  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 of SVMs as
alternatives, emphasizes the need for distributed detection strategies, and
underscores the importance of predictive analyses for a more powerful IOT
security. This research serves as a platform for developing more resilient
security measures in IoT ecosystems.
