Biblio

A threat of eavesdropping display screen image of information device is caused by unintended EM leakage emanation. QR bar-code is capable of error correction, and its information is possibly read from a damaged screen image from EM leakage. A new design of QR bar-code proposed in this paper uses selected colors in consideration of correlation between the EM wave leakage and display color. Proposed design of QR bar-code keeps error correction of displayed image, and makes it difficult to read information on the eavesdropped image.

Quick response (QR) codes are currently used ubiq-uitously. Their interaction protocol design is initially unsecured. It forces users to scan QR codes, which makes it harder to differentiate a genuine code from a malicious one. Intruders can change the original QR code and make it fake, which can lead to phishing websites that collect sensitive data. The interaction model can be improved and made more secure by adding some modifications to the backend side of the application. This paper addresses the vulnerabilities of QR codes and recommends improvements in security design. Furthermore, two state-of-the-art algorithms, Digital Signature (DS) and Elliptic Curve Digital Signature (ECDS), are analytically compared to determine their strengths in QR code security.

This research presented a digital watermarking scheme using multi-level authentication for protecting QR code images in order to provide security and authenticity. This research focuses on the improved digital watermarking scheme for QR code security that can protect the confidentiality of the information stored in QR code images from the public. Information modification, malicious attack, and copyright violation may occur due to weak security and disclosure pattern of QR code. Digital watermarking can be a solution to reduce QR code imitation and increase QR code security and authenticity. The objectives of this research are to provide QR code image authentication and security, tamper localization, and recovery scheme on QR code images. This research proposed digital watermarking for QR code images based on multi-level authentication with Least Significant Bit (LSB) and SHA-256 hash function. The embedding and extracting watermark utilized region of Interest (ROI) and Region of Non-Interest (RONI) in the spatial domain for improving the depth and width of QR code application in the anti-counterfeiting field. The experiments tested the reversibility and robustness of the proposed scheme after a tempered watermarked QR code image. The experimental results show that the proposed scheme provides multi-level security, withstands tampered attacks and it provided high imperceptibility of QR code image.

Modbus is a popular industrial communication protocol supported by most automation devices. Despite its popularity, it is not a secure protocol because when it was developed, security was not a concern due to closed environments of industrial control systems. With the convergence of information technology and operational technology in recent years, the security of industrial control systems has become a serious concern. Due to the high availability requirements, it is not practical or feasible to do security experimentation of production systems. We present an implementation of cyber-physical systems with Modbus/TCP communication for real-time security testing. The proposed architecture consists of a process simulator, an IEC 61131-3 compliant programmable logic controller, and a human-machine interface, all communicating via Modbus/TCP protocol. We use Simulink as the process simulator. It does not have built-in support for the Modbus protocol. A contribution of the proposed work is to extend the functionality of Simulink with a custom block to enable Modbus communication. We use two case studies to demonstrate the utility of the cyber-physical system architecture. We can model complex industrial processes with this architecture, can launch cyber-attacks, and develop protection mechanisms.

To ensure a ship’s fully operational status in a wide spectrum of missions, as passenger transportation, international trade, and military activities, numerous interdependent systems are essential. Despite the potential critical consequences of misunderstanding or ignoring those interdependencies, there are very few documented approaches to enable their identification, representation, analysis, and use. From the cybersecurity point of view, if an anomaly occurs on one of the interdependent systems, it could eventually impact the whole ship, jeopardizing its mission success. This paper presents a proposal to identify the main dependencies of layers within and between generic ship’s functional blocks. An analysis of one of these layers, the platform systems, is developed to examine a naval cyber-physical system (CPS), the water management for passenger use, and its associated dependencies, from an intrinsic perspective. This analysis generates a three layers graph, on which dependencies are represented as oriented edges. Each abstraction level of the graph represents the physical, digital, and system variables of the examined CPS. The obtained result confirms the interest of graphs for dependencies representation and analysis. It is an operational depiction of the different systems interdependencies, on which can rely a cybersecurity evaluation, like anomaly detection and propagation assessment.

Naval vessels infrastructures are evolving towards increasingly connected and automatic systems. Such accelerated complexity boost to search for more adapted and useful navigation devices may be at odds with cybersecurity, making necessary to develop adapted analysis solutions for experts. This paper introduces a novel process to visualize and analyze naval Cyber-Physical Systems (CPS) using oriented graphs, considering operational constraints, to represent physical and functional connections between multiple components of CPS. Rapid prototyping of interconnected components is implemented in a semi-automatic manner by defining the CPS’s digital and physical systems as nodes, along with system variables as edges, to form three layers of an oriented graph, using the open-source Neo4j software suit. The generated multi-layer graph can be used to support cybersecurity analysis, like attacks simulation, anomaly detection and propagation estimation, applying existing or new algorithms.

Modern electric power systems are complex cyber-physical systems. The integration of traditional power and digital technologies result in interdependencies that need to be considered in risk analysis. In this paper we argue the need for analysis methods that can combine the competencies of various experts in a common analysis focusing on the overall system perspective. We report on our experiences on using the Vulnerability Analysis Framework (VAF) and bow-tie diagrams in a combined analysis of the power and cyber security aspects in a realistic case. Our experiences show that an extended version of VAF with increased support for interdependencies is promising for this type of analysis.

As a typical cyber-physical system, the power system utilizes advanced information and communication technologies to transmit crucial control signals in communication channels. However, many adversaries can construct false data injection attacks (FDIA) to circumvent traditional bad data detection and break the stability of the power grid. In this paper, we proposed a threat-maximizing FDIA model from the view of attackers. The proposed FDIA can not only circumvent bad data detection but can also cause a terrible fluctuation in the power system. Furthermore, in order to eliminate potential attack threats, the Spatio-temporal correlations of measurement matrices are considered. To extract the Spatio-temporal features, a data-driven detection method using a deep convolutional neural network was proposed. The effectiveness of the proposed FDIA model and detection are assessed by a simulation on the New England 39 bus system. The results show that the FDIA can cause a negative effect on the power system’s stable operation. Besides, the results reveal that the proposed FDIA detection method has an outstanding performance on Spatio-temporal features extraction and FDIA recognition.

Direct-access attacks were initially considered as un-realistic threats in cyber security because the attacker can more easily mount other non-computerized attacks like cutting a brake line. In recent years, some research into direct-access attacks have been conducted especially in the automotive field, for example, research on an attack method that makes the ECU stop functioning via the CAN bus. The problem with existing risk quantification methods is that direct-access attacks seem not to be recognized as serious threats. To solve this problem, we propose a new risk quantification method by applying vulnerability evaluation criteria and by setting metrics. We also confirm that direct-access attacks not recognized by conventional methods can be evaluated appropriately, using the case study of an automotive system as an example of a cyber-physical system.

Cyber-physical systems (CPS) and IoT systems are nowadays commonly designed as self-adaptive, endowing them with the ability to dynamically reconFigure to reflect their changing environment. This adaptation concerns also the security, as one of the most important properties of these systems. Though the state of the art on adaptivity in terms of security related to these systems can often deal well with fully anticipated situations in the environment, it becomes a challenge to deal with situations that are not or only partially anticipated. This uncertainty is however omnipresent in these systems due to humans in the loop, open-endedness and only partial understanding of the processes happening in the environment. In this paper, we partially address this challenge by featuring an approach for tackling access control in face of partially unanticipated situations. We base our solution on special kind of aspects that build on existing access control system and create a second level of adaptation that addresses the partially unanticipated situations by modifying access control rules. The approach is based on our previous work where we have analyzed and classified uncertainty in security and trust in such systems and have outlined the idea of access-control related situational patterns. The aspects that we present in this paper serve as means for application-specific specialization of the situational patterns. We showcase our approach on a simplified but real-life example in the domain of Industry 4.0 that comes from one of our industrial projects.

Industry 4.0 is now much more than just a buzzword. However, with the advancement of automation through digitization and softwarization of dedicated hardware, applications are also becoming more susceptible to random hardware errors in the calculation. This cyber-physical demonstrator uses a robotic application to show the effects that even single bit flips can have in the real world due to hardware errors. Using the graphical user interface including the human machine interface, the audience can generate hardware errors in the form of bit flips and see their effects live on the robot. In this paper we will be showing a new technology, the SIListra Safety Transformer (SST), that makes it possible to detect those kind of random hardware errors, which can subsequently make safety-critical applications more reliable.

Recent technological advances in developing intelligent telecommunication networks, ultra-compact bendable wireless transceiver chips, adaptive wearable sensors and actuators, and secure computing infrastructures along with the progress made in psychology and neuroscience for understanding neu-rocognitive and computational principles of human behavior combined have paved the way for a new field of research: Tactile Internet with Human-in-the-Loop (TaHiL). This emerging field of transdisciplinary research aims to promote next generation digitalized human-machine interactions in perceived real time. To achieve this goal, mechanisms and principles of human goal-directed multisensory perception and action need to be integrated into technological designs for breakthrough innovations in mobile telecommunication, electronics and materials engineering, as well as computing. This overview highlights key challenges and the frontiers of research in the new field of TaHiL. Revolutionizing the current Internet as a digital infrastructure for sharing visual and auditory information globally, the TaHiL research will enable humans to share tactile and haptic information and thus veridically immerse themselves into virtual, remote, or inaccessible real environments to exchange skills and expertise with other humans or machines for applications in medicine, industry, and the Internet of Skills.

Cyber-Physical Systems (CPS) suffer from extendable vulnerabilities due to the convergence of the physical world with the cyber world, which makes it victim to a number of sophisticated cyber-attacks. The motives behind such attacks range from criminal enterprises to military, economic, espionage, political, and terrorism-related activities. Many governments are more concerned than ever with securing their critical infrastructure. One of the effective means of detecting threats and securing their infrastructure is the use of Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS). A number of studies have been conducted and proposed to assess the efficacy and effectiveness of IDS through the use of self-learning techniques, especially in the Industrial Control Systems (ICS) era. This paper investigates and analyzes the utilization of IDS systems and their proposed solutions used to enhance the effectiveness of such systems for CPS. The targeted data extraction was from 2011 to 2021 from five selected sources: IEEE, ACM, Springer, Wiley, and ScienceDirect. After applying the inclusion and exclusion criteria, 20 primary studies were selected from a total of 51 studies in the field of threat detection in CPS, ICS, SCADA systems, and the IoT. The outcome revealed the trends in recent research in this area and identified essential techniques to improve detection performance, accuracy, reliability, and robustness. In addition, this study also identified the most vulnerable target layer for cyber-attacks in CPS. Various challenges, opportunities, and solutions were identified. The findings can help scholars in the field learn about how machine learning (ML) methods are used in intrusion detection systems. As a future direction, more research should explore the benefits of ML to safeguard cyber-physical systems.

In-vehicle CAN (Controller Area Network) bus network does not have any network security protection measures, which is facing a serious network security threat. However, most of the intrusion detection solutions requiring extensive computational resources cannot be implemented in in- vehicle network system because of the resource constrained ECUs. To add additional hardware or to utilize cloud computing, we need to solve the cost problem and the reliable communication requirement between vehicles and cloud platform, which is difficult to be applied in a short time. Therefore, we need to propose a short-term solution for automobile manufacturers. In this paper, we propose a signature-based light-weight intrusion detection system, which can be applied directly and promptly to vehicle's ECUs (Electronic Control Units). We detect the anomalies caused by several attack modes on CAN bus from real-world scenarios, which provide the basis for selecting signatures. Experimental results show that our method can effectively detect CAN traffic related anomalies. For the content related anomalies, the detection ratio can be improved by exploiting the relationship between the signals.

Ransomware is one of the most serious threats which constitute a significant challenge in the cybersecurity field. The cybercriminals use this attack to encrypts the victim's files or infect the victim's devices to demand ransom in exchange to restore access to these files and devices. The escalating threat of Ransomware to thousands of individuals and companies requires an urgent need for creating a system capable of proactively detecting and preventing ransomware. In this research, a new approach is proposed to detect and classify ransomware based on three machine learning algorithms (Random Forest, Support Vector Machines , and Näive Bayes). The features set was extracted directly from raw byte using static analysis technique of samples to improve the detection speed. To offer the best detection accuracy, CF-NCF (Class Frequency - Non-Class Frequency) has been utilized for generate features vectors. The proposed approach can differentiate between ransomware and goodware files with a detection accuracy of up to 98.33 percent.

Cybersecurity has become an emerging challenge for business information management and critical infrastructure protection in recent years. Artificial Intelligence (AI) has been widely used in different fields, but it is still relatively new in the area of Cyber-Physical Systems (CPS) security. In this paper, we provide an approach based on Machine Learning (ML) to intelligent threat recognition to enable run-time risk assessment for superior situation awareness in CPS security monitoring. With the aim of classifying malicious activity, several machine learning methods, such as k-nearest neighbours (kNN), Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF), have been applied and compared using two different publicly available real-world testbeds. The results show that RF allowed for the best classification performance. When used in reference industrial applications, the approach allows security control room operators to get notified of threats only when classification confidence will be above a threshold, hence reducing the stress of security managers and effectively supporting their decisions.

Intrusion detection systems installed on the information security devices that control the internal and external perimeter of the demilitarized zones are not able to detect the vulnerability of ZeroLogon after the successful penetration of the intruder into the zone. Component solution for ZeroLogon control is offered. The paper presents the research results of the capabilities for built-in Active Directory audit mechanisms and open source intrusion detection/prevention systems, which allow identification of the critical vulnerability CVE-2020-1472. These features can be used to improve the quality of cyber-physical systems management, to perform audits, as well as to check corporate domains for ZeroLogon vulnerabilities.

The papers in this special section focus on resilient control in large-scae networked cyber-physical systems. These papers deal with the opportunities offered by these emerging technologies to mitigate undesired phenomena arising when intentional jamming and false data injections, categorized as cyber-attacks, infer communication channels. Recent advances in sensing, communication and computing have open the door to the deployment of largescale networks of sensors and actuators that allow fine-grain monitoring and control of a multitude of physical processes and infrastructures. The appellation used by field experts for these paradigms is Cyber-Physical Systems (CPS) because the dynamics among computers, networking media/resources and physical systems interact in a way that multi-disciplinary technologies (embedded systems, computers, communications and controls) are required to accomplish prescribed missions. Moreover, they are expected to play a significant role in the design and development of future engineering applications such as smart grids, transportation systems, nuclear plants and smart factories.

Recent cyber attacks on the power grid have been of increasing complexity and sophistication. In order to understand the impact of cyber-attacks on the power system resiliency, it is important to consider an holistic cyber-physical system specially with increasing industrial automation. In this study, device-level resilience properties of the various controllers and their impact on the microgrid resiliency is studied. In addition, a cyber-physical resiliency metric considering vulnerabilities, system model, and device-level properties is proposed. Resiliency is defined as the system ability to provide energy to critical loads even in extreme contingencies and depends on system ability to withstand, predict, and recover. A use case is presented inspired by the recent Ukraine cyber-attack. A use case has been presented to demonstrate application of the developed cyber-physical resiliency metric to enhance situational awareness of the operator, and enable better proactive or remedial control actions to improve resiliency.

Recent cyber attacks on the power grid have been of increasing complexity and sophistication. In order to understand the impact of cyber-attacks on the power system resiliency, it is important to consider an holistic cyber-physical system specially with increasing industrial automation. In this work, device level resilience properties of the various controllers and their impact on the microgrid resiliency is studied. In addition, a cyber-physical resiliency metric considering vulnerabilities, system model, and device level properties is proposed. A use case is presented inspired by the recent Ukraine cyber-attack. A use case has been presented to demonstrate application of the developed cyber-physical resiliency metric to enhance situational awareness of the operator, and enable better control actions to improve resiliency.

As the world becomes more dependent on connected cyber-physical systems, the cybersecurity workforce must adapt to meet these growing needs. The authors present the notion of a cyber-resiliency workforce to prepare the next generation of cybersecurity professionals.

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.

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.

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.