Malware Detection in IOT Systems Using Machine Learning Techniques

Malware detection in IoT environments necessitates robust methodologies. This study introduces a CNN-LSTM hybrid model for IoT malware identification and evaluates its performance against established methods. Leveraging K-fold cross-validation, the proposed approach achieved 95.5% accuracy, surpassing existing methods. The CNN algorithm enabled superior learning model construction, and the LSTM classifier exhibited heightened accuracy in classification. Comparative analysis against prevalent techniques demonstrated the efficacy of the proposed model, highlighting its potential for enhancing IoT security. The study advocates for future exploration …

accuracy algorithm classifier cnn construction cs.cr cs.lg cs.ni detection environments hybrid identification iot k-fold lstm machine machine learning machine learning techniques malware malware detection performance robust study systems validation