Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/4034 2019 2019 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia Maha Maha Network security has become vulnerable to hacker threats owing to its advancement and easily accessible to computer and internet technology. Ransomware is the most commonly used malware in cyberattacks to mislead the victim user to expose private and sensitive data to hackers. Ransomware is malicious software that encodes the entire system or consumer’s files, creating it impossible, and later demands a payment fee from the victim’s computer in exchange for the decryption key. Ransomware attacks become highly popular and overwhelming for both individuals and organizations. Recently, deep learning (DL) and machine learning (ML) models are established to identify ransomware attacks in real-time and categorize them into various types. The system will be considered to examine the behaviors of malicious software and detect the particular kind of ransomware being utilized. This data will enhance the system’s accuracy and deliver appropriate data to cybersecurity professionals and victims. Therefore, this study proposes an accurate Ransomware Detection and classification using the Hybrid Metaheuristic Feature Selection with Deep Learning (RDC-HMFSDL) technique. The aim is in effectually detecting and classifying the ransomware attacks. Initially, the RDC-HMFSDL technique utilizes min-max model to transform the input data into a standard setting. Furthermore, the hybrid red deer sparrow search optimization (HRDSO) approach is used for the feature selection (FS). For ransomware attack detection, the long short-term memory autoencoder (LSTM-AE) approach is employed. Finally, the sine cosine algorithm (SCA) is used to optimally choose the parameter values of the LSTM-AE approach. The RDC-HMFSDL approach was tested on a benchmark dataset, achieving a superior accuracy of 99.88% compared to existing methods. 2026 2026 34 47 10.54216/JCIM.170204 https://www.americaspg.com/articleinfo/2/show/4034