Impact of Dataset Composition on Machine Learning Performance for Anomaly Detection in Smart Home Cybersecurity

Anomaly-based cyberattack detection plays a crucial role in protecting smart home environments by detecting and preventing potential threats and unauthorized access. However, there is a discernible gap in evaluating existing datasets based on real-world smart home features, highlighting their use cases and limitations, and providing recommendations for developing more suitable datasets for dynamic smart homes. This paper includes a comparative analysis of some selected datasets, an assessment of how the datasets' limitations impact the performance of machine learning-based anomaly detection techniques, and a discussion of their implications for anomaly-based cybersecurity research and practice. Furthermore, this research serves as a foundation for future studies in smart home anomaly-based detection, emphasizing the importance of high-quality datasets and adaptive detection techniques in securing smart homes and protecting user privacy.