Biblio

Cyber-physical systems (CPS) data privacy protection during sharing, aggregating, and publishing is a challenging problem. Several privacy protection mechanisms have been developed in the literature to protect sensitive data from adversarial analysis and eliminate the risk of re-identifying the original properties of shared data. However, most of the existing solutions have drawbacks, such as (i) lack of a proper vulnerability characterization model to accurately identify where privacy is needed, (ii) ignoring data providers privacy preference, (iii) using uniform privacy protection which may create inadequate privacy for some provider while over-protecting others, and (iv) lack of a comprehensive privacy quantification model assuring data privacy-preservation. To address these issues, we propose a personalized privacy preference framework by characterizing and quantifying the CPS vulnerabilities as well as ensuring privacy. First, we introduce a Standard Vulnerability Profiling Library (SVPL) by arranging the nodes of an energy-CPS from maximum to minimum vulnerable based on their privacy loss. Based on this model, we present our personalized privacy framework (PDP) in which Laplace noise is added based on the individual node's selected privacy preferences. Finally, combining these two proposed methods, we demonstrate that our privacy characterization and quantification model can attain better privacy preservation by eliminating the trade-off between privacy, utility, and risk of losing information.

In this paper, we present the concept of boosting the resiliency of optimization-based observers for cyber-physical systems (CPS) using auxiliary sources of information. Due to the tight coupling of physics, communication and computation, a malicious agent can exploit multiple inherent vulnerabilities in order to inject stealthy signals into the measurement process. The problem setting considers the scenario in which an attacker strategically corrupts portions of the data in order to force wrong state estimates which could have catastrophic consequences. The goal of the proposed observer is to compute the true states in-spite of the adversarial corruption. In the formulation, we use a measurement prior distribution generated by the auxiliary model to refine the feasible region of a traditional compressive sensing-based regression problem. A constrained optimization-based observer is developed using l1-minimization scheme. Numerical experiments show that the solution of the resulting problem recovers the true states of the system. The developed algorithm is evaluated through a numerical simulation example of the IEEE 14-bus system.

Smart grid aims to improve control and monitoring routines to ensure reliable and efficient supply of electricity. The rapid advancements in information and communication technologies of Supervisory Control And Data Acquisition (SCADA) networks, however, have resulted in complex cyber physical systems. This added complexity has broadened the attack surface of power-related applications, amplifying their susceptibility to cyber threats. A particular class of system integrity attacks against the smart grid is False Data Injection (FDI). In a successful FDI attack, an adversary compromises the readings of grid sensors in such a way that errors introduced into estimates of state variables remain undetected. This paper presents an end-to-end case study of how to instantiate real FDI attacks to the Alternating Current (AC) –nonlinear– State Estimation (SE) process. The attack is realized through firmware modifications of the microprocessor-based remote terminal systems, falsifying the data transmitted to the SE routine, and proceeds regardless of perfect or imperfect knowledge of the current system state. The case study concludes with an investigation of an attack on the IEEE 14 bus system using load data from the New York Independent System Operator (NYISO).