A Machine Learning-Based Anomaly Packets Detection for Smart Home

The advent of smart homes has revolutionized residential living, integrating advanced technologies and intelligent devices to create secure, comfortable, and efficient environments. However, this integration of diverse smart devices has brought significant cybersecurity challenges. Detecting and analyzing abnormal network packets have become paramount, signifying potential intrusions, malicious activities, or system errors and ensuring the security and stability of smart home systems. Machine learning techniques, such as Decision Trees, Support Vector Machines (SVM), Convolutional Neural Networks (CNN), K-Nearest Neighbors (KNN), Recurrent Neural Networks (RNN), and Random Forests, have shown promise in addressing these challenges. However, most research has concentrated on anomaly detection rather than malicious activity in smart homes. The vast datasets collected from various scenarios pose methodological and algorithmic challenges for applying machine learning techniques. To fill these research gaps, our study introduces traditional machine-learning methods for detecting abnormal network packets in smart homes using the IoT-23 dataset. It involves preprocessing the dataset, extracting relevant features, and training various machine learning models. The correlation matrix helps validate the feature selection of the best models based on performance metrics like precision, F1-score, recall, accuracy ratio, training score, and training time cost. Additionally, the study classifies 12 types of malicious malware across different machine learning models, considering performance within the context of smart home devices. This study implements real-time anomaly detection on the Raspberry Pi using packet captures and Zeek flowmeter methods. The findings contribute insights into models suitable for smart home security. In addition, our research enhances the understanding and application of machine learning methods for bolstering security in smart homes.