Enhanced Malware Detection in Distributed IoT Environment Using Optimized Cascaded LSTM-GRU Framework

In the changing terrain of Internet of Things (IoT) security, especially in distributed systems, effective and fast virus detection is a challenging task. Using a Cascaded LSTM-GRU architecture, this work presents a Malware Detection Framework tuned for the special needs of edge and cloud computing. This method leverages Gated Recurrent Units (GRUs) for sequence data management and Long Short-Term Memory (LSTM) networks' strengths for temporal pattern recognition to increase the efficacy of malware detection. The empirical assessment of our methodology revealed remarkable classification accuracy and Fl scores. These findings demonstrate the framework's ability to greatly improve cybersecurity measures across smart computing systems, especially in edge and cloud computing environments, therefore marking major progress in the area of intelligent malware detection.