A Framework for Learning Event Sequences and Explaining Detected Anomalies in a Smart Home Environment

Abstract

This paper presents a framework for learning event sequences for anomaly detection in a smart home environment. It addresses environment conditions, device grouping, system performance and explainability of anomalies. Our method models user behavior as sequences of events, triggered by interaction of the home residents with the Internet of Things (IoT) devices. Based on a given set of recorded event sequences, the system can learn the habitual behavior of the residents. An anomaly is described as deviation from that normal behavior, previously learned by the system. One key feature of our framework is the explainability of detected anomalies, which is implemented through a simple rule analysis.

Optimizing Anomaly Detection in Smart Grids with Modified FDA and Dilated GRU-based Adaptive Residual RNN

“…But, the enhancements of smart technologies face different cyber security problems due to the presence of insecurity and legacy systems like Industrial Control Systems (ICS); new attacks are developed using smart technologies and the vulnerability nature of Internet Protocols (IP) [16]. Smart grid technologies provide more advancement to individuals but face different technical complications like energy thefts, equipment failure, cyberattacks, outages, and faulty equipment that produce a minimal effectualness rate [17]. In the classical grid technologies, abnormal utilization patterns are detected by general observations in onsite inspections and utility bill analysis but, this procedure consumes more time and is ineffectual [18].…”