Synthetic Data for Smart Meter Attack Detection

This dataset contains synthetic smart meter data with simulated cyber attacks, designed to support research in anomaly detection, cybersecurity, and energy consumption analysis. The dataset is based on 159 users from the Smart Meters in London dataset, selected for their regular consumption patterns. It spans one year with half-hourly data, totaling 17,520 consumption records per user. The first 11 months remain unaltered, and the attacks are introduced in the final month. Seven distinct attack strategies have been applied, resulting in a total of 1,272 user datasets, including both the original and attacked versions. The attacks are indexed from 0 to 7 and include: (0) reduction to the historical minimum, (1) a one-week consumption reduction within the month by a fixed percentage, (2) progressive reduction over time, (3) cut-off at a predefined threshold preventing consumption from exceeding a set limit, (4) progressive reduction during peak hours, (5) progressive reduction during peak hours with redistribution to off-peak hours, (6) swapping consumption between peak and off-peak hours, and (7) unaltered consumption. This dataset may be useful for testing anomaly detection methods and exploring different strategies for identifying attacks in smart meter data.