Evaluation of one-class algorithms for anomaly detection in home networks

We have seen significant growth in adopting new protocols and intelligent devices for the residential environment in recent years. These devices provide convenience, security, and energy efficiency to users. For instance, security cameras can detect unauthorized movement, and smoke sensors can detect potential fire accidents. However, many examples have shown that such devices may generate new cyber threats causing concerns about the security of sensitive data present in the home environment. In this article, we explore the application of machine learning to identify anomalous activities that can take place in a smart-home environment. We evaluated the performance of twelve one-class algorithms in three test cases; the best models showed an AUC higher than 94%, which indicates the usefulness of adopting unsupervised learning algorithms in identifying anomalous traffic in home networks.