Malware Detection in IOT Systems Using Machine Learning Techniques. (arXiv:2312.17683v1 [cs.CR])

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 cnn construction detection environments hybrid identification iot iot malware machine machine learning malware malware detection malware identification performance study systems techniques validation