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

TL;DR

This paper proposes an incentive-weighted anomaly detection method for identifying false data injection attacks on smart meter load profiles, leveraging clustering and spot price information to improve detection accuracy.

Contribution

It introduces a novel clustering-based anomaly detection approach that incorporates spot pricing incentives to better detect cyber-attacks on industrial load smart meters.

Findings

Effective detection of false data injection attacks using incentive-weighted clustering.

Reduced false positives by modeling incentive-based detection.

Incorporates spot price considerations into load profile anomaly detection.

Abstract

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