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Malware detection in IoT environments necessitates robust methodologies. Thisstudy introduces a CNN-LSTM hybrid model for IoT malware identification andevaluates its performance against established methods. Leveraging K-foldcross-validation, the proposed approach achieved 95.5% accuracy, surpassingexisting methods. The CNN algorithm enabled superior learning modelconstruction, and the LSTM classifier exhibited heightened accuracy inclassification. Comparative analysis against prevalent techniques demonstratedthe efficacy of the proposed model, highlighting its potential for enhancingIoT security. The study advocates for future exploration of SVMs asalternatives, emphasizes the need for distributed detection strategies, andunderscores the importance of predictive analyses for a more powerful IOTsecurity. This research serves as a platform for developing more resilientsecurity measures in IoT ecosystems.

Malware Detection in IOT Systems Using Machine Learning Techniques