Intrusion Detection in Cloud Environment via Soft‐Max Deep Spectral Recurrent Neural Network

Cloud computing is one of the most promising technologies for effectively storing data and offering internet services. There are several benefits to using this quickly evolving technology instead of more conventional defenses to shield computer‐based systems from cyberattacks. In this paper, a novel Intrusion Detection in Cloud Environment via Soft‐max Deep Spectral Recurrent Neural Network has been proposed to improve the security in cloud computing. Initially, Data preprocessing using IoT‐23 dataset values reduces null or inappropriate feature values. Feature extraction utilizes Principal Component Analysis (PCA) to reduce dimensionality while retaining significant information. Feature selection is optimized using the Reptile Search algorithm (RSO) to prioritize relevant features by evaluating their relational weights. A Soft‐max Deep Spectral Recurrent Neural Network (SDSRN 2 ) classifies data into intrusion or non‐intrusion categories. Detected intrusions undergo further analysis using a Recursive Multi‐Perception Neural Classifier (RMNC) to assess risk levels. To evaluate the effectiveness of the proposed model, several metrics are utilized, namely accuracy, precision, F1 score, and recall. The performance analysis of accuracy attained by the proposed technique is 99.5%, which is higher than the existing technique. The proposed approach compared to existing methods such as SSAFS‐DLID, SeArch, Improved‐IDs, and the proposed model improves detection accuracy by 5.18%, 3.7%, and 1.77%, respectively.