Enhanced Ransomware Threat Detection Based On AI-Powered Deep Feature Engineering Using Intellectual Cyber Swarm Intelligence Technique With Hyper Capsule Deep Neural Network - Porkodi, C.; Thiyagarajan, P. (Journal of Neonatal Surgery, 2025)
Increasing the Internet of communication in heterogeneity connects the healthcare sectors to make incredible communication, monitoring, tracking, and analyzing the patients remotely through the Internet of Things. Such data are highly protective and contain sensitive and private information. Throughout, cyber-attacks are increased by attackers without knowing the user knowledge to create data breaches with the support of various malware, applications, and viruses. The fact Ransomware virus is one of the dangerous malicious activity programs tackled by attackers to create attacks to own the healthcare industries. So, privacy and security are essential to protect PHI data records from cyberattacks. Most of the traditional methodologies failed to analysis the Ransomware behavior impacts and logical progress leads poor accuracy in detection rate. Due to inadequate feature analysis, data shifting problems lead to increasing feature dimensions, producing a lower precision rate to degrade the identification rate. To resolve this problem, we propose an Artificial Intelligence powered deep feature engineering based on Intellectual Cyber Swarm Intelligence Technique (ICSIT) with Hyper Capsule Deep Neural Network (HCDNN) to identify the Ransomware properties effectively to enhance security. Initially, the preprocessing is carried out c-score normalization to verify the actual margin of feature presence in the RaNASP dataset, and the behavioural point of RaNSAP Malware Attack Impact Rate (RMDIR) is estimated by decisive margin class predictor to marginalize the affected features. Then, Linear Support Vector Swarm Intelligence Feature selection (LSV-SIFS) is applied to select the ransomware features. Finally, the Fuzzy Inference Hyper Capsule Multi Perceptron Neural Network (FIHc-MPNN) is applied to identify the behavioral class of ransomeware attack nature. The classifier marginalizes the defect feature limits to cover the mutual dependencies of attack behavior in the classifier unit to identify the attack. The proposed system produces high performance compared to the other systems by identifying Ransomware by increasing the precession recall rate to attain a higher actual positive rate compared to other traditional methods.