Artificial Intelligence Techniques for Secure Cloud Data Storage and Retrieval Using Giant Trevally Optimizer with Quantum Convolutional Neural Network-Based Encryption Algorithm: Trends and Challenges

Abstract

Cloud computing has become a fundamental infrastructure for modern digital services due to its scalability, cost efficiency, and accessibility. However, the rapid growth of cloud environments has also raised significant concerns regarding data confidentiality, integrity, and secure access mechanisms. Traditional encryption methods and security protocols often struggle to cope with evolving cyber threats, large-scale data processing requirements, and the distributed architecture of cloud systems. To address these challenges, researchers are increasingly integrating artificial intelligence and advanced optimization techniques into cloud security frameworks.  Artificial Intelligence (AI)–driven approaches such as deep learning, metaheuristic optimization algorithms, and quantum-inspired computing models have demonstrated strong potential for enhancing data protection in cloud environments. In particular, the Giant Trevally Optimizer (GTO), a nature-inspired metaheuristic algorithm designed for complex optimization problems, has been widely explored for resource allocation, intrusion detection, and security optimization in distributed systems. The algorithm imitates the hunting behaviour of giant trevally fish and is capable of efficiently exploring large solution spaces to identify optimal configurations in cloud infrastructure.  Alongside optimization algorithms, Quantum Convolutional Neural Networks (QCNNs) and deep learning-based encryption techniques have emerged as promising solutions for secure data transmission and storage. QCNN-based encryption mechanisms combine the feature extraction capability of convolutional neural networks with quantum cryptographic principles to generate complex encryption keys and secure communication channels. Such hybrid techniques offer stronger resistance to cryptographic attacks and improve the randomness of encryption keys compared with traditional methods.