An Efficient IOT-Malware Classification Model Using Ensemble Machine Learning

In the realm of Internet of Things (IoT) security, malware classification is crucial for identifying, isolating, and mitigating the impacts of malicious software that exploits system vulnerabilities. Effective IoT malware classification employs various machine learning algorithms to enhance detection accuracy and system resilience. The most commonly used Machine Learning algorithms for Malware Classification are "Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and Logistic Regression (LR)". Each of these algorithms has distinct advantages in identifying malware, but this study explores the creation of an Ensemble / Hybrid model by integrating the two most effective machine learning algorithms, which can lead to superior performance. The proposed model outperforms the simple models mentioned above and other related works discussed in the literature. Key performance metrics evaluated for this work includes Accuracy, Precision, Recall, F-Score. This advancement is vital for improving the security posture of IOT systems by providing a more reliable mechanism for early detection and effective response to emerging malware threats