Biblio

The Internet of Things (IoT) is a ubiquitous system connecting many different devices - the things - which can be accessed from the distance. The cyber-physical systems (CPSs) monitor and control the things from the distance. As a result, the concepts of dependability and security get deeply intertwined. The increasing level of dynamicity, heterogeneity, and complexity adds to the system's vulnerability, and challenges its ability to react to faults. This paper summarizes the state of the art of existing work on anomaly detection, fault-tolerance, and self-healing, and adds a number of other methods applicable to achieve resilience in an IoT. We particularly focus on non-intrusive methods ensuring data integrity in the network. Furthermore, this paper presents the main challenges in building a resilient IoT for the CPS, which is crucial in the era of smart CPS with enhanced connectivity (an excellent example of such a system is connected autonomous vehicles). It further summarizes our solutions, work-in-progress and future work to this topic to enable ``Trustworthy IoT for CPS''. Finally, this framework is illustrated on a selected use case: a smart sensor infrastructure in the transport domain.

In this paper, a general model for cyber-physical systems (CPSs), that captures the diffusion of attacks from the cyber layer to the physical system, is studied. In particular, a game-theoretic approach is proposed to analyze the interactions between one defender and one attacker over a CPS. In this game, the attacker launches cyber attacks on a number of cyber components of the CPS to maximize the potential harm to the physical system while the system operator chooses to defend a number of cyber nodes to thwart the attacks and minimize potential damage to the physical side. The proposed game explicitly accounts for the fact that both attacker and defender can have different computational capabilities and disparate levels of knowledge of the system. To capture such bounded rationality of attacker and defender, a novel approach inspired from the behavioral framework of cognitive hierarchy theory is developed. In this framework, the defender is assumed to be faced with an attacker that can have different possible thinking levels reflecting its knowledge of the system and computational capabilities. To solve the game, the optimal strategies of each attacker type are characterized and the optimal response of the defender facing these different types is computed. This general approach is applied to smart grid security considering wide area protection with energy markets implications. Numerical results show that a deviation from the Nash equilibrium strategy is beneficial when the bounded rationality of the attacker is considered. Moreover, the results show that the defender's incentive to deviate from the Nash equilibrium decreases when faced with an attacker that has high computational ability.

The smart electricity grids have been evolving to a more complex cyber-physical ecosystem of infrastructures with integrated communication networks, new carbon-free sources of power generation, advanced monitoring and control systems, and a myriad of emerging modern physical hardware technologies. With the unprecedented complexity and heterogeneity in dynamic smart grid networks comes additional vulnerability to emerging threats such as cyber attacks. Rapid development and deployment of advanced network monitoring and communication systems on one hand, and the growing interdependence of the electric power grids to a multitude of lifeline critical infrastructures on the other, calls for holistic defense strategies to safeguard the power grids against cyber adversaries. In order to improve the resilience of the power grid against adversarial attacks and cyber intrusions, advancements should be sought on detection techniques, protection plans, and mitigation practices in all electricity generation, transmission, and distribution sectors. This survey discusses such major directions and recent advancements from a lens of different detection techniques, equipment protection plans, and mitigation strategies to enhance the energy delivery infrastructure resilience and operational endurance against cyber attacks. This undertaking is essential since even modest improvements in resilience of the power grid against cyber threats could lead to sizeable monetary savings and an enriched overall social welfare.

This paper is about the estimation of the cyber-resilience of CPS. We define two new resilience estimation metrics: k-steerability and l-monitorability. They aim at assisting designers to evaluate and increase the cyber-resilience of CPS when facing stealthy attacks. The k-steerability metric reflects the ability of a controller to act on individual plant state variables when, at least, k different groups of functionally diverse input signals may be processed. The l-monitorability metric indicates the ability of a controller to monitor individual plant state variables with l different groups of functionally diverse outputs. Paired together, the metrics lead to CPS reaching (k,l)-resilience. When k and l are both greater than one, a CPS can absorb and adapt to control-theoretic attacks manipulating input and output signals. We also relate the parameters k and l to the recoverability of a system. We define recoverability strategies to mitigate the impact of perpetrated attacks. We show that the values of k and l can be augmented by combining redundancy and diversity in hardware and software, in order to apply the moving target paradigm. We validate the approach via simulation and numeric results.

Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and information or cyber worlds. Their deployment in critical infrastructure have demonstrated a potential to transform the world. However, harnessing this potential is limited by their critical nature and the far reaching effects of cyber attacks on human, infrastructure and the environment. An attraction for cyber concerns in CPS rises from the process of sending information from sensors to actuators over the wireless communication medium, thereby widening the attack surface. Traditionally, CPS security has been investigated from the perspective of preventing intruders from gaining access to the system using cryptography and other access control techniques. Most research work have therefore focused on the detection of attacks in CPS. However, in a world of increasing adversaries, it is becoming more difficult to totally prevent CPS from adversarial attacks, hence the need to focus on making CPS resilient. Resilient CPS are designed to withstand disruptions and remain functional despite the operation of adversaries. One of the dominant methodologies explored for building resilient CPS is dependent on machine learning (ML) algorithms. However, rising from recent research in adversarial ML, we posit that ML algorithms for securing CPS must themselves be resilient. This article is therefore aimed at comprehensively surveying the interactions between resilient CPS using ML and resilient ML when applied in CPS. The paper concludes with a number of research trends and promising future research directions. Furthermore, with this article, readers can have a thorough understanding of recent advances on ML-based security and securing ML for CPS and countermeasures, as well as research trends in this active research area.

Modern vehicles equipped with a large number of electronic components, sensors, actuators, and extensive connectivity, are the classical example of cyber-physical systems (CPS). Communication as an integral part of the CPS has enabled and offered many value-added services for vehicular networks. The communication mechanism helps to share contents with all vehicular network nodes and the surrounding environment, e.g., vehicles, traffic lights, and smart road signs, to efficiently take informed and smart decisions. Thus, it opens the doors to many security threats and vulnerabilities. Traditional TCP/IP-based communication paradigm focuses on securing the communication channel instead of the contents that travel through the network. Nevertheless, for content-centered application, content security is more important than communication channel security. To this end, named data networking (NDN) is one of the future Internet architectures that puts the contents at the center of communication and offers embedded content security. In this paper, we first identify the cyberattacks and security challenges faced by the vehicular CPS (VCPS). Next, we propose the NDN-based cyber-resilient, the layered and modular architecture for VCPS. The architecture includes the NDN's forwarding daemon, threat aversion, detection, and resilience components. A detailed discussion about the functionality of each component is also presented. Furthermore, we discuss the future challenges faced by the integration of NDN with VCPS to realize NDN-based VCPS.

Highly distributed self-organizing CPS exhibit coordination schemata and communication requirements which are similar to structured overlay networks. To determine the resilience of such overlays, we analyze the connectivity of Kademlia, which has been successfully deployed in multiple applications with several thousands of nodes, e.g., BitTorrent. We measure the network connectivity within extensive simulations for different network configurations and present selected results.

The information network plays a crucial role in the stability of infrastructure CPS. The adoption of measurements and networked control technologies provide timely measurements that can be used to design control strategies for the stability of the energy network during a failure or a fault. However, these technologies have also significantly increased the exposure to novel security threats and risks. This tutorial will present case studies for methodological security and resiliency assessment for infrastructure cyber-physical systems on the DETER networking and cyber security testbed.

Cyber-Physical Systems (CPSs) are engineered systems seamlessly integrating computational algorithms and physical components. CPS advances offer numerous benefits to domains such as health, transportation, smart homes and manufacturing. Despite these advances, the overall cybersecurity posture of CPS devices remains unclear. In this paper, we provide knowledge on how to improve CPS resiliency by evaluating and comparing the accuracy, and scalability of two popular vulnerability assessment tools, Nessus and OpenVAS. Accuracy and suitability are evaluated with a diverse sample of pre-defined vulnerabilities in Industrial Control Systems (ICS), smart cars, smart home devices, and a smart water system. Scalability is evaluated using a large-scale vulnerability assessment of 1,000 Internet accessible CPS devices found on Shodan, the search engine for the Internet of Things (IoT). Assessment results indicate several CPS devices from major vendors suffer from critical vulnerabilities such as unsupported operating systems, OpenSSH vulnerabilities allowing unauthorized information disclosure, and PHP vulnerabilities susceptible to denial of service attacks.

In this article, we describe a neighbour disjoint multipath (NDM) scheme that is shown to be more resilient amidst node or link failures compared to the two well-known node disjoint and edge disjoint multipath techniques. A centralised NDM was first conceptualised in our initial published work utilising the spatial diversity among multiple paths to ensure robustness against localised poor channel quality or node failures. Here, we further introduce a distributed version of our NDM algorithm adapting to the low-power and lossy network (LLN) characteristics. We implement our distributed NDM algorithm in Contiki OS on top of LOADng—a lightweight On-demand Ad hoc Distance Vector Routing protocol. We compare this implementation's performance with a standard IPv6 Routing Protocol for Low power and Lossy Networks (RPL), and also with basic LOADng, running in the Cooja simulator. Standard performance metrics such as packet delivery ratio, end-to-end latency, overhead and average routing table size are identified for the comparison. The results and observations are provided considering a few different application traffic patterns, which serve to quantify the improvements in robustness arising from NDM. The results are confirmed by experiments using a public sensor network testbed with over 100 nodes.

While power grid systems benefit from utilizing communication network through networked control and protection, the addition of communication exposes the power system to new security vulnerabilities and potential attacks. To mitigate these attacks, such as denial of service, intrusion detection systems (IDS) are often employed. In this paper we investigate the relationship of IDS accuracy performance to the stability of power systems via its impact on communication latency. Several IDS machine learning algorithms are implemented on the NSL-KDD dataset to obtain accuracy performance, and a mathematical model for computing the latency when incorporating IDS detection information during network routing is introduced. Simulation results on the New England 39-bus power system suggest that during a cyber-physical attack, a practical IDS can achieve similar stability as an ideal IDS with perfect detection. In addition, false positive rate has been found to have a larger impact than false negative rate under the simulation conditions studied. These observations can contribute to the design requirements of future embedded IDS solutions for power systems.

In a electrical distribution network, the challenges involved in the decentralized power generation and the resilience of the network to handle the failures, can be easily anticipated. With the use of information technology, a better control can be achieved over the distributed generation units and the fault handling in them. In this contribution, the use of a graceful degradation strategy is proposed as a means to improve the availability of the system during a fault situation. The Graceful degradation is presented as a constraint satisfaction problem. The trigger and the computation of the degradation process are formulated as the constraints. The concept of the utility of the resources is used to support a dynamic decision to trigger the degradation process. The computation of the graceful degradation strategy is formalized as an SMT problem and analyzed using the Z3 SMT-solver. The approach is illustrated with the help of a use case of applying the degradation strategy on a prosumer node during the power outage in the distribution network. It illustrates the dynamic calculation capability of the degradation scheme in the face of an unpredictable power from a renewable energy resource.

Wireless sensor-actuator networks (WSANs) are being adopted in process industries because of their advantages in lowering deployment and maintenance costs. While there has been significant theoretical advancement in networked control design, only limited empirical results that combine control design with realistic WSAN standards exist. This paper presents a cyber-physical case study on a wireless process control system that integrates state-of-the-art network control design and a WSAN based on the WirelessHART standard. The case study systematically explores the interactions between wireless routing and control design in the process control plant. The network supports alternative routing strategies, including single-path source routing and multi-path graph routing. To mitigate the effect of data loss in the WSAN, the control design integrates an observer based on an Extended Kalman Filter with a model predictive controller and an actuator buffer of recent control inputs. We observe that sensing and actuation can have different levels of resilience to packet loss under this network control design. We then propose a flexible routing approach where the routing strategy for sensing and actuation can be configured separately. Finally, we show that an asymmetric routing configuration with different routing strategies for sensing and actuation can effectively improve control performance under significant packet loss. Our results highlight the importance of co-joining the design of wireless network protocols and control in wireless control systems.

We introduce a scalable observer architecture to estimate the states of a discrete-time linear-time-invariant (LTI) system whose sensors can be manipulated by an attacker. Given the maximum number of attacked sensors, we build on previous results on necessary and sufficient conditions for state estimation, and propose a novel multi-modal Luenberger (MML) observer based on efficient Satisfiability Modulo Theory (SMT) solving. We present two techniques to reduce the complexity of the estimation problem. As a first strategy, instead of a bank of distinct observers, we use a family of filters sharing a single dynamical equation for the states, but different output equations, to generate estimates corresponding to different subsets of sensors. Such an architecture can reduce the memory usage of the observer from an exponential to a linear function of the number of sensors. We then develop an efficient SMT-based decision procedure that is able to reason about the estimates of the MML observer to detect at runtime which sets of sensors are attack-free, and use them to obtain a correct state estimate. We provide proofs of convergence for our algorithm and report simulation results to compare its runtime performance with alternative techniques. Our algorithm scales well for large systems (including up to 5000 sensors) for which many previously proposed algorithms are not implementable due to excessive memory and time requirements. Finally, we illustrate the effectiveness of our algorithm on the design of resilient power distribution systems.

Extensible Cyber-Physical Systems (CPS) are loosely connected, multi-domain platforms that "virtualize" their resources to provide an open platform capable of hosting different cyber-physical applications. These cyber-physical platforms are extensible since resources and applications can be added or removed at any time. However, realizing such platform requires resolving challenges emanating from different properties; for this paper, we focus on resilience. Resilience is important for extensible CPS to make sure that extensibility of a system doesn't result in failures and anomalies.

Energy management systems (EMS) are used to control energy usage in buildings and campuses, by employing technologies such as supervisory control and data acquisition (SCADA) and building management systems (BMS), in order to provide reliable energy supply and maximise user comfort while minimising energy usage. Historically, EMS systems were installed when potential security threats were only physical. Nowadays, EMS systems are connected to the building network and as a result directly to the outside world. This extends the attack surface to potential sophisticated cyber-attacks, which adversely impact EMS operation, resulting in service interruption and downstream financial implications. Currently, the security systems that detect attacks operate independently to those which deploy resiliency policies and use very basic methods. We propose a novel EMS cyber-physical-security framework that executes a resilient policy whenever an attack is detected using security analytics. In this framework, both the resilient policy and the security analytics are driven by EMS data, where the physical correlations between the data-points are identified to detect outliers and then the control loop is closed using an estimated value in place of the outlier. The framework has been tested using a reduced order model of a real EMS site.

Riding on the success of SDN for enterprise and data center networks, recently researchers have shown much interest in applying SDN for critical infrastructures. A key concern, however, is the vulnerability of the SDN controller as a single point of failure. In this paper, we develop a cyber-physical simulation platform that interconnects Mininet (an SDN emulator), hardware SDN switches, and PowerWorld (a high-fidelity, industry-strength power grid simulator). We report initial experiments on how a number of representative controller faults may impact the delay of smart grid communications. We further evaluate how this delay may affect the performance of the underlying physical system, namely automatic gain control (AGC) as a fundamental closed-loop control that regulates the grid frequency to a critical nominal value. Our results show that when the fault-induced delay reaches seconds (e.g., more than four seconds in some of our experiments), degradation of the AGC becomes evident. Particularly, the AGC is most vulnerable when it is in a transient following say step changes in loading, because the significant state fluctuations will exacerbate the effects of using a stale system state in the control.

Software defined networking (SDN) is an emerging technology for controlling flows through networks. Used in the context of industrial control systems, an objective is to design configurations that have built-in protection for hardware failures in the sense that the configuration has "baked-in" back-up routes. The objective is to leave the configuration static as long as possible, minimizing the need to have the controller push in new routing and filtering rules We have designed and implemented a tool that enables us to determine the complete connectivity map from an analysis of all switch configurations in the network. We can use this tool to explore the impact of a link failure, in particular to determine whether the failure induces loss of the ability to deliver a flow even after the built-in back-up routes are used. A measure of the original configuration's resilience to link failure is the mean number of link failures required to induce the first such loss of service. The computational cost of each link failure and subsequent analysis is large, so there is much to be gained by reducing the overall cost of obtaining a statistically valid estimate of resiliency. This paper shows that when analysis of a network state can identify all as-yet-unfailed links any one of whose failure would induce loss of a flow, then we can use the technique of importance sampling to estimate the mean number of links required to fail before some flow is lost, and analyze the potential for reducing the variance of the sample statistic. We provide both theoretical and empirical evidence for significant variance reduction.

Traffic signals were originally standalone hardware devices running on fixed schedules, but by now, they have evolved into complex networked systems. As a consequence, traffic signals have become susceptible to attacks through wireless interfaces or even remote attacks through the Internet. Indeed, recent studies have shown that many traffic lights deployed in practice have easily exploitable vulnerabilities, which allow an attacker to tamper with the configuration of the signal. Due to hardware-based failsafes, these vulnerabilities cannot be used to cause accidents. However, they may be used to cause disastrous traffic congestions. Building on Daganzo's well-known traffic model, we introduce an approach for evaluating vulnerabilities of transportation networks, identifying traffic signals that have the greatest impact on congestion and which, therefore, make natural targets for attacks. While we prove that finding an attack that maximally impacts congestion is NP-hard, we also exhibit a polynomial-time heuristic algorithm for computing approximately optimal attacks. We then use numerical experiments to show that our algorithm is extremely efficient in practice. Finally, we also evaluate our approach using the SUMO traffic simulator with a real-world transportation network, demonstrating vulnerabilities of this network. These simulation results extend the numerical experiments by showing that our algorithm is extremely efficient in a microsimulation model as well.