Anomaly Detection Models for Smart Home Security
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Computer Science and Electrical Engineering
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Computer Science
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Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission.
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
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Abstract
Recent years have seen significant growth in adoption of smart homes devices. These devices provide convenience and energy efficiency to users. Some are even targeted at the physical security of the home. For example, smart security cameras can detect unauthorized movements and smoke or heat sensors can detect potential fire accidents. However, as many recent examples have shown, this opens up a new cyber threat surface. There have been several recent examples of smart devices being hacked for nuisance or privacy violations, and also being used as bots in DDoS attacks. This work explores the detecting when a smart home has been cyber attacked. We explore the application of big data and machine learning to identify anomalous activities that can occur in a smart home environment. A Hidden Markov Model (HMM) is trained on network level sensor data, created from a test bed with multiple sensors and smart devices. The generated HMM model is shown to achieve an accuracy of 97 % in identifying potential anomalies that indicate attacks.