A hybrid trust based malware detection system using glow-worm swarm optimization and recurrent deep neural network for enhanced security for cloud applications
Sivakumar Depuru, M. Sakthivel, Anjana Devi Nandam, Sutharsini Sivanantham, K. J. Amala, Jalappagari Sainath · 2025
User credentials are exposed or might end up in a demilitarized zone due to a variety of software vulnerabilities and hardware threats. The research project aims to investigate and ultimately suggest a trust-based malware detection (TMD) method for the best possible classification of data. An enhanced Glow-Worm Swarm Optimization (IGWSO) technique is suggested to organize the taken dataset into various clusters. Recurrent deep neural network-(RNN) based computing is utilized to classify the suspected intrusion and derive varied trust levels for the cloud data after clustering. Rate of detection, precision, recall, and F-measures are the metrics used by the introduced Trust oriented Malware Detection System (TMDS) system, which is built in Java using the Cloud Simulator (CloudSim) tool to determine the algorithm&s;s efficacy over current state-of-the-art systems.