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Spot pricing is often suggested as a method of increasing demand-sideflexibility in electrical power load. However, few works have considered thevulnerability of spot pricing to financial fraud via false data injection (FDI)style attacks. In this paper, we consider attacks which aim to alter theconsumer load profile to exploit intraday price dips. We examine an anomalydetection protocol for cyber-attacks that seek to leverage spot prices forfinancial gain. In this way we outline a methodology for detecting attacks onindustrial load smart meters. We first create a feature clustering model of theunderlying business, segregated by business type. We then use these clusters tocreate an incentive-weighted anomaly detection protocol for false data attacksagainst load profiles. This clustering-based methodology incorporates both theload profile and spot pricing considerations for the detection of injected loadprofiles. To reduce false positives, we model incentive-based detection, whichincludes knowledge of spot prices, into the anomaly tracking, enabling themethodology to account for changes in the load profile which are unlikely to beattacks.

Incentive-weighted Anomaly Detection for False Data Injection Attacks Against Smart Meter Load Profiles