TinySec: a link layer security architecture for wireless sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Survey Paper: A survey on the communication architectures in smart grid
Computer Networks: The International Journal of Computer and Telecommunications Networking
A framework for short-term activity-aware load forecasting
Joint Proceedings of the Workshop on AI Problems and Approaches for Intelligent Environments and Workshop on Semantic Cities
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A machine learning based model to monitor the smart grid for any suspicious activity or malicious attack is presented in this paper. The model is designed to detect and classify anomalies in the sensory data and is helpful in ensuring the security and stability of the smart grid. The model relies on the real time data collected using wireless sensor networks as an overlay network on the power distribution grid. The overlay network of wireless sensors/devices uses a cluster topology at each tower to collect local information about the tower, and is further augmented by the linear chain topology to connect each tower to the base station (usually at the substation). Preliminary results show that detection mechanism is promising and is able to detect the occurrence of any anomalous event that may cause threat to the smart grid.