Evaluating Lightweight Machine Learning Algorithms for Malware Detection in Resource Limited Environments

The rapid expansion of the Internet of Things (IoT) has revolutionized smart home environments, enhancing automation and efficiency through devices like smart thermostats, security cameras, and voice assistants. However, this technological advancement has introduced significant cybersecurity challenges, as many IoT devices enter the market with inadequate security measures. This paper addresses the critical issue of malware detection in smart home networks, focusing on the limitations of traditional security mechanisms such as firewalls and signature-based intrusion detection systems. We propose an automated machine learning-based solution that employs Decision Tree and Random Forest classifiers to detect and mitigate malware threats effectively. Our approach includes comprehensive data preprocessing, feature selection using Chi-Square and MRMR techniques, and model evaluation through accuracy, precision, recall, F1-score, and ROC-AUC metrics. The study utilizes the CTU-IoT-Malware-Capture dataset, demonstrating that both classifiers achieve high accuracy in distinguishing between benign and malicious network traffic. The Random Forest classifier shows superior performance, highlighting its potential for real-world smart home security applications. This research contributes to the development of adaptive and scalable malware detection systems, offering practical insights for network administrators, security professionals, and IoT manufacturers. By integrating machine learning with IoT security, we aim to establish a robust framework for protecting smart home ecosystems from evolving cyber threats.