology, 3(3), 36-54 (2025). https://www.multijournals.org/index.php/innovative/article/view/3208
HarvardPereira, Andrés, Ivanov, Nikolai, and Wang, Zhihao (2025) ‘Real-Time AI-Based Threat Intelligence for Cloud Security Enhancement’, Innovative Multidisciplinary Journal of Applied Technology, 3(3), 36-54. https://www.multijournals.org/index.php/innovative/article/view/3208
BibTeX
@article{pereiraandrs2025,
  title = {Real-Time AI-Based Threat Intelligence for Cloud Security Enhancement},
  author = {Pereira, Andrés and Ivanov, Nikolai and Wang, Zhihao},
  journal = {Innovative Multidisciplinary Journal of Applied Technology},
  year = {2025},
  volume = {3},
  number = {3},
  pages = {36-54},
  url = {https://www.multijournals.org/index.php/innovative/article/view/3208}
}

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