A Cyber Physical System (CPS) consists of cyber components for computation and communication, and physical components such as sensors and actuators for process control. These components are networked and interact in a feedback loop. CPS are found in critical infrastructure such as water distribution, power grid, and mass transportation. Often these systems are vulnerable to attacks as the cyber components such as Supervisory Control and Data Acquisition workstations, Human Machine Interface and Programmable Logic Controllers are potential targets for attackers. In this work, we report a study to investigate the impact of cyber attacks on a water distribution (WADI) system. Attacks were designed to meet attacker objectives and launched on WADI using a specially designed tool. This tool enables the launch of single and multi-point attacks where the latter are designed to specifically hide one or more attacks. The outcome of the experiments led to a better understanding of attack propagation and behavior of WADI in response to the attacks as well as to the design of an attack detection mechanism for water distribution system.
Attacks have been modeled as noise in sensor data , @cite_49 . Attack models designed specifically for CPS include a variety of deception attacks including surge, bias, and geometric , @cite_3 . Such models have been used in experiments to understand the effectiveness of statistical techniques in detecting cyber attacks. The attacks designed in this work are based on a cyber-physical attacker model , @cite_21 . @cite_43 proposed a detailed procedure for modeling cyber systems using attack graphs. Such graphs model practical vulnerabilities in distributed networked systems. , @cite_24 have proposed argument graphs as a means to capture the workflow in a CPS. The graphs are intended to assess a system in the presence of an attacker. The graphs are formed based on information in the workflow such as use case or state, physical system topology such as network type, and an attacker model such as an order to interrupt, power supply, physical tampering, network connection, denial of service, etc. Typed graphs , @cite_35 and Bayesian defense graphs , @cite_30 are a few other important contributions to the modeling of cyber attacks.
@cite_30 @cite_35 @cite_21 @cite_3 @cite_24 @cite_43 @cite_49
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An attacker model is proposed for Cyber Physical Systems (CPS). The attack models derived from the attacker model are used to generate parameterized attack procedures and functions that target a specific CPS. The proposed models capture both physical and cyber attacks and unify a number of existing attack models into a common framework useful for researchers in the experimental assessment of attack detection techniques. The generality of the models is shown by mapping a broad variety of existing attack models to the models proposed here, as well as generating attacks that are not found in the CPS design literature. The models have been used extensively in understanding the impact of cyber attacks on a water treatment system and in the design and assessment of detection mechanisms. In this paper we advocate the use of workflow---describing how a system provides its intended functionality---as a pillar of cybersecurity analysis and propose a holistic workflow-oriented assessment framework. While workflow models are currently used in the area of performance and reliability assessment, these approaches are designed neither to assess a system in the presence of an active attacker, nor to assess security aspects such as confidentiality. On the other hand, existing security assessment methods typically focus on modeling the active attacker (e.g., attack graphs), but many rely on restrictive models that are not readily applicable to complex (e.g., cyber-physical or cyber-human) systems. By "going with the flow," our assessment framework can naturally adopt a holistic view of such systems, unifying information about system components, their properties, and possible attacks to argue about a security goal. The argument is expressed in a graph structure, based on inputs from several distinct classes that are integrated in a systematic manner. That rigorous structure allows our approach to provide quantitative assessment in an automated fashion (like reliability assessment tools and attack graphs), while maintaining a broad assessment scope. We demonstrate our security assessment process using the case of Advanced Metering Infrastructure in a smart power grid and obtain quantitative results for system availability and confidentiality. Security of Cyber-Physical Systems (CPS) against cyber attacks is an important yet challenging problem. Since most cyber attacks happen in erratic ways, it is difficult to describe them systematically. In this paper, instead of identifying a specific cyber attack model, we are focused on analyzing the system's response during cyber attacks. Deception attacks (or false data injection attacks), which are performed by tampering with system components or data, are not of particular concern if they can be easily detected by the system's monitoring system. However, intelligent cyber attackers can avoid being detected by the monitoring system by carefully design cyber attacks. Our main objective is to investigate the performance of such stealthy deception attacks from the system's perspective. We investigate three kinds of stealthy deception attacks according to the attacker's ability to compromise the system. Based on the information about the dynamics of the system and existing hypothesis testing algorithms, we derive the necessary and sufficient conditions under which the attacker could perform each kind of attack without being detected. In the end, we illustrate the threat of these cyber attacks using an Unmanned Aerial Vehicle (UAV) navigation example. A distributed detection method is proposed to detect single stage multi-point (SSMP) attacks on a Cyber Physical System (CPS). Such attacks aim at compromising two or more sensors or actuators at any one stage of a CPS and could totally compromise a controller and prevent it from detecting the attack. However, as demonstrated in this work, using the flow properties of water from one stage to the other, a neighboring controller was found effective in detecting such attacks. The method is based on physical invariants derived for each stage of the CPS from its design. The attack detection effectiveness of the method was evaluated experimentally against an operational water treatment testbed containing 42 sensors and actuators. Results from the experiments point to high effectiveness of the method in detecting a variety of SSMP attacks but also point to its limitations. Distributing the attack detection code among various controllers adds to the scalability of the proposed method. This paper presents a novel unsupervised approach to detect cyber attacks in Cyber-Physical Systems (CPS). We describe an unsupervised learning approach using a Recurrent Neural network which is a time series predictor as our model. We then use the Cumulative Sum method to identify anomalies in a replicate of a water treatment plant. The proposed method not only detects anomalies in the CPS but also identifies the sensor that was attacked. The experiments were performed on a complex dataset which is collected through a Secure Water Treatment Testbed (SWaT). Through the experiments, we show that the proposed technique is able to detect majority of the attacks designed by our research team with low false positive rates. Future power networks will be characterized by safe and reliable functionality against physical and cyber attacks. This paper proposes a unified framework and advanced monitoring procedures to detect and identify network components malfunction or measurements corruption caused by an omniscient adversary. We model a power system under cyber-physical attack as a linear time-invariant descriptor system with unknown inputs. Our attack model generalizes the prototypical stealth, (dynamic) false-data injection and replay attacks. We characterize the fundamental limitations of both static and dynamic procedures for attack detection and identification. Additionally, we design provably-correct (dynamic) detection and identification procedures based on tools from geometric control theory. Finally, we illustrate the effectiveness of our method through a comparison with existing (static) detection algorithms, and through a numerical study. With the exponential growth of cyber-physical systems (CPSs), new security challenges have emerged. Various vulnerabilities, threats, attacks, and controls have been introduced for the new generation of CPS. However, there lacks a systematic review of the CPS security literature. In particular, the heterogeneity of CPS components and the diversity of CPS systems have made it difficult to study the problem with one generalized model. In this paper, we study and systematize existing research on CPS security under a unified framework. The framework consists of three orthogonal coordinates: 1) from the security perspective, we follow the well-known taxonomy of threats, vulnerabilities, attacks and controls; 2) from the CPS components perspective, we focus on cyber, physical, and cyber-physical components; and 3) from the CPS systems perspective, we explore general CPS features as well as representative systems (e.g., smart grids, medical CPS, and smart cars). The model can be both abstract to show general interactions of components in a CPS application, and specific to capture any details when needed. By doing so, we aim to build a model that is abstract enough to be applicable to various heterogeneous CPS applications; and to gain a modular view of the tightly coupled CPS components. Such abstract decoupling makes it possible to gain a systematic understanding of CPS security, and to highlight the potential sources of attacks and ways of protection. With this intensive literature review, we attempt to summarize the state-of-the-art on CPS security, provide researchers with a comprehensive list of references, and also encourage the audience to further explore this emerging field. Future power networks will be characterized by safe and reliable functionality against physical malfunctions and cyber attacks. This paper proposes a unified framework and advanced monitoring procedures to detect and identify network components malfunction or measurements corruption caused by an omniscient adversary. We model a power system under cyber-physical attack as a linear time-invariant descriptor system with unknown inputs. Our attack model generalizes the prototypical stealth, (dynamic) false-data injection and replay attacks. We characterize the fundamental limitations of both static and dynamic procedures for attack detection and identification. Additionally, we design provably-correct (dynamic) detection and identification procedures based on tools from geometric control theory. Finally, we illustrate the effectiveness of our method through a comparison with existing (static) detection algorithms, and through a numerical study. An important way cyber adversaries find vulnerabilities in modern networks is through reconnaissance, in which they attempt to identify configuration specifics of network hosts. To increase uncertainty of adversarial reconnaissance, the network administrator (henceforth, defender) can introduce deception into responses to network scans, such as obscuring certain system characteristics. We introduce a novel game theoretic model of deceptive interactions of this kind between a defender and a cyber attacker, which we call the Cyber Deception Game. We consider both a powerful (rational) attacker, who is aware of the defender's exact deception strategy, and a naive attacker who is not. We show that computing the optimal deception strategy is NP-hard for both types of attackers. For the case with a powerful attacker, we provide a mixed-integer linear program solution as well as a fast and effective greedy algorithm. Similarly, we provide complexity results and propose exact and heuristic approaches when the attacker is naive. Our extensive experimental analysis demonstrates the effectiveness of our approaches. An experimental investigation was undertaken to understand the impact of single-point cyber attacks on a Secure Water Treatment (SWaT) system. Cyber attacks were launched on SWaT through its SCADA server that connects to the Programmable Logic Controllers (PLCs) that in turn are connected to sensors and actuators. Attacks were designed to meet attacker objectives selected from a novel attacker model. Outcome of the experiments led to a better understanding of (a) the propagation of an attack across the system measured in terms of the number of components affected and (b) the behavior of the water treatment process in SWaT in response to the attacks. The observed response to various attacks was then used to propose attack detection mechanisms based on various physical properties measured during the treatment process. The increasing integration of autonomous systems with publicly available networks exposes them to cyber attackers. An adversary can launch a man-in-the-middle attack to gain control of the system and inflict maximum damages with collision and suicidal attacks. To address this issue, this work establishes an integrative game and control framework to incorporate security into the automatic designs, and take into account the cyber-physical nature and the real-time requirements of the system. We establish a cyber-physical signaling game to develop an impact-aware cyber defense mechanism and leverage model-predictive control methods to design cyber-aware control strategies. The integrative framework enables the co-design of cyber-physical systems to minimize the inflicted systems, leading to online updating the cyber defense and physical layer control decisions. We use unmanned aerial vehicles (UAVs) to illustrate the algorithm, and corroborate the analytical results in two case studies. Recent studies on sequential attack schemes revealed new smart grid vulnerability that can be exploited by attacks on the network topology. Traditional power systems contingency analysis needs to be expanded to handle the complex risk of cyber-physical attacks. To analyze the transmission grid vulnerability under sequential topology attacks, this paper proposes a Q-learning-based approach to identify critical attack sequences with consideration of physical system behaviors. A realistic power flow cascading outage model is used to simulate the system behavior, where attacker can use the Q-learning to improve the damage of sequential topology attack toward system failures with the least attack efforts. Case studies based on three IEEE test systems have demonstrated the learning ability and effectiveness of Q-learning-based vulnerability analysis. Cyber-physical systems (CPS) consist of sensors, actuators, and controllers all communicating over a network; if any subset becomes compromised, an attacker could cause significant damage. With access to data logs and a model of the CPS, the physical effects of an attack could potentially be detected before any damage is done. Manually building a model that is accurate enough in practice, however, is extremely difficult. In this paper, we propose a novel approach for constructing models of CPS automatically, by applying supervised machine learning to data traces obtained after systematically seeding their software components with faults ("mutants"). We demonstrate the efficacy of this approach on the simulator of a real-world water purification plant, presenting a framework that automatically generates mutants, collects data traces, and learns an SVM-based model. Using cross-validation and statistical model checking, we show that the learnt model characterises an invariant physical property of the system. Furthermore, we demonstrate the usefulness of the invariant by subjecting the system to 55 network and code-modification attacks, and showing that it can detect 85 of them from the data logs generated at runtime. An approach for integrating Six-Step Model (SSM) with Information Flow Diagrams (IFDs) is proposed. SSM is a model for Cyber-Physical System (CPS) safety and security analysis, which incorporates six hierarchies of CPS, namely, functions, structure, failures, safety countermeasures, cyber-attacks, and security countermeasures. Relationship matrices are used in SSM to identify inter-relationships between these hierarchies and determine the effect of failures and cyber-attacks on CPSs. Although SSM is a useful tool for CPS safety and security modeling, it lacks guidance for identifying failures and attacks, and selecting adequate set of safety and security countermeasures. To address this issue, an approach for integrating SSM with IFDs is proposed and explained using the water treatment system example. Today's Cyber-Physical Systems (CPSs) are large, complex, and affixed with networked sensors and actuators that are targets for cyber-attacks. Conventional detection techniques are unable to deal with the increasingly dynamic and complex nature of the CPSs. On the other hand, the networked sensors and actuators generate large amounts of data streams that can be continuously monitored for intrusion events. Unsupervised machine learning techniques can be used to model the system behaviour and classify deviant behaviours as possible attacks. In this work, we proposed a novel Generative Adversarial Networks-based Anomaly Detection (GAN-AD) method for such complex networked CPSs. We used LSTM-RNN in our GAN to capture the distribution of the multivariate time series of the sensors and actuators under normal working conditions of a CPS. Instead of treating each sensor's and actuator's time series independently, we model the time series of multiple sensors and actuators in the CPS concurrently to take into account of potential latent interactions between them. To exploit both the generator and the discriminator of our GAN, we deployed the GAN-trained discriminator together with the residuals between generator-reconstructed data and the actual samples to detect possible anomalies in the complex CPS. We used our GAN-AD to distinguish abnormal attacked situations from normal working conditions for a complex six-stage Secure Water Treatment (SWaT) system. Experimental results showed that the proposed strategy is effective in identifying anomalies caused by various attacks with high detection rate and low false positive rate as compared to existing methods. With rapid advances in sensor, computer, and communication networks, modern power systems have become complicated cyber-physical systems. Assessing and enhancing cyber-physical system security is, therefore, of utmost importance for the future electricity grid. In a successful false data injection attack (FDIA), an attacker compromises measurements from grid sensors in such a way that undetected errors are introduced into estimates of state variables such as bus voltage angles and magnitudes. In evading detection by commonly employed residue-based bad data detection tests, FDIAs are capable of severely threatening power system security. Since the first published research on FDIAs in 2009, research into FDIA-based cyber-attacks has been extensive. This paper gives a comprehensive review of state-of-the-art in FDIAs against modern power systems. This paper first summarizes the theoretical basis of FDIAs, and then discusses both the physical and the economic impacts of a successful FDIA. This paper presents the basic defense strategies against FDIAs and discusses some potential future research directions in this field. Abstract Estimating the possible impacts of security attacks on physical processes can help to rank the critical assets based on their sensitivity to performed attacks and predict their attractiveness from the attacker’s point of view. To address this challenge, this paper proposes a new method for assessing the direct and indirect impacts of attacks on cyber–physical systems (CPSs). The proposed method studies the dynamic behavior of systems in normal situation and under security attacks and evaluates the consequence propagation of attacks. The inputs to the model are control parameters including sensor readings and controller signals. The output of the model is evaluating the consequence propagation of attacks, ranking the important assets of systems based on their sensitivity to conducted attacks, and prioritizing the attacks based on their impacts on the behavior of system. The validation phase of the proposed method is performed by modeling and evaluating the consequence propagation of attacks against a boiling water power plant (BWPP). Within the last decade, Security became a major focus in the traditional IT-Industry, mainly through the interconnection of systems and especially through the connection to the Internet. This opened up a huge new attack surface, which resulted in major takedowns of legitimate services and new forms of crime and destruction. This led to the development of a multitude of new defense mechanisms and strategies, as well as the establishing of Security procedures on both, organizational and technical level. Production systems have mostly remained in isolation during these past years, with security typically focused on the perimeter. Now, with the introduction of new paradigms like Industry 4.0, this isolation is questioned heavily with Physical Production Systems (PPSs) now connected to an IT-world resulting in cyber-physical systems sharing the attack surface of traditional web based interfaces while featuring completely different goals, parameters like lifetime and safety, as well as construction. In this work, we present an outline on the major security challenges faced by cyber-physical production systems. While many of these challenges harken back to issues also present in traditional web based IT, we will thoroughly analyze the differences. Still, many new attack vectors appeared in the past, either in practical attacks like Stuxnet, or in theoretical work. These attack vectors use specific features or design elements of cyber-physical systems to their advantage and are unparalleled in traditional IT. Furthermore, many mitigation strategies prevalent in traditional IT systems are not applicable in the industrial world, e.g., patching, thus rendering traditional strategies in IT-Security unfeasible. A thorough discussion of the major challenges in CPPS-Security is thus required in order to focus research on the most important targets. High correlation in failure of information systems due to worms and viruses has been cited as major impediment to cyber-insurance. However, of the many cyber-risk classes that influence failure of information systems, not all exhibit similar correlation properties. In this paper, we introduce a new classification of correlation properties of cyber-risks based on a twin-tier approach. At the first tier, is the correlation of cyber-risks within a firm i.e. correlated failure of multiple systems on its internal network. At second tier, is the correlation in risk at a global level i.e. correlation across independent firms in an insurer’s portfolio. Various classes of cyber-risks exhibit dierent level of correlation at two tiers, for instance, insider attacks exhibit high internal but low global correlation. While internal risk correlation within a firm influences its decision to seek insurance, the global correlation influences insurers’ decision in setting the premium. Citing real data we study the combined dynamics of the two-step risk arrival process to determine conditions conducive to the existence of cyber-insurance market. We address technical, managerial and policy choices influencing the correlation at both steps and the business implications thereof. The massive proliferation of information and communications technologies (hardware and software) into the heart of modern critical infrastructures has given birth to a unique technological ecosystem. Despite the many advantages brought about by modern information and communications technologies, the shift from isolated environments to "systems-of-systems" integrated with massive information and communications infrastructures (e.g., the Internet) exposes critical infrastructures to significant cyber threats. Therefore, it is imperative to develop approaches for identifying and ranking assets in complex, large-scale and heterogeneous critical infrastructures. To address these challenges, this paper proposes a novel methodology for assessing the impacts of cyber attacks on critical infrastructures. The methodology is inspired by research in system dynamics and sensitivity analysis. The proposed behavioral analysis methodology computes the covariances of the observed variables before and after the execution of a specific intervention involving the control variables. Metrics are proposed for quantifying the significance of control variables and measuring the impact propagation of cyber attacks.Experiments conducted on the IEEE 14-bus and IEEE 300-bus electric grid models, and on the well-known Tennessee Eastman chemical process demonstrate the efficiency, scalability and cross-sector applicability of the proposed methodology in several attack scenarios. The advantages of the methodology over graph-theoretic and electrical centrality metric approaches are demonstrated using several test cases. Finally, a novel, stealthy cyber-physical attack is demonstrated against a simulated power grid; this attack can be used to analyze the precision of anomaly detection systems.