Self Adaptive AI Framework for Cloud Centric Cybersecurity and Intelligent Threat Response Systems
DOI:
https://doi.org/10.15662/IJRAI.2023.0604011Keywords:
Self-Adaptive Systems, Artificial Intelligence, Cloud Cybersecurity, Threat Detection, Intelligent Response, Machine Learning, Deep Learning, Reinforcement Learning, Anomaly Detection, Behavioral Analytics, Cloud Computing, Data SecurityAbstract
The increasing dependence on cloud-centric infrastructures has transformed modern enterprise operations while simultaneously exposing them to advanced cybersecurity threats. Traditional security mechanisms often fail to address dynamic and evolving attack patterns due to their static and rule-based architectures. This research proposes a self-adaptive artificial intelligence (AI) framework designed to enhance cybersecurity in cloud environments through intelligent threat detection and automated response mechanisms. The framework integrates machine learning, deep learning, and reinforcement learning techniques to continuously learn from data, adapt to new threats, and improve detection accuracy over time. It employs behavioral analytics and anomaly detection to identify deviations from normal system activities in real time. Additionally, the framework incorporates secure authentication, encryption, and access control strategies to ensure data integrity and confidentiality. The self-adaptive nature of the system enables it to respond autonomously to threats, minimizing human intervention and reducing response time. Experimental evaluation demonstrates improved performance in terms of detection rate, scalability, and resilience compared to conventional systems. This research contributes to the development of next-generation cybersecurity solutions by combining adaptive intelligence with cloud-based infrastructures for proactive and efficient threat management.
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