On the Use of Reinforcement Learning for Attacking and Defending Load Frequency Control

The electric grid is an attractive target for cyberattackers given its critical nature in society. With the increasing sophistication of cyberattacks, effective grid defense will benefit from proactively identifying vulnerabilities and attack strategies. We develop a deep reinforcement learning-based method that recognizes vulnerabilities in load frequency control, an essential process that maintains grid security and reliability. We demonstrate how our method can synthesize a variety of attacks involving false data injection and load switching, while specifying the attack and threat models - providing insight into potential attack strategies and impact. We discuss how our approach can be employed for testing electric grid vulnerabilities. Moreover our method can be employed to generate data to inform the design of defense strategies and develop attack detection methods. For this, we design and compare a (deep learning-based) supervised attack detector with an unsupervised anomaly detector to highlight the benefits of developing defense strategies based on identified attack strategies

A Novel Distributed Privacy Paradigm for Visual Sensor Networks Based on Sharing Dynamical Systems

Visual sensor networks (VSNs) provide surveillance images/video which must be protected from eavesdropping and tampering en route to the base station. In the spirit of sensor networks, we propose a novel paradigm for securing privacy and confidentiality in a distributed manner. Our paradigm is based on the control of dynamical systems, which we show is well suited for VSNs due to its low complexity in terms of processing and communication, while achieving robustness to both unintentional noise and intentional attacks as long as a small subset of nodes are affected. We also present a low complexity algorithm called TANGRAM to demonstrate the feasibility of applying our novel paradigm to VSNs. We present and discuss simulation results of TANGRAM

Vector Field Driven Design for Lightweight Signal Processing and Control Schemes for Autonomous Robotic Navigation

We address the problem of realizing lightweight signal processing and control architectures for agents in multirobot systems. Motivated by the promising results of neuromorphic engineering which suggest the efficacy of analog as an implementation substrate for computation, we present the design of an analog-amenable signal processing scheme. We use control and dynamical systems theory both as a description language and as a synthesis toolset to rigorously develop our computational machinery; these mechanisms are mated with structural insights from behavior-based robotics to compose overall algorithmic architectures. Our perspective is that robotic behaviors consist of actions taken by an agent to cause its sensory perception of the environment to evolve in a desired manner. To provide an intuitive aid for designing these behavioral primitives we present a novel visual tool, inspired vector field design, that helps the designer to exploit the dynamics of the environment. We present simulation results and animation videos to demonstrate the signal processing and control architecture in action

Enhancing Power Quality Event Classification with AI Transformer Models

Recently, there has been a growing interest in utilizing machine learning for accurate classification of power quality events (PQEs). However, most of these studies are performed assuming an ideal situation, while in reality, we can have measurement noise, DC offset, and variations in the voltage signal's amplitude and frequency. Building on the prior PQE classification works using deep learning, this paper proposes a deep-learning framework that leverages attention-enabled Transformers as a tool to accurately classify PQEs under the aforementioned considerations. The proposed framework can operate directly on the voltage signals with no need for a separate feature extraction or calculation phase. Our results show that the proposed framework outperforms recently proposed learning-based techniques. It can accurately classify PQEs under the aforementioned conditions with an accuracy varying between 99.81%$-$91.43% depending on the signal-to-noise ratio, DC offsets, and variations in the signal amplitude and frequency.Comment: Accepted in the IEEE Power and Energy Society General Meeting, 202

Collusion-resistant fingerprinting for multimedia in a broadcast channel environment

Digital fingerprinting is a method by which a copyright owner can uniquely embed a buyer-dependent, inconspicuous serial number (representing the fingerprint) into every copy of digital data that is legally sold. The buyer of a legal copy is then deterred from distributing further copies, because the unique fingerprint can be used to trace back the origin of the piracy. The major challenge in fingerprinting is collusion, an attack in which a coalition of pirates compare several of their uniquely fingerprinted copies for the purpose of detecting and removing the fingerprints. The objectives of this work are two-fold. First, we investigate the need for robustness against large coalitions of pirates by introducing the concept of a malicious distributor that has been overlooked in prior work. A novel fingerprinting code that has superior codeword length in comparison to existing work under this novel malicious distributor scenario is developed. In addition, ideas presented in the proposed fingerprinting design can easily be applied to existing fingerprinting schemes, making them more robust to collusion attacks. Second, a new framework termed Joint Source Fingerprinting that integrates the processes of watermarking and codebook design is introduced. The need for this new paradigm is motivated by the fact that existing fingerprinting methods result in a perceptually undistorted multimedia after collusion is applied. In contrast, the new paradigm equates the process of collusion amongst a coalition of pirates, to degrading the perceptual characteristics, and hence commercial value of the multimedia in question. Thus by enforcing that the process of collusion diminishes the commercial value of the content, the pirates are deterred from attacking the fingerprints. A fingerprinting algorithm for video as well as an efficient means of broadcasting or distributing fingerprinted video is also presented. Simulation results are provided to verify our theoretical and empirical observations

A Framework for Modeling Cyber-Physical Switching Attacks in Smart Grid

Security issues in cyber-physical systems are of paramount importance due to the often safety- critical nature of its associated applications. A rst step in understanding how to protect such systems requires an understanding of emergent weaknesses, in part, due to the cyber-physical coupling. In this paper, we present a framework that models a class of cyber-physical switching vulnerabilities in smart grid systems. Variable structure system theory is employed to effectively characterize the cyber-physical interaction of the smart grid and demonstrate how existence of the switching vulnerability is dependent on the local structure of the power grid. We identify and demonstrate how through successful cyber intrusion and local knowledge of the grid an opponent can compute and apply a coordinated switching sequence to a circuit breaker to disrupt operation within a short interval of time. We illustrate the utility of the attack approach empirically on the Western Electricity Coordinating Council three-machine, nine-bus system under both model error and partial state information.The open access fee for this work was funded through the Texas A&M University Open Access to Knowledge (OAK) Fund

Implications for high capacity data hiding in the presence of lossy compression

We derive capacity bounds for watermarking and data hiding in the presence of JND perceptual coding for a class of techniques that do not suffer from host signal interference. By modeling the lossy compression distortions on the hidden data using non-Gaussian statistics, we demonstrate that binary antipodal channel codes achieve capacity. It is shown that the data hiding capacity is at most equal to the loss in storage efficiency bit rate if watermarking and quantization for lossy compression occur in the same domain.