Conventional approaches to analyzing industrial control systems have relied on either white-box analysis or black-box fuzzing. However, white-box methods rely on sophisticated domain expertise, while black-box methods suffers from state explosion and thus scales poorly when analyzing real ICS involving a large number of sensors and actuators. To address these limitations, we propose XAI-based gray-box fuzzing, a novel approach that leverages explainable AI and machine learning modeling of ICS to accurately identify a small set of actuators critical to ICS safety, which result in significant reduction of state space without relying on domain expertise. Experiment results show that our method accurately explains the ICS model and significantly speeds-up fuzzing by 64x when compared to conventional black-box methods.

Wireless Sensor Networks (WSNs) play a pivotal role in critical applications, ranging from industrial control systems to healthcare monitoring. As these networks become increasingly integrated into our daily lives, understanding their energy consumption behavior is paramount for achieving sustainability and resilience. This paper delves into the intricate relationship between energy consumption patterns in WSNs and their security implications within critical contexts. We commence by conducting a comprehensive analysis of energy consumption behavior in WSNs, considering factors such as data transmission, node mobility, and sensing activities. Through empirical studies and simulations, we identify key parameters influencing energy utilization and establish a foundation for further investigation. Building upon this understanding, we explore the security impacts associated with the energy profile of WSNs operating in critical environments. We address potential vulnerabilities arising from compromised nodes due to energy depletion, communication constraints, and malicious attacks. By examining these security challenges, we highlight the urgency of developing robust solutions to ensure the reliability and integrity of WSNs in critical applications. In response to these challenges, we propose mitigation strategies that synergistically address both energy consumption and security concerns. Our approach based on security information and event management with deep learning security use case algorithms for impact mitigation. These strategies aim to enhance the overall sustainability and security of WSNs, ensuring their continued functionality in demanding and sensitive environments. In conclusion, this paper provides a comprehensive overview of the intricate interplay between energy consumption behavior and security impacts in WSNs within critical contexts. Our findings underscore the need for holistic approaches that integrate energy-awareness and security measures to fortify the resilience of WSNs, fostering their sustainable deployment in critical applications.

Even with the rise of cyberattacks on high-value systems, we still do not see widespread adoption of intrusion-tolerant replication protocols, despite their long history in the research community and potential to support the needed resiliency. A key barrier is that deploying and managing intrusion-tolerant systems in practice requires substantial investment in additional physical infrastructure, as well as specialized technical expertise. In this work, we address this gap by designing a hybrid management model: while the system operator manages their application, a service provider hosts and manages the intrusion-tolerant replication service using cloud infrastructure. We develop the protocols to support this system architecture, without revealing application state, algorithms, or client information to the cloud provider, even when application servers are compromised. We implement and evaluate our approach in the context of an industrial control system and show that it meets the system s performance and resilience requirements.

The innovation introduced by connectivity brings about significant changes in the industrial environment leading to the fourth industrial revolution, known as Industry 4.0. However, the integration and connectivity between industrial systems have significantly increased the risks and cyberattack surfaces. Nowadays, Virtualization is added to the security field to provide maximum protection against toxic attacks at minimum costs. Combining paradigms such as Software Defined Networking (SDN), and Network Function Virtualization (NFV) can improve virtualization performance through Openness (unified control of heterogeneous hardware and software resources), Flexibility (remote management and rapid response to changing demands), and Scalability (a faster cycle of innovative services deployment). The present paper proposes a Virtualized Security for Industry 4.0 (ViSI4.0), based on both SDN and Network Security Function Virtualisation (NSFV), to prevent attacks on Cyber-Physical System (CPS). Since industrial devices are limited in memory and processing, vNSFs are deployed as Docker containers. We conducted experiments to evaluate the performances of IIoT applications when using virtualized security services. Results showed that many real-time IIoT applications are still within their latency tolerance range. However, the additional delays introduced by virtualization have an impact on IIoT applications with very strict delays.

In Industry 4.0, the Digital twin has been widely used in industrial activities. However, the data-driven industry is placing a higher demand on digital twins, especially for the secure sharing and management of data throughout the lifecycle. As a distributed ledger technology, Blockchain is well suited to address these challenges. Unfortunately, current blockchain-based digital twin lifecycle management does not focus on data processing after the retirement stage. In this paper, we propose BDTwins, a blockchain-based digital twin lifecycle management framework, which is built based on our proposed 7D model. In this framework, we make innovative use of Non-Fungible Tokens (NFT) to process the data in the recovery stage of the digital twin. This method solves digital intellectual property disputes and inherits digital twin knowledge completely and stably after the destruction of physical entities. In addition, BDTwins has designed a fine-grained hierarchical access control policy to enable secure data sharing among stakeholders. And solves the performance bottleneck of traditional single-chain blockchain architecture by utilizing directed acyclic graph (DAG) blockchain and off-chain distributed storage. Finally, we implement a general blockchain-based digital twin case using smart contract technology to demonstrate our proposed digital twin lifecycle management framework.

Industrial control systems (ICSs) and supervisory control and data acquisition (SCADA) are frequently used and are essential to the operation of vital infrastructure such as oil and gas pipelines, power plants, distribution grids, and airport control towers. However, these systems confront a number of obstacles and risks that can jeopardize their safety and reliability, including communication failures, cyber-attacks, environmental hazards, and human errors. How can ensure that SCADA systems are both effective and secure? The oil and gas industry literature needs to include an analysis of the underpinning design process. Available research fails to offer appropriate direction for a methodical technique or modeling language that enables trust-based study of ICS and SCADA systems. The most pressing challenges include attaining trust by design in ICS and SCADA, as well as methodically implementing trust design into the development process from the beginning of the system s life cycle. This paper presents the design of a modern ICS and SCADA system for the oil and gas industries utilizing model-based systems engineering (MBSE) approaches. ICS and SCADA concepts and definitions are presented, and ICS and SCADA are examined using comprehensive architectural artifacts. By extending the SysML diagrams to trust ICS, SCADA, and UML diagrams, we showcase the usefulness of the MBSE method.

The construction of traditional industrial networks poses challenges in cybersecurity, a sindus-tries are increasingly becoming more interconnected for management purposes. In this study, we analyzed events related to the insertion of the Zero Trust approach in industrial control systems. In a simulated test environment, we investigate how these systems respond to cyberattacks commonly observed in industrial scenarios. The results aim to identify potential benefits that Zero Trust policies can offer to industrial control systems vulnerable to cyber-attacks.

For modern industrial automation and control systems (IACS), it is a cybersecurity risk that provokes the most growing anxiety among other potential hazards. In order to manage the risk efficiently, a risk assessment is necessary. A standard CIA approach explores the confidentiality, integrity, and availability properties of assets. However, for IACS dealing with critical infrastructures, it is more crucial to investigate separately the availability part of the risk. Moreover, not assets but functions are particularly important. One of the major problems arising during the assessment is how to assign values for the availability property of IACS functions. For establishing the CIA values, techniques related to confidentiality and integrity seem to be quite evident and make just a minor issue to develop and employ. However, methods for assessing the availability property happen to be not obvious and not widely used. The article presents a metric helpful for the availability valuation. Inherently constructed to be applicable particularly to functions, not to assets, the metric will be found especially effective for IACS. Essentially based on delay as a measure, the metric is proved to be conformant to the IEC 62443 series availability interpretation and the general requirements for the cybersecurity metrics. For the metric to be accurately calculated, the availability reference model, dependency theory, and a theory of deterministic queuing systems Network calculus are proposed to be utilized. Applying Network calculus to the metric calculation, the article reveals that this problem can be reduced to the problem of obtaining sets of service curves.

Advanced persistent threat (APT) attack is one of the most serious threats to power system cyber security. ATT\&CK framework integrates the known historical and practical APT attack tactics and techniques to form a general language for describing hacker behavior and an abstract knowledge base framework for hacker attacks. Combined with the ATT\&CK for ICS framework, this paper combed the known attack techniques used by viruses or hacker groups aimed at cyberattacks on infrastructure, especially power systems. Then found the corresponding mitigations for each attack technique, and merged them. Next, we listed the high frequency and important mitigations for reference. At last, we proposed a cyber security defense model suitable for ICS to provide a reference for security teams on how to apply ATT\&ck; other similar cyberattack frameworks.

Advanced Persistent Threats (APTs) have been a major challenge in securing both Information Technology (IT) and Operational Technology (OT) systems. APT is a sophisticated attack that masquerade their actions to navigates around defenses, breach networks, often, over multiple network hosts and evades detection. It also uses “low-and-slow” approach over a long period of time. Resource availability, integrity, and confidentiality of the operational cyber-physical systems (CPS) state and control is highly impacted by the safety and security measures in place. A framework multi-stage detection approach termed “APT$_\textrmDASAC$” to detect different tactics, techniques, and procedures (TTPs) used during various APT steps is proposed. Implementation was carried out in three stages: (i) Data input and probing layer - this involves data gathering and pre-processing, (ii) Data analysis layer; applies the core process of “APT$_\textrmDASAC$” to learn the behaviour of attack steps from the sequence data, correlate and link the related output and, (iii) Decision layer; the ensemble probability approach is utilized to integrate the output and make attack prediction. The framework was validated with three different datasets and three case studies. The proposed approach achieved a significant attacks detection capability of 86.36\% with loss as 0.32\%, demonstrating that attack detection techniques applied that performed well in one domain may not yield the same good result in another domain. This suggests that robustness and resilience of operational systems state to withstand attack and maintain system performance are regulated by the safety and security measures in place, which is specific to the system in question.

Most proposals for securing control systems are heuristic in nature, and while they increase the protection of their target, the security guarantees they provide are unclear. This paper proposes a new way of modeling the security guarantees of a Cyber-Physical System (CPS) against arbitrary false command attacks. As our main case study, we use the most popular testbed for control systems security. We first propose a detailed formal model of this testbed and then show how the original configuration is vulnerable to a single-actuator attack. We then propose modifications to the control system and prove that our modified system is secure against arbitrary, single-actuator attacks.

The increasing complexity and interconnectedness of Industrial Control Systems (ICSs) necessitate the integration of safety and security measures. Ensuring the protection of both personnel and critical assets has become a necessity. As a result, an integrated risk assessment approach is essential to comprehensively identify and address potential hazards and vulnerabilities. However, the data sources needed for an integrated risk assessment comes in many forms. In this context, Automation Markup Language (AutomationML or AML) emerges as a valuable solution to facilitate data exchange and integration in the risk assessment process. The benefits of utilizing AML include improved interoperability, enhanced documentation, and seamless collaboration between stakeholders. A model, filled with information relevant to integrated risk assessment, is developed to illustrate the effectiveness of AML. Ultimately, this paper showcases how AML serves as a valuable information model in meeting the growing need for comprehensive safety and security risk assessment in ICSs.

Satellite technologies are used for both civil and military purposes in the modern world, and typical applications include Communication, Navigation and Surveillance (CNS) services, which have a direct impact several economic, social and environmental protection activity. The increasing reliance on satellite services for safety-of-life and mission-critical applications (e.g., transport, defense and public safety services) creates a severe, although often overlooked, security problem, particularly when it comes to cyber threats. Like other increasingly digitized services, satellites and space platforms are vulnerable to cyberattacks. Thus, the existence of cybersecurity flaws may pose major threats to space-based assets and associated key infrastructure on the ground. These dangers could obstruct global economic progress and, by implication, international security if they are not properly addressed. Mega-constellations make protecting space infrastructure from cyberattacks much more difficult. This emphasizes the importance of defensive cyber countermeasures to minimize interruptions and ensure efficient and reliable contributions to critical infrastructure operations. Very importantly, space systems are inherently complex Cyber-Physical System (CPS) architectures, where communication, control and computing processes are tightly interleaved, and associated hardware/software components are seamlessly integrated. This represents a new challenge as many known physical threats (e.g., conventional electronic warfare measures) can now manifest their effects in cyberspace and, vice-versa, some cyber-threats can have detrimental effects in the physical domain. The concept of cyberspace underlies nearly every aspect of modern society s critical activities and relies heavily on critical infrastructure for economic advancement, public safety and national security. Many governments have expressed the desire to make a substantial contribution to secure cyberspace and are focusing on different aspects of the evolving industrial ecosystem, largely under the impulse of digital transformation and sustainable development goals. The level of cybersecurity attained in this framework is the sum of all national and international activities implemented to protect all actions in the cyber-physical ecosystem. This paper focuses on cybersecurity threats and vulnerabilities in various segments of space CPS architectures. More specifically, the paper identifies the applicable cyber threat mechanisms, conceivable threat actors and the associated space business implications. It also presents metrics and strategies for countering cyber threats and facilitating space mission assurance.

Understanding dynamic human behavior based on online video has many applications in security control, crime surveillance, sports, and industrial IoT systems. This paper solves the problem of classifying video data recorded on surveillance cameras in order to identify fragments with instances of shoplifting. It is proposed to use a classifier that is a symbiosis of two neural networks: convolutional and recurrent. The convolutional neural network is used for extraction of features from each frame of the video fragment, and the recurrent network for processing the temporal sequence of processed frames and subsequent classification.

Neural Network Security - With the development of computer and network technology, industrial control systems are connecting with the Internet and other public networks in various ways, viruses, trojans and other threats are spreading to industrial control systems, industrial control system information security issues are becoming increasingly prominent. Under this background, it is necessary to construct the network security evaluation model of industrial control system based on the safety evaluation criteria and methods, and complete the safety evaluation of the industrial control system network according to the design scheme. Based on back propagation (BP) neural network’s evaluation of the network security status of industrial control system, this paper determines the number of neurons in BP neural network input layer, hidden layer and output layer by analyzing the actual demand, empirical equation calculation and experimental comparison, and designs the network security evaluation index system of industrial control system according to factors affecting industrial control safety, and constructs a safety rating table. Finally, by comparing the performance of BP neural network and multilinear regression to the evaluation of the network security status of industrial control system through experimental simulation, it can be found that BP neural network has higher accuracy for the evaluation of network security status of industrial control system.

Network Control Systems Security - Machine tool is known as the mother of industry. CNC machine tool is the embodiment of modern automatic control productivity. In the context of the rapid development of the industrial Internet, a large number of equipment and systems are interconnected through the industrial Internet, realizing the flexible adaptation from the supply side to the demand side. As the a typical core system of industrial Internet, CNC system is facing the threat of industrial virus and network attack. The problem of information security is becoming more and more prominent. This paper analyzes the security risks of the existing CNC system from the aspects of terminal security, data security and network security. By comprehensively using the technologies of data encryption, identity authentication, digital signature, access control, secure communication and key management, this paper puts forward a targeted security protection and management scheme, which effectively strengthens the overall security protection ability.

Network Control Systems Security - With the development of computer and network technology, industrial control systems are connecting with the Internet and other public networks in various ways, viruses, trojans and other threats are spreading to industrial control systems, industrial control system information security issues are becoming increasingly prominent. Under this background, it is necessary to construct the network security evaluation model of industrial control system based on the safety evaluation criteria and methods, and complete the safety evaluation of the industrial control system network according to the design scheme. Based on back propagation (BP) neural network’s evaluation of the network security status of industrial control system, this paper determines the number of neurons in BP neural network input layer, hidden layer and output layer by analyzing the actual demand, empirical equation calculation and experimental comparison, and designs the network security evaluation index system of industrial control system according to factors affecting industrial control safety, and constructs a safety rating table. Finally, by comparing the performance of BP neural network and multilinear regression to the evaluation of the network security status of industrial control system through experimental simulation, it can be found that BP neural network has higher accuracy for the evaluation of network security status of industrial control system.

Network Control Systems Security - With the development of industrial informatization, information security in the power production industry is becoming more and more important. In the power production industry, as the critical information egress of the industrial control system, the information security of the Networked Control System is particularly important. This paper proposes a construction method for an information security platform of Networked Control System, which is used for research, testing and training of Networked Control System information security.

Network on Chip Security - This paper designs a network security protection system based on artificial intelligence technology from two aspects of hardware and software. The system can simultaneously collect Internet public data and secret-related data inside the unit, and encrypt it through the TCM chip solidified in the hardware to ensure that only designated machines can read secret-related materials. The data edgecloud collaborative acquisition architecture based on chip encryption can realize the cross-network transmission of confidential data. At the same time, this paper proposes an edge-cloud collaborative information security protection method for industrial control systems by combining endaddress hopping and load balancing algorithms. Finally, using WinCC, Unity3D, MySQL and other development environments comprehensively, the feasibility and effectiveness of the system are verified by experiments.

Moving Target Defense - In the modern era, much of worldwide critical operations from a variety of different sectors are managed by industrial control systems (ICS). A typical ICS includes an extensive range of computerized devices, control systems, and networking appliances used to manage efficiently an industrial process across large geographical areas. ICS underpin sensitive and critical national infrastructures such as water treatment and energy production and transportation. The consequences of a successful attack against them can lead to shutting the infrastructure down which has major impacts such as production stoppages or safety implications for people, the environment, and assets. At the same time, running a process while the infrastructure is under attack or compromised also has safety implications, potentially catastrophic. This work-in-progress focuses on an adaptive approach, able to alter the defensive posture while providing assurances about operational capacity (or downgrading it) and safety. Our approach involves transforming policies from simply a means to enforce security requirements defined a priori, to adaptive objects that are capable to evolve in response to unfolding attacks. We use a case study of reconnaissance attacks and moving target defense as a means to realize such adaptive security policies.

Insider Threat - Compare to outside threats, insider threats that originate within targeted systems are more destructive and invisible. More importantly, it is more difficult to detect and mitigate these insider threats, which poses significant cyber security challenges to an industry control system (ICS) tightly coupled with today’s information technology infrastructure. Currently, power utilities rely mainly on the authentication mechanism to prevent insider threats. If an internal intruder breaks the protection barrier, it is hard to identify and intervene in time to prevent harmful damage. Based on the existing in-depth security defense system, this paper proposes an insider threat protection scheme for ICSs of power utilities. This protection scheme can conduct compliance check by taking advantage of the characteristics of its business process compliance and the nesting of upstream and downstream business processes. Taking the Advanced Metering Infrastructures (AMIs) in power utilities as an example, the potential insider threats of violation and misoperation under the current management mechanism are identified after the analysis of remote charge control operation. According to the business process, a scheme of compliance check for remote charge control command is presented. Finally, the analysis results of a specific example demonstrate that the proposed scheme can effectively prevent the consumers’ power outage due to insider threats.

Industrial Control Systems - The power industrial control system is an important part of the national critical Information infrastructure. Its security is related to the national strategic security and has become an important target of cyber attacks. In order to solve the problem that the vulnerability detection technology of power industrial control system cannot meet the requirement of non-destructive, this paper proposes an industrial control vulnerability analysis technology combined with dynamic and static analysis technology. On this basis, an industrial control non-destructive vulnerability detection system is designed, and a simulation verification platform is built to verify the effectiveness of the industrial control non-destructive vulnerability detection system. These provide technical support for the safety protection research of the power industrial control system.

Industrial Control Systems - Machine tool is known as the mother of industry. CNC machine tool is the embodiment of modern automatic control productivity. In the context of the rapid development of the industrial Internet, a large number of equipment and systems are interconnected through the industrial Internet, realizing the flexible adaptation from the supply side to the demand side. As the a typical core system of industrial Internet, CNC system is facing the threat of industrial virus and network attack. The problem of information security is becoming more and more prominent. This paper analyzes the security risks of the existing CNC system from the aspects of terminal security, data security and network security. By comprehensively using the technologies of data encryption, identity authentication, digital signature, access control, secure communication and key management, this paper puts forward a targeted security protection and management scheme, which effectively strengthens the overall security protection ability.

Industrial Control Systems - Currently, risk assessment of industrial control systems is static and performed manually. With the increased convergence of operational technology and information technology, risk assessment has to incorporate a combined safety and security analysis along with their interdependency. This paper investigates the data inputs required for safety and security assessments, also if the collection and utilisation of such data can be automated. A particular focus is put on integrated assessment methods which have the potential for automation. In case the overall process to identify potential hazards and threats and analyze what could happen if they occur can be automated, manual efforts and cost of operation can be reduced, thus also increasing the overall performance of risk assessment.

Industrial Control Systems - With the introduction of the national “carbon peaking and carbon neutrality” strategic goals and the accelerated construction of the new generation of power systems, cloud applications built on advanced IT technologies play an increasingly important role in meeting the needs of digital power business. In view of the characteristics of the current power industrial control system operation support cloud platform with wide coverage, large amount of log data, and low analysis intelligence, this paper proposes a cloud platform network security behavior audit method based on FP-Growth association rule algorithm, aiming at the uniqueness of the operating data of the cloud platform that directly interacts with the isolated system environment of power industrial control system. By using the association rule algorithm to associate and classify user behaviors, our scheme formulates abnormal behavior judgment standards, establishes an automated audit strategy knowledge base, and improves the security audit efficiency of power industrial control system operation support cloud platform. The intelligent level of log data analysis enables effective discovery, traceability and management of internal personnel operational risks.