Security Of Cyber-physical Systems Research Articles

Analysis of Cross-Domain Security and Privacy Aspects of Cyber-Physical Systems

Analysis of Cross-Domain Security and Privacy Aspects of Cyber-Physical Systems

Design of False Data Injection Attacks in Cyber-Physical Systems

Cyber-physical systems (CPSs) are more vulnerable to false data injection (FDI) attacks. An attacker can launch an FDI attack at any chosen location in the CPS. It is required to analyze the system’s behaviour in the presence of FDI attacks rather than analyzing attacks at a specific location. In this paper, we consider security in CPS due to FDI attacks at a single as well as multiple locations. The CPS is modeled as a discrete linear time-invariant system with white Gaussian noise. The system is equipped with a Kalman filter as a state estimator and a Chi-square detector for attack detection. We studied FDI attacks at the sensor, actuator, and physical system. Attackers may guess the system parameters in the worst-case and remain undetectable by carefully designing the attack sequences. The attacker always attempts to increase the state estimation error in the system. Based on the system model, we have identified seven kinds of FDI attacks and analyzed their effects on the system’s security. It is found that the attacker can produce bounded errors in some cases and unbounded errors in some others. Simulation of the attacks on the system model is performed through numerical examples to illustrate their effectiveness.

Security of Cyber-Physical Systems: Design of a Security Supervisor to Thwart Attacks

Cyber-physical systems (CPSs) integrate computing and communication capabilities to monitor and control physical processes. In order to do so, communication networks are commonly used to connect sensors, actuators, and controllers in the feedback system. The use of communication networks increases the vulnerability of CPSs to cyberattacks that can drive the system to unsafe states. One of the most powerful cyberattacks is the so-called man-in-the-middle attack, where the intruder can observe, hide, create, or change information in the attacked network channels. In this article, we propose a defense strategy that can thwart man-in-the-middle attacks in the sensor and/or control communication channels of CPSs modeled as discrete-event systems. We also introduce the definition of network attack security (NA-Security), which is related to the possibility of preventing the system from reaching unsafe states by using a security supervisor, whose online implementation has polynomial computational complexity, and we propose an algorithm to verify this property. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —CPSs form the basis for the fourth industrial revolution, called Industry 4.0. In these kinds of systems, communication networks are used to connect sensors, actuators, and controllers in the closed-loop system. The increase in the use of communication networks also increases the vulnerability of CPSs to cyberattacks. The use of conventional defenses, such as firewalls, is not recommended in industrial systems due to the introduction of communication delays. In this article, we propose a new defense strategy that prevents damages in the system caused by man-in-the-middle attacks. A security supervisor is proposed to disable controllable events when there is a risk to reach states of the system that may represent damages to the system or its operators.

Cyber LOPA: An Integrated Approach for the Design of Dependable and Secure Cyber-Physical Systems

Safety risk assessment is an essential process to ensure a dependable cyber-physical system (CPS) design. Traditional risk assessment considers only physical failures. For modern CPSs, failures caused by cyber attacks are on the rise. The focus of latest research effort is on safety–security lifecycle integration and the expansion of modeling formalisms for risk assessment to incorporate security failures. The interaction between safety and security lifecycles and its impact on the overall system design, as well as the reliability loss resulting from ignoring security failures, are some of the overlooked research questions. This article addresses these research questions by presenting a new safety design method named cyber layer of protection analysis (CLOPA) that extends the existing layer of protection analysis (LOPA) framework to include failures caused by cyber attacks. The proposed method provides a rigorous mathematical formulation that expresses quantitatively the tradeoff between designing a highly reliable and a highly secure CPS. We further propose a co-design lifecycle process that integrates the safety and security risk assessment processes. We evaluate the proposed CLOPA approach and the integrated lifecycle on a practical case study of a process reactor controlled by an industrial control testbed and provide a comparison between the proposed CLOPA and current LOPA risk assessment practice.

Towards automated hazard analysis for CPS security with application to CSTR system

Hazard analysis to identify risks due to cyber attacks is crucial to ensure cyber–physical systems’ safety and correct their behavior. This study proposes a framework to analyze cyber–physical systems (CPS) risks using system dynamics to identify the critical physical components and hazardous interactions. We divide the input space into three regions, namely safe, alarm, and hazard zones, based on some given information and the dynamics of the CPS. These regions show how by manipulation of the critical inputs system can be driven to some predefined region. Moreover, using the concept of gap metric, we refine the list of the input value of vulnerable components to update the regions. Also, as a part of hazard analysis, we study and obtain the time needed to operate the system from a safe region to a hazard region by manipulating the inputs. The proposed framework uses the physical system’s model dynamics to simulate safe, alarm, and hazard mode activity. This framework’s primary goal is to provide decision-makers with a framework to evaluate CPS safety measures to implement adequate safety mechanisms. Model dynamics of continuous stirred tank reactor system (CSTR) has been employed to simulate the process under different manipulation of the input signal based on the proposed framework to identify the critical inputs and hazardous scenarios.

Towards situational aware cyber-physical systems: A security-enhancing use case of blockchain-based digital twins

The complexity of cyberattacks in Cyber-Physical Systems (CPSs) calls for a mechanism that can evaluate critical infrastructures’ operational behaviour and security without affecting the operation of live systems. In this regard, Digital Twins (DTs) provide actionable insights through monitoring, simulating, predicting, and optimizing the state of CPSs. Through the use cases, including system testing and training, detecting system misconfigurations, and security testing, DTs strengthen the security of CPSs throughout the product lifecycle. However, such benefits of DTs depend on an assumption about data integrity and security. Data trustworthiness becomes more critical while integrating multiple components among different DTs owned by various stakeholders to provide an aggregated view of the complex physical system. This article envisions a blockchain-based DT framework as Trusted Twins for Securing Cyber-Physical Systems (TTS-CPS). With the automotive industry as a CPS use case, we demonstrate the viability of the TTS-CPS framework through a proof of concept. To utilize reliable system specification data for building the process knowledge of DTs, we ensure the trustworthiness of data-generating sources through Integrity Checking Mechanisms (ICMs). Additionally, Safety and Security (S&S) rules evaluated during simulation are stored and retrieved from the blockchain, thereby establishing more understanding and confidence in the decisions made by the underlying systems. Finally, we perform formal verification of the TTS-CPS.

FlexiChain: A Minerless Scalable Next Generation Blockchain for Rapid Data and Device Security in Large Scale Complex Cyber-Physical Systems

FlexiChain: A Minerless Scalable Next Generation Blockchain for Rapid Data and Device Security in Large Scale Complex Cyber-Physical Systems

Cyber-Physical Systems and their Security Issues

Broadly, cyber physical systems are systems that integrate computing into the physical world. Control systems, the increase in their intersection with information technology networks and the variety of communication methods also cause the threat vectors in cyber physical systems security to change and increase. This shows that traditional information technology security measures, new generation information technology security solutions or security solutions specific to control systems will be insufficient alone, and the need for integrated security solutions that include all components in the cyber physical systems network and include operational scenarios. In this work, it was cover a couple of definitions of cyber physical systems and it was present an overview of cyber physical systems, what they are, where they are found, and how they are used. It also includes some information on the specific reasons cyber physical systems present greater security demands. The stakes are higher, the components are much more difficult to maintain and update, and the systems are more complex. The goal is to understand these systems well enough to design effective security measures to counter potential attacks.

サイバーフィジカルシステムのサイバーセキュリティと課題

サイバーフィジカルシステムのサイバーセキュリティと課題

Attack Detection and Data Generation for Wireless Cyber-Physical Systems Based on Self-Training Powered Generative Adversarial Networks

Malicious attacks in wireless cyber-physical systems have become more frequent in recent years. With the development of attack methods used by attackers, security in wireless cyber-physical systems needs to progress to match various attacks. Deep learning is a field that has developed rapidly in recent years, and generative adversarial network (GAN) is a deep learning model that has shown promising results. GAN has two interlocking subsystems, one to generate fake samples and another to classify the generated fake samples. Finally, the system has two well trained subsystems that are capable of both generating convincing samples and classifying generated samples. In this article, we propose a self-training powered GAN (ST-GAN) system to detect attacks in wireless cyber-physical systems. At the same time, the proposed ST-GAN system solves the issue of limited data in the field of security for wireless cyber-physical systems, which is caused by confidentiality as well as the number of attacks. Our experiments have shown that the proposed system can effectively detect attacks in wireless cyber-physical systems.

Geometrical Characterization of Sensor Placement for Cone-Invariant and Multi-Agent Systems against Undetectable Zero-Dynamics Attacks

Undetectable attacks are an important class of malicious attacks threatening the security of cyber-physical systems, which can modify a system's state but leave the system output measurements unaffected and hence cannot be detected from the output. This paper studies undetectable attacks on cone-invariant systems and multi-agent systems. We first provide a general characterization of zero-dynamics attacks, which characterizes fully undetectable attacks targeting the nonminimum phase zeros of a system. This geometrical characterization makes it possible to develop a defense strategy seeking to place a minimal number of sensors to detect and counter the zero-dynamics attacks on the system's actuators. The detect and defense scheme amounts to computing a set containing potentially vulnerable actuator locations and nodes and a defense union for feasible placement of sensors based on the geometrical properties of the cones under consideration.

A lightweight device-level Public Key Infrastructure with DRAM based Physical Unclonable Function (PUF) for secure cyber physical systems

Cyber–Physical Systems (CPS) are becoming increasingly ubiquitous and serve as a platform for connecting vast majority of everyday devices to the Internet. Traditional computer-based security systems are not preferred in CPS devices due to limited storage, security issues, and computation power, thus necessitating the demand for efficient and secure computation techniques that would ensure robust CPS platforms. In this paper, we proposed a lightweight Public Key Infrastructure (PKI) scheme that can be used in CPS devices. Conventional PKI systems are characterized with three resource-intensive processes, namely, key generation, certificate validation, and certificate revoking. The proposed method uses Physical Unclonable Function (PUF) to simplify the key generation process and introduces the session key concept to address the other two limitations, viz., certificate validation and revoking. In addition to this, the proposed method addresses some crucial security needs of the PKI scheme, such as node registration, strong random number generation, defining trust relationships among the different “thing” nodes. Additionally, PUF-based trust relationships require reduced infrastructural setup, lesser power, and storage capacity while also incorporating Elliptic Curve Cryptography (ECC) for asymmetric key pair generation. The proposed method has been realized using a Dynamic Random Access Memory (DRAM)-based PUF. The experimental result proves the technique’s efficiency compared to the state-of-the-art methods with a 4–7 times, consuming 5–10 times, lesser energy.

Security Perspective Analysis of Industrial Cyber Physical Systems (I-CPS): A Decade-wide Survey

Considering the exceptional growth of Cyber Physical Systems (CPSs), multiple and potentially grave security challenges have emerged in this field. Different vulnerabilities and attacks are present in front of new generation CPSs, such as Industrial CPS (I-CPS). The underlying non-uniform standards, device heterogeneity, network complexity, etc., make it difficult to offer a systematized coverage on CPS security in an industrial environment. This work considers the security perspective of I-CPSs, and offers a decade-wide survey including different vulnerabilities, attacks, CPS components, and various other aspects. The comparative year-wise analysis of the existing works w.r.t objective, approach referred, testbed used and derived inference, is also presented over a decade. Additionally, the work details different security issues and research challenges present in I-CPS. This work attempts to offer a concise and precise literature study focused on the state-of-the-art I-CPS security. This work also encourages the young researchers to explore the wide possibilities present in this emerging field.

Confidentiality attacks against encrypted control systems

ABSTRACT Encrypted control systems were introduced to enhance the security of cyber-physical systems, which outsource control action computations to a third-party platform. To protect the confidentiality of the transmitted data, homomorphic encryption schemes are particularly appealing for their capability of allowing computations on encrypted data. By considering the popular ElGamal and Paillier encryption schemes, this paper shows that encrypted control systems are vulnerable to attackers leveraging the inherently small domains of the plaintext data in control systems and the randomisation process required to make the utilised ciphers semantically secure. Finally, we present some countermeasures to defend against these attacks.

Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach.

False alerts due to misconfigured or compromised intrusion detection systems (IDS) in industrial control system (ICS) networks can lead to severe economic and operational damage. However, research using deep learning to reduce false alerts often requires the physical and cyber sensor data to be trustworthy. Implicit trust is a major problem for artificial intelligence or machine learning (AI/ML) in cyber-physical system (CPS) security, because when these solutions are most urgently needed is also when they are most at risk (e.g., during an attack). To address this, the Inter-Domain Evidence theoretic Approach for Inference (IDEA-I) is proposed that reframes the detection problem as how to make good decisions given uncertainty. Specifically, an evidence theoretic approach leveraging Dempster–Shafer (DS) combination rules and their variants is proposed for reducing false alerts. A multi-hypothesis mass function model is designed that leverages probability scores obtained from supervised-learning classifiers. Using this model, a location-cum-domain-based fusion framework is proposed to evaluate the detector’s performance using disjunctive, conjunctive, and cautious conjunctive rules. The approach is demonstrated in a cyber-physical power system testbed, and the classifiers are trained with datasets from Man-In-The-Middle attack emulation in a large-scale synthetic electric grid. For evaluating the performance, we consider plausibility, belief, pignistic, and general Bayesian theorem-based metrics as decision functions. To improve the performance, a multi-objective-based genetic algorithm is proposed for feature selection considering the decision metrics as the fitness function. Finally, we present a software application to evaluate the DS fusion approaches with different parameters and architectures.

Design Hands-on Lab Exercises for Cyber-physical Systems Security Education

The integration of cyber-physical systems (CPS) has been extremely advantageous to society, it merges the attention of cybersecurity for vehicles as a timely concern as a matter of public and individual. The failure of any vehicle system could have a serious impact on vehicle control and cause undesired consequences. With the growing demand for security in CPS, there are few hands-on labs/modules available for training current students, future engineers, or IT professionals to understand cybersecurity in CPS. This study describes the execution of a free security testbed to replicate a vehicle’s network system and the implementation of this testbed via hands-on lab designed to introduce concepts of vehicle control systems. The hands-on lab simulates insider threat scenarios where students had to use can-utils toolkits and SavvyCAN to send, modify, and capture the network packet and exploit the system vulnerability threats such as replay attacks and fuzzing attacks on the vehicle system. We conducted a case study with 21 university-level students, and all students completed the hands-on lab, pretest, posttest, and a satisfaction survey as part of a non-graded class assignment. The experimental results show that most students were not familiar with cyber-physical systems and vehicle control systems and never had the chance to do any hands-on lab in this field before. Furthermore, students reported that the hands-on lab helped them learn about CAN-bus and rated high scores for enjoyment. We discussed the design of an affordable tool to teach about vehicle control systems and proposed directions for future work.

Data Mining for the Security of Cyber Physical Systems Using Deep-Learning Methods

Cyber Physical Systems (CPSs) have become widely popular in recent years, and their applicability have been growing exponentially. A CPS is an advanced system that incorporates a computation unit along with a hardware unit, allowing for computing processes to interact with the physical world. However, this increased usage has also led to the security concerns in them, as they allow potential attack vendors to exploit the possibilities of committing misconduct for their own benefit. It is of paramount importance that these systems have comprehensive security mechanisms to mitigate these security threats. A typical attack vector for a CPS is malicious data supplied by compromised sensors that are part of the CPSs. To combat this attack vector, many systems are secured through fault tolerance, including methods such as checkpointing to recover the system. Looking at the diverse nature of attacks and their ever growing complexities, traditional security approaches may not counter them efficiently, which creates a vacuum to be filled with sophisticated state-of-the-art techniques. In this paper, Deep Learning methods such as autoencoders, and Support Vector Machines are proposed to secure CPSs against these attacks. The networks in these applied methods are trained with a normal data profile devoid of any malicious data. Data collected from the system’s sensors at specified intervals is used to form a data series and input to the neural networks. The networks compare and analyze new data to the normal profile to detect anomalies, if there is any. In the presence of anomalous data, the networks generate corrective action(s) for these sensors and the physical states they are recording. Through detection of anomalies, effective security of CPSs may be improved in addition to providing protection for the sensors. Moreover, the proposed method of securing CPSs opens up the possibility of further research by showcasing the applicability of neural networks in securing CPSs.

Critical Systems Protection (CSP): The US Secret Service’s Tactical Cyber Capability for Securing Protectees

This paper examines a largely unknown cybersecurity function within the United States Secret Service (USSS), that is separate and apart from its traditional cybersecurity mission in financial crime investigations. This program - Critical Systems Protection (CSP) – is instead focused on the Secret Service’s protective mission and analyzes and secures the cyber environments of facilities and locations visited by the agency’s protectees. Exploiting publicly available documents about CSP, this paper will document this unique organization that combines some of the highest profile issues in cybersecurity – operational and tactical level cybersecurity efforts, and the security of cyber-physical systems. Drawing on open-source information and government documents, this paper sketches out the CSP mission, its partnerships with other agencies and organizations, and some of the tools and other capabilities it has developed.

Diversity-by-Design for Dependable and Secure Cyber-Physical Systems: A Survey

Diversity-based security approaches have been studied for several decades since the 1970's. The concept of diversity-by-design emerged in the 1980's and, since then, diversity-based system design research has been explored to build more secure and dependable systems. In this work, we are particularly interested in providing an in-depth, comprehensive survey of existing diversity-based approaches, insights, and future work directions for those who want to conduct research on developing secure and dependable cyber-physical systems (CPSs) using diversity as a system design feature. To be specific, this survey paper provides: (i) The common concept of diversity based on a multidisciplinary study of diversity from nine different fields along with the historical evolution of diversity-by-design for security; (ii) The design principles of diversity-based approaches; (iii) The key benefits and caveats of using diversity-by-design; (iv) The key concerns of CPS environments in introducing diversity-by-design; (v) A variety of existing diversity-based approaches based on five different classifications; (vi) The types of attacks mitigated by existing diversity-based approaches; (vii) The overall trends of evaluation methodologies used in diversity-based approaches, in terms of metrics, datasets, and testbeds; and (viii) The insights, lessons, and gaps identified from this extensive survey.

Event-Based Fault Diagnosis of Networked Discrete Event Systems

The problem of fault diagnosis has become an active research topic in Discrete Event Systems, which can be applied into many different areas, such as communication networks, process control, flexible manufacturing systems, software engineering, etc. This brief explores the effect of communication delays on the event-based fault diagnosis in networked discrete event systems (NDESs), which can be modeled by finite state automata. Firstly, the framework of NDESs is introduced, and a novel concept of networked diagnosability ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Net$ </tex-math></inline-formula> -diagnosability) is formally defined. Subsequently, the necessary and sufficient conditions for the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Net$ </tex-math></inline-formula> -diagnosability in the partially observable outputs are provided, and the corresponding <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Net$ </tex-math></inline-formula> -diagnosability can be verified by an effective diagnoser, which can be obtained by a constructed algorithm. Finally, an illustrative example is given to demonstrate the proposed method. The current results about networked diagnosability will be conducive to maintain the information security in the actual cyber-physical systems.