Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{196589,
        author = {Vidurga Sabarisha J and Mekaladevi S and Pooja sri S},
        title = {Adaptive Cyber Defence in Cloud Computing Using Artificial Intelligence Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {4848-4855},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196589},
        abstract = {The rapid adoption of cloud computing has transformed modern digital infrastructure by enabling scalable, flexible, and cost-effective services. However, this shift has also expanded the attack surface, making cloud environments increasingly vulnerable to sophisticated cyber threats such as distributed denial-of-service attacks, data breaches, and advanced persistent threats. Traditional security mechanisms, which are largely static and rule-based, often fail to detect and respond to dynamic and evolving attack patterns. This paper proposes an adaptive cyber defense framework for cloud computing environments using artificial intelligence techniques. The system leverages machine learning models to analyze network traffic, user behavior, and system activity in real time to identify anomalies and potential threats. By continuously learning from new data, the proposed approach enhances detection accuracy and enables proactive threat mitigation. Key techniques such as classification algorithms, anomaly detection models, and behavioral analysis are integrated to provide a multi layered defense strategy. The framework is designed to automatically respond to detected threats by triggering alerts, isolating compromised re sources, and preventing unauthorized access. Experimental evaluation demonstrates improved performance in terms of detection rate, reduced false positives, and faster response time compared to conventional methods. The proposed system contributes to building a resilient and intelligent security architecture for cloud environments, ensuring data integrity, confidentiality, and availability.},
        keywords = {Cloud Computing, Cybersecurity, Artificial Intelligence, Machine Learning, Anomaly Detection, Intrusion Detection, Adaptive Security},
        month = {April},
        }