Smart water metering (SWM) infrastructure collects real-Time water usage data that is useful for automated billing, leak detection, and forecasting of peak periods. Cyber/physical attacks can lead to data falsification on water usage data. This paper proposes a learning approach that converts smart water meter data into a Pythagorean mean-based invariant that is highly stable under normal conditions but deviates under attacks. We show how adversaries can launch deductive or camouflage attacks in the SWM infrastructure to gain benefits and impact the water distribution utility. Then, we apply a two-Tier approach of stateless and stateful detection, reducing false alarms without significantly sacrificing the attack detection rate. We validate our approach using real-world water usage data of 92 households in Alicante, Spain for varying attack scales and strengths and prove that our method limits the impact of undetected attacks and expected time between consecutive false alarms. Our results show that even for low-strength, low-scale deductive attacks, the model limits the impact of an undetected attack to only C0.2199375 and for high-strength, low-scale camouflage attack, the impact of an undetected attack was limited to C1.434375


Computer Science


National Science Foundation, Grant DGE-1914771

Keywords and Phrases

Anomaly detection; False data injection; Smart Water Meter; Smart Water Metering Infrastructure

Document Type

Article - Conference proceedings

Document Version


File Type





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Publication Date

01 Jan 2023