Enhancing Cloud Storage Security with Intrusion Detection System using CNN and Grey Wolf Optimization Algorithm

Cloud Computing (CC) is the preference of all information technology (IT) organizations as it offers pay-per-use based and flexible services to its users. But the privacy and security become the main hindrances in its achievement due to distributed and open architecture that is prone to intruders. This research article proposes a novel approach for enhancing the security of cloud storage systems by developing an Intrusion Detection System (IDS) that utilizes feature extraction, pre-processing, and advanced analysis tools. Specifically, the proposed IDS leverage a Convolutional Neural Network (CNN) model and a grey wolf optimization algorithm to detect potential cyber-attacks on cloud storage. The research work focuses on extracting relevant features from the data and performing preprocessing to remove any irrelevant or noisy data points. The CNN model is then trained using the pre-processed data, while the grey wolf optimization algorithm is engaged to optimize the runtime performance of the IDS. The outcome of the proposed approach demonstrates its ability to detect various types of cyber-attacks with high accuracy and efficiency, which can help to improve the security of cloud storage systems. By measuring the effectiveness and efficiency of the algorithm, it can be determined whether GWO is a suitable optimization algorithm for intrusion detection in cloud security.