Visible to the public Anomaly Detection Models for Smart Home Security

TitleAnomaly Detection Models for Smart Home Security
Publication TypeConference Paper
Year of Publication2019
AuthorsRamapatruni, S., Narayanan, S. N., Mittal, S., Joshi, A., Joshi, K.
Conference Name2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS)
Keywordsanomalous activities, anomaly detection, anomaly detection models, Big Data, compositionality, computer network security, cyber threat surface, Data collection, data privacy, DDoS Attacks, Energy efficiency, fire accidents, hidden Markov model, Hidden Markov models, HMM model, home automation, Intelligent Data and Security, Intelligent Data Security, Intelligent sensors, learning (artificial intelligence), machine learning, Monitoring, multiple sensors, network level sensor data, privacy violations, pubcrawl, Resiliency, Scalability, security, sensor fusion, smart devices, smart home, smart home environment, smart home security, Smart homes, smart homes devices, smart security cameras, smoke sensors, unauthorized movements
AbstractRecent years have seen significant growth in the adoption of smart homes devices. These devices provide convenience, security, and energy efficiency to users. For example, smart security cameras can detect unauthorized movements, and smoke sensors can detect potential fire accidents. However, many recent examples have shown that they open up a new cyber threat surface. There have been several recent examples of smart devices being hacked for privacy violations and also misused so as to perform DDoS attacks. In this paper, 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. We present our approach to build this model and compare with other techniques available in the literature.
Citation Keyramapatruni_anomaly_2019