Deep Learning Hybridization For Improved Malware Detection In Smart Internet Of Things

This article discusses a hybrid deep-learning approach to malware detection using a diverse set of datasets and various techniques such as Single Hot Encode, Feature Engineering, and DBSCAN clustering. The proposed paradigm aims to mitigate overfitting and improve accuracy in detecting malicious software.