Enhanced cloud security with Bi-Optimized Sand Cat Swarm and Conv-Bi-ALSTM deep learning models

Abstract

As cyberattacks on cloud infrastructures become increasingly frequent and sophisticated, there is a growing demand for intelligent, scalable, and efficient intrusion detection systems (IDS). Traditional machine learning (ML) and deep learning (DL) models often struggle with computational complexity, data quality dependency, and scalability challenges. To address these limitations, this study introduces a novel AI-driven framework, Bi-Optimized SandCat-Conv-Bi-ALSTM (Bi-SC-CBALSTM), for enhanced threat detection in cloud environments. The framework begins with robust data preprocessing, employing Minkowski distance for redundancy elimination, nearest neighbor imputation for missing values, and min–max normalization for feature scaling. To resolve class imbalance, the ADASYN technique adaptively synthesizes minority samples near decision boundaries. For feature selection, the Binary Sand Cat Swarm Optimization (BOSCSA) algorithm efficiently extracts relevant features from high-dimensional data. These features are then passed into a hybrid deep model Conv-Bi-ALSTM, which combines convolutional layers for spatial feature extraction and a bidirectional LSTM enhanced with a 1 − tanh(x) function for improved sequential learning. Dropout layers are integrated to prevent overfitting, followed by a fully connected classifier. Experimental evaluation demonstrates that the proposed model achieves a balanced accuracy, precision, recall, and F1-score of 96 %, validating its robustness, scalability, and potential for real-time cloud threat detection.