Browsing by Author "Buckshumiyan, A."

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    AZTEM: A Self-evolving zero-trust model for adaptive cloud security using AI-driven threat mitigation and quantum-resilient encryption
    (IEEE, 2026-02-20) Khemraj, Ikhar Avinash; Buckshumiyan, A.; Balassem, Zayd; Shekar, A.; Arunkumar, B.; Ali, Guma; Jabborov, Nurboy
    The new digital ecosystems are being based on cloud environments but they are highly vulnerable to the shifts in cyber threat, which can take advantage of the stationary trust models, loose access rules and encryption functions that hangs in the air with the advent of quantum computing. The traditional models of perimeter based security fail to operate in highly dynamic cloud environments with insider based mauling, cross-lateral mauling and the zero day mauling bypassing the traditional security models. In this work, A Self-evolving Zero-Trust Model of Adaptive Cloud security with AI-based Threat Mitigation and Quantumresilient Encryption is introduced and is capable of addressing these emerging problems. AZTEM eliminates the implicit trust, validating and permitting all user, device, and microservice relations through the utilization of a contextsensitive trust engine that is reinforced by reinforcement learning algorithms. To counter the rapid adaptations of attack vectors, the system relies on the deep-learning-based anomaly detection methods such as LSTM-based sequence classifiers on cloud workload telemetry, network flow logs, and publicly accessible datasets (UNSW-NB15 and CICIDS2018). An adaptive policy controller is the basis of dynamic orchestration of mitigation strategies and uses AI-based response mechanisms to quarantine, reroute, or restrict malicious sessions in real time. Besides, the model includes quantum-resilient encryption that extends the protection of both data-at-rest and data-in-transit by using lattice cryptography to secure quantum decryption threats where confidentiality is ensured at the long-term scale. An initial deployment of AWS on Kubernetes clusters demonstrated that threat detection accuracy and false positives decreased by 27 percent and 34 percent respectively over a baseline zero-trust deployment, and performance overhead is less than 8 percent. Its application in adversarial context was assessed through measures such as precision, recall, F1-score and confidence intervals and proved it to be qualified. The architecture is not only making the cloud more resilient to the present, and the infrastructures of the quantum age impervious to the threats of the quantum age, but it is also providing businesses, governments and hard-to-secure areas in need of cloud security without a trade-off an ethical and scaleable and deployment-ready path.