Hybrid Quantum Classical Convolutional Neural Network with Spider Wasp Optimizer for Enhanced Data Security in Cloud Environments

The growing volume of sensitive data being handled and kept online makes data protection in cloud-based environments essential. Maintaining the integrity and confidentiality of cloud-based systems requires strong protection against cyber-attacks and data breaches. This research presents a novel method utilizing a Hybrid Quantum-Classical Convolutional Neural Network with Spider Wasp Optimizer (HQCCNN-SWO) in the search for cutting-edge data security solutions for cloud environments. Utilizing cutting-edge methodologies, this approach improves data security detection's precision and effectiveness. To ensure that the model covers a wide range of attack scenarios, it is trained and assessed using three widely-used datasets: NSL-KDD, KDDCUP99, and UNSW-NB15. To improve the resilience of feature extraction and normalize data, the methodology starts with global-local depth normalization-based preprocessing. This addresses variances in input distributions and improves consistency. After that, features are extracted using a geometric algebra transformer, which enhances feature representation by identifying complex patterns and correlations in the data. Utilizing the best features of both classical and quantum computers, the model's central component, HQCCNN-SWO, combines quantum-classical convolutional neural network architecture. By using its sophisticated search capabilities and adaptability mechanisms, the Spider Wasp Optimizer (SWO) optimizes performance by fine-tuning network settings. HQCCNN-SWO is a potent framework that can be used in MATLAB to detect security threats more accurately and with less computational overhead. This methodology constitutes a significant progression in the identification of data security, providing a resilient and effective instrument for cloud settings. HQCCNN-SWO is a top-performing solution in the field of cloud data security because it combines advanced optimization techniques with quantum-classical methodologies.