Date of Award

5-2014

Embargo Period

6-30-2014

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Electrical and Computer Engineering

Advisor(s)

Bruno Sinopoli

Abstract

A SCADA system employing the distributed networks of sensors and actuators that interact with the physical environment is vulnerable to attacks that target the interface between the cyber and physical subsystems. An attack that hijacks the sensors in an attempt to provide false readings to the controller (for example, the Stuxnet worm that targeted Iran’s nuclear centrifuges) can be used to feign normal system operation for the control system, while the attacker can hijack the actuators to send the system beyond its safety range. This thesis extends the results of a previously proposed method. The original method proposed addition of a randomized “watermarking” signal and checking for the presence of this signal and its effects in the received sensor measurements. Since the control inputs traverse the cyberphysical boundary and make their effects apparent in the sensor measurements, they are employed to carry this watermarking signal through to the system and back to the SCADA controller. The sensor measurements are compared to the expected measurements (calculated using a suitably delayed model of the system within the controller). This methodology is based on using the statistics of the linear system and its controller. The inclusion of a randomized signal on the control inputs induces an increase in the performance cost of the physical system. This thesis proposes a method of optimization of the watermarking signal based on the trade-off between this performance cost and the attack detection rate, by leveraging the distribution the watermarking signal over multiple inputs and multiple outputs. It is further proved that regardless of the number of inputs and outputs in the system, only one watermarking signal needs to be generated. This optimization, and its necessity in improving the effectiveness of the detector without detriment to the performance cost, are demonstrated on a simulated chemical plant. The thesis also proposes another methodology that does not rely on these statistics, but is instead based on calculating the correlation between the received sensor measurements and the expected measurements accrued from the model inside the controller.

Generalizing the form of attack even further to attacks that target the integrity of the data sent to the actuators and received from the sensors, this thesis demonstrates the effect of such integrity attack on electricity market operations, where the attacker successfully uses a vulnerability in the Global Position System to break synchronicity among dispersed phasor measurements to gain a competitive advantage over other bidders in the electricity market. In an effort to make state estimation robust against integrity attacks, the sensors and states are modeled as binary variables. Sensor networks use binary measurements and state estimations for several reasons, including communication and processing overheads. Such a state estimator is vulnerable to attackers that can hijack a subset of the sensors in an effort to change the state estimate. This thesis proposes a method for designing the estimators using the concept of invariant sets. This methodology relies on identifying the sets of measurement vectors for which no amount of manipulation by the attacker can change estimate, and maximizing the probability that the sensor measurement vector lies in this set. Although the problem of finding the best possible invariant sets for a general set of sensors has double-exponential complexity, by using some simplifications on the types of sensors, this can be reduced significantly. For the problem that employs all sensors of the same type, this method reduces to a linear search. For sensors that can be classified into two types, this complexity reduces to a search over a two-dimensional space, which is still tractable. Further increase in the confidence of the estimate can be achieved by considering the correlation between the sensor measurements.

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