The consensus problem is relevant to different areas ranging from biology, social psychology, and physics to power systems and robotics. Two crucial aspects of the design of a consensus system are the implementation issues that arise in densely connected networks and the presence of malicious agents that try to cause a deviation from a synchronization state. In this article, we introduce a formulation to design the topology of a consensus network to improve its resilience to attacks while remaining sparse and consistent with the a priori structural relations between the agents. Through mathematical analysis and simulations on artificial and real-world cases, we show the benefits and usefulness of using this strategy to design resilient and structurally sparse consensus networks.

This article proposes a novel moving target defense (MTD) strategy that leverages the versatility of the Internet of Things (IoT) networks to enhance the security of cyber–physical systems (CPSs) by replicating relevant sensory and control signals. The replicated data are randomly selected and transmitted to create two layers of uncertainties that reduce the ability of adversaries to launch successful cyberattacks, without affecting the performance of the system in a normal operation. The theoretical foundations of designing the IoT network and optimal allocation of replicas per signal are developed for linear-time-invariant systems, and fundamental limits of uncertainties introduced by the framework are calculated. The orchestration of the layers and applications integrated in the proposed framework is demonstrated in experimental implementation on a real-time water system over a WiFi network, adopting a data-centric architecture. The implementation results demonstrate that the proposed framework considerably limits the impact of false-data-injection attacks, while decreasing the ability of adversaries to learn details about the physical system operation.

This paper takes a first look at the potential consequences of cyberattacks against structural control systems. We design algorithms and implement them in a testbed and on well-known benchmark models for buildings and bridges. Our results show that attacks to structures equipped with semi-active and active vibration control systems can let the attacker oscillate the building or bridge at the resonance frequency, effectively generating threats to the structure and the people using it. We also implement and test the effectiveness of attack-detection systems.

Existing methods for wireless-sensor network (WSN) topology optimization employ simplifying assumptions of a fixed communication radius between network nodes, which is ill-suited for IoT networks deployed in complex terrain. This article proposes a data-driven approach to WSN topology optimization, employing a Bayesian link classifier trained on LIDAR-derived terrain characteristics and anin-situsurvey of link quality. The classifier is trained to predict where good network links (packet-delivery ratio, PDR>0.5) are likely to form in a region given complex terrain attributes. Then, given numerous candidate wireless node placements throughout the domain, the classifier is used to construct an undirected weighted graph of the potential connectivity across the domain. Edge weights in the connectivity graph are proportional to the probability of forming a good link between the nodes. A novel modified cycle-union (MCyU) algorithm for generating a 2-vertex-connected, Steiner minimal network is then applied to the undirected weighted graph of potential network element placements. This ensures a survivable network design, while maximizing the probability of good links within the final network. The total number and spatial distribution of network elements produced by the algorithm is compared to an existing WSN, deployed for environmental monitoring in remote regions. In addition, the MCyU algorithm has been evaluated in three graph test cases to compare with state-of-the-art solutions, where MCyU outperforms in terms of weight minimization and computation time.

Identifying the anomaly location and type (fault or attack) is of paramount importance for enhancing cyber-physical situational awareness, and taking informed and effective mitigation actions in power distribution systems with an increasing number of attack points in distributed and renewable energy sources. This article proposes the fault and attack location and classification (FALCON) system to classify and locate cyber and physical anomalies, including false data injection attacks on protection devices, replay attacks on communication networks, and physical faults on distribution lines. The proposed system takes as input the transient short-circuit current and voltage measured by protection relays, the relays command status as well as the fault alarm from fault indicators, which is fed into a deep neural network that classifies and identifies the location of the fault and attacks in the distribution system. Numerical studies demonstrate FALCON's capability to classify and locate multiple cyber and physical anomalies with more than 98% accuracy, even when multiple devices are simultaneously compromised. Furthermore, the impact of different sets of input data is explored to highlight the importance of fault indicators, fault voltage data, and data collected from the RES relays.

In the past decade we have seen an active research community proposing attacks and defenses to Cyber-Physical Systems (CPS). Most of these attacks and defenses have been heuristic in nature, limiting the attacker to a set of predefined operations, and proposing defenses with unclear security guarantees. In this paper, we propose a generic adversary model that can capture any type of attack (our attacker is not constrained to follow specific attacks such as replay, delay, or bias) and use it to design security mechanisms with provable security guarantees. In particular, we propose a new secure design paradigm we call DARIA: Designing Actuators to Resist arbItrary Attacks. The main idea behind DARIA is the design of physical limits to actuators in order to prevent attackers from arbitrarily manipulating the system, irrespective of their point of attack (sensors or actuators) or the specific attack algorithm (bias, replay, delays, etc.). As far as we are aware, we are the first research team to propose the design of physical limits to actuators in a control loop in order to keep the system secure against attacks. We demonstrate the generality of our proposal on simulations of vehicular platooning and industrial processes.

While attacks on information systems have for most practical purposes binary outcomes (information was manipulated/eavesdropped, or not), attacks manipulating the sensor or control signals of Industrial Control Systems (ICS) can be tuned by the attacker to cause a continuous spectrum in damages. Attackers that want to remain undetected can attempt to hide their manipulation of the system by following closely the expected behavior of the system, while injecting just enough false information at each time step to achieve their goals. In this work, we study if attack-detection can limit the impact of such stealthy attacks. We start with a comprehensive review of related work on attack detection schemes in the security and control systems community. We then show that many of those works use detection schemes that are not limiting the impact of stealthy attacks. We propose a new metric to measure the impact of stealthy attacks and how they relate to our selection on an upper bound on false alarms. We finally show that the impact of such attacks can be mitigated in several cases by the proper combination and configuration of detection schemes. We demonstrate the effectiveness of our algorithms through simulations and experiments using real ICS testbeds and real ICS systems.