A Novel Methodology for Cybersecurity Investment Optimization in Smart Grids using Attack-Defense Trees and Game Theory

Guest Editorial

Security and Privacy Aspect of Cyber Physical Systems

Smart grids are electric grids that utilize advanced technologies of monitoring, control, and communication to supply reliable and secure energy, improve the efficiency of the system, and provide flexibility to the prosumers. The beginning of the smart grid era and the development in modern infrastructures of metering, communication, and energy storage have revolutionized the power grid. Smart grids are developed with complex physical networks and cyber systems thus enabling smart grids for the Internet of Energy (IoE). IoE is the cloud where sources of power generation and loads of power consumption are embedded with intelligence. Modern electric power grids are integrated with sensors used to provide measurements. The sensor measurements and the complex applications of various sensors cause a need for Cyber-Physical System (CPS). CPS is a class of systems that integrates physical process, computation, and networking. The CPS model of the smart grid helps in modeling and simulation for evaluation of the system performance and characteristics. The CPS model of the smart grid must enable the smart grid to be robust, 2allow future extensions, and compatible with web service technologies. The power generation sources and loads such as smart buildings are the physical layers, the sensors used for measurements form the cyber-physical integration and the data storage and processing using IOE forms the cyber layer of the smart grid. The CPS model of the smart grid helps in the integration of intelligent devices and allied information and communication technologies for robust and reliable operation in smart grids. In the smart grid paradigm, the energy management system has a vital role to increase the efficiency and reliability of the system. This chapter presents a CPS model for smart grid and the challenges associated with the development of the CPS model. In addition, this chapter describes the energy management system model of the smart grid.

This paper aims to investigate the security problem of nonlinear cyber-physical systems (CPSs), which poses a challenge to handle compared with linear CPSs. A series of optimization problems for nonlinear CPSs under injection attack are constructed, which are based on a general model of the nonlinear systems with repetitive operation characteristics and a novel introduction of the key technical lemma. These optimization results are more general than the existing injection attack results and the requirements for attackers to obtain system information are relaxed. Also, the form of switching applied to the attack strategy possesses several advantages, including high stealthiness, lower cost, and more flexibility. Therefore, the new optimal injection attack strategies are expected to be more widespread and provide a basis for the design of defense strategies. The key to acquiring the designed optimal attack strategies is to adopt the linear input/output (I/O) data model for these systems via introducing an estimation term of the improved projection estimation method into the linear model. Finally, a networked GLUON-6L3 manipulator example validates the effectiveness of the proposed methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The main purpose of this paper is to study the cyber security of nonlinear cyber-physical systems from the perspective of attackers, which can help defenders fully understand the behavior of attackers. Most of the existing attack strategies aimed at linear systems or have a priori knowledge of the attacked systems. However, there are great limitations and difficulties in practical application. In this paper, the new attack strategies are proposed by combining system identification, iterative learning and control theory, which relaxes the requirement of the attacker’s ability. In detail, the attacker only needs to obtain the control input and output data of the attacked system and design the attack strategy according to it. Among them, the first-in, first-out queue storage method is applied to remove the requirements for the storage capability of the attacker. In practical applications, the attacker does not need to store the I/O data continuously, but only store the initial value and the data of two successive iterations. The mathematical analytic forms of two optimal attack strategies are given, and then their effectiveness are verified on a welding work of a networked six-axis manipulator. In the future, we will investigate the design of attack strategies for nonlinear systems with heterogeneous dynamics and multiple delays.

Cyber-physical systems (CPS) security has become an increasingly important research topic in recent years. Geared towards more advanced cyberattack detection techniques as part of strategies for enhancing the security of CPS, in this paper we propose a machine learning based cyber-attack detection scheme. The proposed scheme is a physical-domain technique; specifically, it assumes the physical measurements of the system carry sufficient information for capturing the system behavior, thus can be used for differentiating normal operation and attacks. CPS are complex in nature and the number of physical measurements available for CPS is often overwhelmingly high. Thus, accurately modeling CPS’ dynamic behavior, more importantly, distinguishing normal and adversary activities based on the large number of physical measurements, can be challenging. To address the challenge, we have focused our research effort on feature engineering, that is, to intelligently derive a set of salient signatures or features from the noisy measurements. We make sure the derived features are more compact and, more importantly, have more discriminant power than the original physical measurements, thus enabling us to achieve more accurate and robust detection performance. To demonstrate the effectiveness of the proposed scheme, in our experimental study we consider gas turbines of combined cycle power plants as the cyber-physical system. Using the data from the high-fidelity simulation we show that our proposed cyberattack detection scheme is able to achieve high detection performance.

A Through Analysis on Protecting Cyber Threats and Attacks on Cps Embedded Subsystems

Smart Grid is the deep interactive fusion of power information system and physical system under the background of modern energy interconnection. Compared with the traditional power grid, its performance has been greatly improved. The essence of smart grid is the application of cyber physical system (CPS) in power industry, which is called power CPS. In view of the cyber security risk caused by the integration of the two systems in power CPS, which may cause the system to suffer complex and severe cascading failures and blackouts, it is of great significance to study the power CPS modeling and security defense based on the integration of the two networks. Different things have different classification methods from different angles. This paper summarizes the main research and analysis methods of power CPS modeling and security defense protection at home and abroad, and puts forward the future development direction, hoping to provide reference for the relevant researchers.

Smart Grid is a cyber-physical system that incorporates Information and Communication Technologies (ICT) into the physical power system, which introduces vulnerabilities to the grid and opens the door to cyber attacks. Wide area protection is one of the most important smart grid applications that aims at protecting the power system against faults and disturbances, which makes it an attractive target to cyber attacks, aiming at compromising the reliability of the power system. Understanding the interaction between the cyber and physical components of the smart grid and analyzing the damage that cyber-attacks can do to wide area protection is very important as it helps in developing effective mitigation approaches. This paper evaluates the impact of cyber attacks on a wide area distance relay backup protection scheme in real-time, through the development of a co-simulation platform based on Real Time Digital Simulator (RTDS) and network simulator 3 (NS3) and using the IEEE-14 bus power system model.

IT security management in distributed Smart Grids has been discussed in a number of recent works, mainly from the viewpoint of how to prevent unauthorized access, either organizationally or cryptographically. In this paper, we aim to extend this viewpoint by introducing the concept of emergent security in cyber-physical energy systems. The main contribution of this paper is the description of four architectural layers, providing a structured approach for security assessment and evaluation. The proposed emergent security layer shifts the perspective of IT security towards incorporating real-time measurements, decoupling feedback of components, increasing topology-awareness, and implementing distributed counter-measures and participant reputation systems. We propose an iterative process for designing resilient distributed control algorithms for Smart Grids. This process acknowledges the new challenges for security management in critical infrastructures due to the recent advances in inter connectivity of systems. The proposed approach is validated using an implementation of Smart Grid control and a co-simulation of a cyber-physical energy system, indicating the importance of the introduced emergent security layer.

Due to digitalization and technological advancement, systems and their requirements are changing, and there is an increasing use of Cyber-Physical Systems (CPS) with a direct connection between the physical and the digital world. These systems process data and have integrated functions and a real-time requirement. There is a great need for security, protection of data, and reliability. The use of digital systems in the energy sector is increasing and changing, as are consumers and generators. This requires a secure IT, communications infrastructure, and highly performing data platforms. The new systems being created are called CPS, which are highly scalable, dynamic, and volatile and process many data of various kinds. One significant aspect of a CPS is security. Personal data and business-sensitive data may be processed, or mission-critical processes may be mapped. Risk analysis and security assessments based on conventional methods and guidelines (for example, BSI IT Basic Protection) have revealed drawbacks. Present security assessment methods focus on analyzing corporate information systems or are applied for software development life cycles. CPS criteria and their impact on security have not yet been accounted for in today’s security assessments and their corresponding frameworks. This thesis concentrates on modeling CPS security and deriving a framework for CPS security assessments. The considered criteria are data security as conventional, expanded by scalability, and real-time. The underlying framework is process-oriented. CPS use cases will be broken down into (atomic) processes and the security assessed based on each process’ data security, scalability and real-time model. Eventually, this will mean security measures can be mapped at the process level. Conducting this research, the focus was on smart grid systems as one example of CPS. For the discussion of mapping security measures, authentication was selected. The result analysis shows an added value in the security assessment of CPS based on the criteria of data security, scalability, real-time, and the breakdown at the process level. The underlying model allows to cope with the complexity of CPS and more precisely assess the security of CPS. The overall approach of CPS security modeling and provision by using a process-oriented framework is highly innovative and provides a concept for developing future CPS security assessment tools.

Cyber attack models for smart grid environments

A survey on applications of Alternating Direction Method of Multipliers in smart power grids

Cyber Physical Systems (CPS) are characterized by their ability to integrate\nthe physical and information or cyber worlds. Their deployment in critical\ninfrastructure have demonstrated a potential to transform the world. However,\nharnessing this potential is limited by their critical nature and the far\nreaching effects of cyber attacks on human, infrastructure and the environment.\nAn attraction for cyber concerns in CPS rises from the process of sending\ninformation from sensors to actuators over the wireless communication medium,\nthereby widening the attack surface. Traditionally, CPS security has been\ninvestigated from the perspective of preventing intruders from gaining access\nto the system using cryptography and other access control techniques. Most\nresearch work have therefore focused on the detection of attacks in CPS.\nHowever, in a world of increasing adversaries, it is becoming more difficult to\ntotally prevent CPS from adversarial attacks, hence the need to focus on making\nCPS resilient. Resilient CPS are designed to withstand disruptions and remain\nfunctional despite the operation of adversaries. One of the dominant\nmethodologies explored for building resilient CPS is dependent on machine\nlearning (ML) algorithms. However, rising from recent research in adversarial\nML, we posit that ML algorithms for securing CPS must themselves be resilient.\nThis paper is therefore aimed at comprehensively surveying the interactions\nbetween resilient CPS using ML and resilient ML when applied in CPS. The paper\nconcludes with a number of research trends and promising future research\ndirections. Furthermore, with this paper, readers can have a thorough\nunderstanding of recent advances on ML-based security and securing ML for CPS\nand countermeasures, as well as research trends in this active research area.\n

This paper proposes a reinforcement learning (RL) algorithm for the security problem of state estimation of cyber-physical system (CPS) under denial-of-service (DoS) attacks. The security of CPS will inevitably decline when faced with malicious cyber attacks. In order to analyze the impact of cyber attacks on CPS performance, a Kalman filter, as an adaptive state estimation technology, is combined with an RL method to evaluate the issue of system security, where estimation performance is adopted as an evaluation criterion. Then, the transition of estimation error covariance under a DoS attack is described as a Markov decision process, and the RL algorithm could be applied to resolve the optimal countermeasures. Meanwhile, the interactive combat between defender and attacker could be regarded as a two-player zero-sum game, where the Nash equilibrium policy exists but needs to be solved. Considering the energy constraints, the action selection of both sides will be restricted by setting certain cost functions. The proposed RL approach is designed from three different perspectives, including the defender, the attacker and the interactive game of two opposite sides. In addition, the framework of Q-learning and state–action–reward–state–action (SARSA) methods are investigated separately in this paper to analyze the influence of different RL algorithms. The results show that both algorithms obtain the corresponding optimal policy and the Nash equilibrium policy of the zero-sum interactive game. Through comparative analysis of two algorithms, it is verified that the differences between Q-Learning and SARSA could be applied effectively into the secure state estimation in CPS.

One of the basic requirements of today's sophisticated world is the availability of electrical energy, and neglect of this matter may have irreparable damages such as an extensive blackout. The problems which were introduced about the traditional power grid, and also, the growing advances in smart technologies make the traditional power grid go towards smart power grid. Although widespread utilisation of telecommunication networks in smart power grid enhances the efficiency of the system, it will create a critical platform for cyber attacks and penetration into the system. Automatic generation control (AGC) is a fundamental control system in the power grid, and it is responsible for controlling the frequency of the grid. An attack on the data transmitted through the telecommunications link from the sensors to the AGC will cause frequency deviation, resulting in disconnection of the load, generators and ultimately global blackout. In this study, by using a Kalman filter and a proposed detector, a solution has been presented to detect the attack before it can affect the system. Contrary to existing methods, this method is able to detect attacks that are stealthy from the area control error signal and χ 2 ‐detector. Simulations confirm the effectiveness of this method.