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Design of load forecast systems resilient against cyber-attacks. (English) Zbl 1446.93053
Alpcan, Tansu (ed.) et al., Decision and game theory for security. 10th international conference, GameSec 2019, Stockholm, Sweden, October 30 – November 1, 2019. Proceedings. Cham: Springer. Lect. Notes Comput. Sci. 11836, 1-20 (2019).
Summary: Load forecast systems play a fundamental role the operation in power systems, because they reduce uncertainties about the system’s future operation. An increasing demand for precise forecasts motivates the design of complex models that use information from different sources, such as smart appliances. However, untrusted sources can introduce vulnerabilities in the system. For example, an adversary may compromise the sensor measurements to induce errors in the forecast. In this work, we assess the vulnerabilities of load forecast systems based on neural networks and propose a defense mechanism to construct resilient forecasters.
We model the strategic interaction between a defender and an attacker as a Stackelberg game, where the defender decides first the prediction scheme and the attacker chooses afterwards its attack strategy. Here, the defender selects randomly the sensor measurements to use in the forecast, while the adversary calculates a bias to inject in some sensors. We find an approximate equilibrium of the game and implement the defense mechanism using an ensemble of predictors, which introduces uncertainties that mitigate the attack’s impact. We evaluate our defense approach training forecasters using data from an electric distribution system simulated in GridLAB-D.
For the entire collection see [Zbl 1428.68003].
MSC:
93C83 Control/observation systems involving computers (process control, etc.)
93B70 Networked control
91A65 Hierarchical games (including Stackelberg games)
91A80 Applications of game theory
68M25 Computer security
Software:
Keras; SciPy
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References:
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