Author: Weixian Liao
Publisher:
Published: 2018
Total Pages: 106
ISBN-13:
DOWNLOAD EBOOK →
Cyber-physical systems (CPS) are complex networked systems that consist of cyber components for computation and communication, closely interacting with physical components such as sensors and actuators. Recent years have witnessed exponential growth in the development of cyber-physical systems. As being the basis for emerging and future smart service, they play an increasingly important role in critical infrastructure, government, everyday lives, etc. On the other hand, the integration of CPS brings more threats that may result in catastrophic consequences for the society. In this dissertation, we aim to address the security and privacy issues in cyber-physical systems and internet of things (IoT) devices. Our contributions in this dissertation are two-fold. Firstly, we study the security issues in power grid, which is one of the most critical infrastructures in the world. Security of the power grid has gained enormous attention for decades. Cascading failure, one of the most serious problems in power systems, can result in catastrophic impacts such as massive blackouts. More importantly, it can be taken advantage by malicious attackers to launch physical or cyber attacks on the power grid. However, due to the expansive geographical coverage and complex interdependencies among system components, protecting the power grid is data and computing intensive and hence extremely challenging. We investigate cascading failure attack (CFA) from a stochastic game perspective. In particular, we formulate a zero-sum stochastic attack/defense game for CFA while considering the attack/defense costs, limited budgets, diverse load shedding costs, and dynamic states in the system. Then, we develop a Q-CFA learning algorithm that works efficiently in a large system without any a-priori information. We also formally prove that the proposed algorithm can converge and achieve Nash equilibrium. Simulation results validate the efficacy and efficiency of the proposed scheme by comparisons with the state-of-the-art approaches.Secondly, we focus on secure outsourcing of large-scale fundamental problems in the cloud. Conducting such large-scale data analytics in a timely manner requires a large amount of computing resources, which may not be available for individuals and small companies in practice. By outsourcing their computations to the cloud, clients can solve such problems in a cost-effective way. However, confidential data stored at the cloud is vulnerable to cyber attacks, and thus needs to be protected. Previous works employ cryptographic techniques like homomorphic encryption, which significantly increase the computational complexity of solving a large-scale problem at the cloud and is impractical for big data applications. We present an efficient secure outsourcing scheme for convex separable programming problems (CSPs). In particular, we first develop efficient matrix and vector transformation schemes only based on arithmetic operations that are computationally indistinguishable both in value and in structure under a chosen-plaintext attack (CPA). Then, we design a secure outsourcing scheme in which the client and the cloud collaboratively solve the transformed problems. The client can efficiently verify the correctness of returned results to prevent any malicious behavior of the cloud. Theoretical correctness and privacy analysis together show that the proposed scheme obtains optimal results and that the cloud cannot learn private information from the client's concealed data. We conduct extensive simulations on Amazon Elastic Cloud Computing (EC2) platform and find that our proposed scheme provides significant time savings to the clients.
