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This article considers the design and analysis of multiple moving target defenses for recognizing and isolating attacks on cyber-physical systems. We consider attackers who perform integrity attacks on some set of sensors and actuators in a control system. In such cases, it has been shown that a model aware adversary can carefully design attack vectors to bypass bad data detection and identification filters while causing damage to the control system. To counter such an attacker, we propose the moving target defense which introduces stochastic, time-varying parameters in the control system. The underlying random dynamics of the system limit an attacker's knowledge of the model and inhibit his or her ability to construct stealthy attack sequences. Moreover, the time-varying nature of the dynamics thwarts adaptive adversaries. We explore three main designs. First, we consider a hybrid system where parameters within the existing plant are switched among multiple modes. We demonstrate how such an approach can enable both the detection and identification of malicious nodes. Next, we investigate the addition of an extended system with dynamics that are coupled to the original plant but do not affect the system performance. Here, an attack on the original system will affect the authenticating subsystem and in turn be revealed by a set of sensors measuring the extended plant. Finally, we propose the use of sensor nonlinearities to enhance the effectiveness of the moving target defense. The nonlinear dynamics act to conceal normal operational behavior from an attacker who has tampered with the system state, further hindering an attacker's ability to glean information about the time-varying dynamics. In all cases mechanisms for analysis and design are proposed. Finally, we analyze attack detectability for each moving target defense by investigating expected lower bounds on the detection statistic. Our contributions are also tested via simulation.

Software rejuvenation has been proposed to guarantee safety of cyber-physical systems (CPSs) against cyber-attacks. Recent work has demonstrated how this method can be applied to more general control problems such as tracking control. Despite this progress, there are still limitations in applying software rejuvenation to real situations where the presence of persistent attacks and physical environment constraints exist. In this paper we address these issues and propose a secure recovery algorithm that can be deployed not only for recovery against persistent attacks but also in situations where physical environment constraints do not allow the system to tolerate any attack. The effectiveness of the approach is illustrated with a simulation of a quadrotor landing on the ground during recovery from a persistent attack.

Decentralized control systems are widely used in a number of situations and applications. In order for these systems to function properly and achieve their desired goals, information must be propagated between agents. However, communication between agents entails connecting to a network, potentially allowing adversaries to infiltrate the system through the network and attack multiple agents. To increase resiliency against these attacks, it is desirable for agents to operate disconnected from the network as much as possible, only communicating with other agents when it is absolutely necessary to achieve their goal or to maintain the safety of the overall system. This in turn decreases communication costs. Previous approaches to decentralized event-triggered control are mainly concerned with minimizing communication costs and therefore assume that every agent is always connected to the network with the ability to receive any information that is sent to it. In this work, we address the issue of maintaining the safety and stability of the overall system when there is no attack but some agents may be disconnected from the network and unable to receive critical information from other agents. We design an event-triggered mechanism for network connection and communication that is a function of only local information and that ensures stability for the overall system in attack-free scenarios. An algorithm describing this communication protocol is provided, and our approach is illustrated via simulation.

Cyber-physical systems (CPSs) have become targets for malicious adversaries, creating new challenges for attaining reliable CPS performance. Achieving CPS security requires tools which extend beyond what is offered in state of the art software and cyber security. In this tutorial, we consider a science of CPS security which combines tools from both cyber security and system theory to defend against adversarial behavior. We discuss realistic and intelligent attack models from an adversarial perspective and then present mechanisms for defenders to achieve resilience by recognizing and responding to such malicious behavior. The focus of this tutorial is particularly on the recognition and detection of attacks, which is the first and foremost step in achieving resilience and is a necessary prerequisite to any active response.

Efficient sound source detection and location with microphone arrays is important for many applications, including teleconferencing, surveillance, and smart rooms. While the steered response power algorithms exhibit robust performance relative to other approaches, their applications are limited by the high computational load required. For dynamic auditory scenes, the entire space must be scanned at regular intervals due to moving sound sources switching between active and inactive states. This paper introduces a time segmentation and parallelization strategy to speed up the steered response power algorithm for dynamic auditory scenes with multiple speech sources. The primary application targeted by this work is for immersive arrays and off-line auditory scene analysis with beamforming for speaker separation in cocktail party environments. Results from a Monte Carlo simulation with 6 speech sources in a mildly reverberant environment demonstrate a speed-up factor of 45, with a modest loss in the number of detections and a significant reduction in anomalous detections. Experimental results with real recordings demonstrate a performance consistent with those of the simulation.