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Energy fraud detection is a critical aspect of smart grid security and privacy preservation. Machine learning and data mining have been widely used by researchers for extensive intelligent analysis of data to recognize normal patterns of behavior such that deviations can be detected as anomalies. This paper discusses a novel application of a machine(More)
This research addresses privacy concerns in smart meter data. Smart meter data is analyzed for learning normal consumer usage of electricity. Clustering technique such as Fuzzy C-Means is used to disaggregate and learn energy consumption patterns in smart meter data. Results of experimentation with real world meter data demonstrate that it is realistically(More)
The electrical grid is transitioning to new smart grid technology. With smart meters becoming an essential feature in smart homes, concerns regarding smart meters and the vast amount of consumer data that it captures are on the rise. While access to this fine-grained energy consumption data captured by smart meters can potentially violate consumer privacy,(More)
In order to meet the cybersecurity workforce demand, it is important to raise cybersecurity interest among the youth. Just like ACM programming competitions, Capture the Flag (CTF) competitions allow students to learn cybersecurity skills in a fun and engaging way. It is an effective platform to increase students' interest in cybersecurity and prepare them(More)