On Security Indices for State Estimators in Power Networks

Abstract

In this paper, we study stealthy false-data attacks against state estimators in power networks. The focus is on applications in SCADA (Supervisory Control and Data Acquisition) systems where measurement data is corrupted by a malicious attacker. We introduce two security indices for the state estimators. The indices quantify the least effort needed to achieve attack goals while avoiding bad-data alarms in the power network control center (stealthy attacks). The indices depend on the physical topology of the power network and the available measurements, and can help the system operator to identify sparse data manipulation patterns. This information can be used to strengthen the security by allocating encryption devices, for example. The analysis is also complemented with a convex optimization framework that can be used to evaluate more complex attacks taking model deviations and multiple attack goals into account. The security indices are finally computed in an example. It is seen that a large measurement redundancy forces the attacker to use large magnitudes in the data manipulation pattern, but that the pattern still can be relatively sparse.