Biblio

In the smart grid, the sharing of power data among various energy entities can make the data play a higher value. However, there may be unauthorized access while sharing data, which makes many entities unwilling to share their data to prevent data leakage. Based on blockchain and ABAC (Attribute-based Access Control) technology, this paper proposes an access control scheme, so that users can achieve fine-grained access control of their data when sharing them. The solution uses smart contract to achieve automated and reliable policy evaluation. IPFS (Interplanetary File System) is used for off-chain distributed storage to share the storage pressure of blockchain and guarantee the reliable storage of data. At the same time, all processes in the system are stored in the blockchain, ensuring the accountability of the system. Finally, the experiment proves the feasibility of the proposed scheme.

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.

This paper proposes a control flow integrity checking method based on the LBR register: through an analysis of the static target program loaded binary modules, gain function attributes such as borders and build the initial transfer of legal control flow boundary, real-time maintenance when combined with the dynamic execution of the program flow of control transfer record, build a complete profile control flow transfer security; Get the call location of /bin/sh or system() in the program to build an internal monitor for control-flow integrity checks. In the process of program execution, on the one hand, the control flow transfer outside the outline is judged as the abnormal control flow transfer with attack threat; On the other hand, abnormal transitions across the contour are picked up by an internal detector. In this method, by identifying abnormal control flow transitions, attacks are initially detected before the attack code is executed, while some attacks that bypass the coarse-grained verification of security profile are captured by the refined internal detector of control flow integrity. This method reduces the cost of control flow integrity check by using the safety profile analysis of coarse-grained check. In addition, a fine-grained shell internal detector is inserted into the contour to improve the safety performance of the system and achieve a good balance between performance and efficiency.

Critical infrastructures such as the electricity grid can be severely impacted by cyber-attacks on its supply chain. Hence, having a robust cybersecurity infrastructure and management system for the electricity grid is a high priority. This paper proposes a cyber-security protocol for defense against man-in-the-middle (MiTM) attacks to the supply chain, which uses encryption and cryptographic multi-party authentication. A cyber-physical simulator is utilized to simulate the power system, control system, and security layers. The correctness of the attack modeling and the cryptographic security protocol against this MiTM attack is demonstrated in four different attack scenarios.

The fourth industrial revolution has led to the rapid development of industrial control systems. While the large number of industrial system devices connected to the Internet provides convenience for production management, it also exposes industrial control systems to more attack surfaces. Under the influence of multiple attack surfaces, sensitive data leakage has a more serious and time-spanning negative impact on industrial production systems. How to quickly locate the source of information leakage plays a crucial role in reducing the loss from the attack, so there are new requirements for tracing sensitive data in industrial control information systems. In this paper, we propose a digital watermarking traceability scheme for sensitive data in industrial control systems to address the above problems. In this scheme, we enhance the granularity of traceability by classifying sensitive data types of industrial control systems into text, image and video data with differentiated processing, and achieve accurate positioning of data sources by combining technologies such as national secret asymmetric encryption and hash message authentication codes, and mitigate the impact of mainstream watermarking technologies such as obfuscation attacks and copy attacks on sensitive data. It also mitigates the attacks against the watermarking traceability such as obfuscation attacks and copy attacks. At the same time, this scheme designs a data flow watermark monitoring module on the post-node of the data source to monitor the unauthorized sensitive data access behavior caused by other attacks.

Cyber-security incidents have grown significantly in modern networks, far more diverse and highly destructive and disruptive. According to the 2021 Cyber Security Statistics Report [1], cybercrime is up 600% during this COVID pandemic, the top attacks are but are not confined to (a) sophisticated phishing emails, (b) account and DNS hijacking, (c) targeted attacks using stealth and air gap malware, (d) distributed denial of services (DDoS), (e) SQL injection. Additionally, 95% of cyber-security breaches result from human error, according to Cybint Report [2]. The average time to identify a breach is 207 days as per Ponemon Institute and IBM, 2022 Cost of Data Breach Report [3]. However, various preventative controls based on cyber-security risk estimation and awareness results decrease most incidents, but not all. Further, any incident detection delay and passive actions to cyber-security incidents put the organizational assets at risk. Therefore, the cyber-security incident management system has become a vital part of the organizational strategy. Thus, the authors propose a framework to converge a "Security Operation Center" (SOC) and a "Network Operations Center" (NOC) in an "Integrated Network Security Operation Center" (INSOC), to overcome cyber-threat detection and mitigation inefficiencies in the near-real-time scenario. We applied the People, Process, Technology, Governance and Compliance (PPTGC) approach to develop the INSOC conceptual framework, according to the requirements we formulated for its operation [4], [5]. The article briefly describes the INSOC conceptual framework and its usefulness, including the central area of the PPTGC approach while designing the framework.

In this paper, the malicious code is run in the sandbox in a safe and controllable environment, the API sequence is deduplicated by the idea of the longest common subsequence, and the CNN and Bi-LSTM are integrated to process and analyze the API sequence. Compared with the method, the method using deep learning can have higher accuracy and work efficiency.

The article deals with the issues of improving the quality of corporate information systems functioning and ensuring the information security of financial organizations that have a complex structure and serve a significant number of customers. The formation of the company's informational system and its integrated information security system is studied based on the process approach, methods of risk management and quality management. The risks and threats to the security of the informational system functioning and the quality of information support for customer service of a financial organization are analyzed. The methods and tools for improving the quality of information services and ensuring information security are considered on the example of an organization for social insurance. Recommendations are being developed to improve the quality of the informational system functioning in a large financial company.

For the huge charging demands of numerous electric vehicles (EVs), coordinated charging is increasing in power grid. However, since connected with public networks, the coordinated charging control system is in a low-level cyber security and greatly vulnerable to malicious attacks. This paper investigates the malicious mode attack (MMA), which is a new cyber-attack pattern that simultaneously attacks massive EV charging piles to generate continuous sinusoidal power disturbance with the same frequency as the poorly-damped wide-area electromechanical mode. Thereby, high amplitude forced oscillations are stimulated by MMA, which seriously threats the stability of power systems and the power supply of charging stations. The potential threat of MMA is clarified by investigating the vulnerability of the IoT-based coordinated charging load control system, and an MMA process like Mirai is pointed out as an example. An MMA model is established for impact analysis. A hardware test platform is built for the verification of the MMA model. Test result verified the existence of MMA and the accuracy of the MMA model.

Managing and storing big data is non-trivial for traditional relational databases (RDBMS). Therefore, the NoSQL (Not Only SQL) database management system emerged. It is ca-pable of handling the vast amount and the heterogeneity of data. In this research, we are interested in one of its trending types, the graph database, namely, the Directed Property Graph (DPG). This type of database is powerful in dealing with complex relationships (\$\textbackslashmathrme.\textbackslashmathrmg\$., social networks). However, its sen-sitive and private data must be protected against unauthorized access. This research proposes a security model that aims at exploiting and combining the benefits of Access Control, View-Based, and Query-Rewriting approaches. This is a novel combination for securing DPG.

As a new generation of power grid system, smart grid and smart meter conduct two-way communication to realize the intelligent collection, monitoring and dispatching of user power data, so as to achieve a safer, stable, reliable and efficient power grid environment. With the vigorous development of power grid, there are also some security and privacy problems. This paper uses Paillier homomorphic encryption algorithm and role-based access control strategy to ensure the privacy security in the process of multi-dimensional aggregation, data transmission and sharing of power data. Applying the characteristics of blockchain technology such as decentralization, non tampering and traceability to the smart grid can effectively solve the privacy and security problems of power data transmission and sharing in the smart grid. This paper compares Paillier encryption algorithm with PPAR algorithm and SIAHE algorithm in terms of encryption mechanism, number of aggregators and computational complexity respectively. The results show that Paillier homomorphic encryption algorithm has higher data privacy and security.

We present a system for interactive examination of learned security policies. It allows a user to traverse episodes of Markov decision processes in a controlled manner and to track the actions triggered by security policies. Similar to a software debugger, a user can continue or or halt an episode at any time step and inspect parameters and probability distributions of interest. The system enables insight into the structure of a given policy and in the behavior of a policy in edge cases. We demonstrate the system with a network intrusion use case. We examine the evolution of an IT infrastructure’s state and the actions prescribed by security policies while an attack occurs. The policies for the demonstration have been obtained through a reinforcement learning approach that includes a simulation system where policies are incrementally learned and an emulation system that produces statistics that drive the simulation runs.

This work-in-progress paper proposes a design methodology that addresses the complexity and heterogeneity of cyber-physical systems (CPS) while simultaneously proving resilient control logic and security properties. The design methodology involves a formal methods-based approach by translating the complex control logic and security properties of a water flow CPS into timed automata. Timed automata are a formal model that describes system behaviors and properties using mathematics-based logic languages with precision. Due to the semantics that are used in developing the formal models, verification techniques, such as theorem proving and model checking, are used to mathematically prove the specifications and security properties of the CPS. This work-in-progress paper aims to highlight the need for formalizing plant models by creating a timed automata of the physical portions of the water flow CPS. Extending the time automata with control logic, network security, and privacy control processes is investigated. The final model will be formally verified to prove the design specifications of the water flow CPS to ensure efficacy and security.

Given the COVID-19 pandemic, this paper aims at providing a full-process information system to support the detection of pathogens for a large range of populations, satisfying the requirements of light weight, low cost, high concurrency, high reliability, quick response, and high security. The project includes functional modules such as sample collection, sample transfer, sample reception, laboratory testing, test result inquiry, pandemic analysis, and monitoring. The progress and efficiency of each collection point as well as the status of sample transfer, reception, and laboratory testing are all monitored in real time, in order to support the comprehensive surveillance of the pandemic situation and support the dynamic deployment of pandemic prevention resources in a timely and effective manner. Deployed on a cloud platform, this system can satisfy ultra-high concurrent data collection requirements with 20 million collections per day and a maximum of 5 million collections per hour, due to its advantages of high concurrency, elasticity, security, and manageability. This system has also been widely used in Jiangsu, Shaanxi provinces, for the prevention and control of COVID-19 pandemic. Over 100 million NAT data have been collected nationwide, providing strong informational support for scientific and reasonable formulation and execution of COVID-19 prevention plans.

The features of socio-cyber-physical systems are presented, which dictate the need to revise traditional management methods and transform the management system in such a way that it takes into account the presence of a person both in the control object and in the control loop. The use of situational control mechanisms is proposed. The features of this approach and its comparison with existing methods of situational awareness are presented. The comparison has demonstrated wider possibilities and scope for managing socio-cyber-physical systems. It is recommended to consider a wider class of types of relations that exist in socio-cyber-physical systems. It is indicated that such consideration can be based on the use of pseudo-physical logics considered in situational control. It is pointed out that it is necessary to design a classifier of situations (primarily in cyberspace), instead of traditional classifiers of threats and intruders.

Intrusion detection systems (IDSs) are widely deployed in the industrial control systems to protect network security. IDSs typically generate a huge number of alerts, which are time-consuming for system operators to process. Most of the alerts are individually insignificant false alarms. However, it is not the best solution to discard these alerts, as they can still provide useful information about network situation. Based on the study of characteristics of alerts in the industrial control systems, we adopt an enhanced method of exponentially weighted moving average (EWMA) control charts to help operators in processing alerts. We classify all detection signatures as regular and irregular according to their frequencies, set multiple control limits to detect anomalies, and monitor regular signatures for network security situational awareness. Extensive experiments have been performed using real-world alert data. Simulation results demonstrate that the proposed enhanced EWMA method can greatly reduce the volume of alerts to be processed while reserving significant abnormal information.

In this era, with a great extent of automation and connection, modern production processes are highly prone to cyber-attacks. The sensor-controller chain becomes an obvious target for attacks because sensors are commonly used to regulate production facilities. In this research, we introduce a new control configuration for the system, which is sensitive to time delay attacks (TDA), in which data transfer from the sensor to the controller is intentionally delayed. The attackers want to disrupt and damage the system by forcing controllers to use obsolete data about the system status. In order to improve the accuracy of delay identification and prediction, as well as erroneous limit and estimation for control, a new control structure is developed by an Internal Model Control (IMC) based Proportional-Integral-Derivative (PID) scheme with a fractional filter. An additional concept is included to mitigate the effect of time delay attack, i.e., the smith predictor. Simulation studies of the established control framework have been implemented with two numerical examples. The performance assessment of the proposed method has been done based on integral square error (ISE), integral absolute error (IAE) and total variation (TV).

Industrial control systems (ICSs) have become more complex due to their increasing connectivity, heterogeneity and, autonomy. As a result, cyber-threats against such systems have been significantly increased as well. Since a compromised industrial system can easily lead to hazardous safety and security consequences, it is crucial to develop security countermeasures to protect coexisting IT systems and industrial physical processes being involved in modern ICSs. Accordingly, in this study, we propose a deep learning-based semantic anomaly detection framework to model the complex behavior of ICSs. In contrast to the related work assuming only simpler security threats targeting individual controllers in an ICS, we address multi-PLC attacks that are harder to detect as requiring to observe the overall system state alongside single-PLC attacks. Using industrial simulation and emulation frameworks, we create a realistic setup representing both the production and networking aspects of industrial systems and conduct some potential attacks. Our experimental results indicate that our model can detect single-PLC attacks with 95% accuracy and multi-PLC attacks with 80% accuracy and nearly 1% false positive rate.

Public transportation is an important system of urban passenger transport. The purpose of this article is to explore the impact of network resilience when each station of urban rail transit network was attacked by large passenger flow. Based on the capacity load model, we propose a load redistribution mechanism to simulate the passenger flow propagation after being attacked by large passenger flow. Then, taking Xi'an's rail network as an example, we study the resilience variety of the network after a node is attacked by large passenger flow. Through some attack experiments, the feasibility of the model for studying the resilience of the rail transit system is finally verified.

With the ever increasing threat of malware, extensive research effort has been put on applying Deep Learning for malware classification tasks. Graph Neural Networks (GNNs) that process malware as Control Flow Graphs (CFGs) have shown great promise for malware classification. However, these models are viewed as black-boxes, which makes it hard to validate and identify malicious patterns. To that end, we propose CFG-Explainer, a deep learning based model for interpreting GNN-oriented malware classification results. CFGExplainer identifies a subgraph of the malware CFG that contributes most towards classification and provides insight into importance of the nodes (i.e., basic blocks) within it. To the best of our knowledge, CFGExplainer is the first work that explains GNN-based mal-ware classification. We compared CFGExplainer against three explainers, namely GNNExplainer, SubgraphX and PGExplainer, and showed that CFGExplainer is able to identify top equisized subgraphs with higher classification accuracy than the other three models.

Under the situation of regular epidemic prevention and control, teleworking has gradually become a normal working mode. With the development of modern information technologies such as big data, cloud computing and mobile Internet, it's become a problem that how to build an effective security defense system to ensure the information security of teleworking in complex network environment while ensuring the availability, collaboration and efficiency of teleworking. One of the solutions is Zero Trust Network(ZTN), most enterprise infrastructures will operate in a hybrid zero trust/perimeter-based mode while continuing to invest in IT modernization initiatives and improve organization business processes. In this paper, we have systematically studied the zero trust principles, the logical components of zero trust architecture and the key technology of zero trust network. Based on the abstract model of zero trust architecture and information security technologies, a prototype has been realized which suitable for iOS terminals to access enterprise resources safely in teleworking mode.

Aiming at the specificity and complexity of the power IoT terminal, a method of power IoT terminal firmware vulnerability detection based on memory fuzzing is proposed. Use the method of bypassing the execution to simulate and run the firmware program, dynamically monitor and control the execution of the firmware program, realize the memory fuzzing test of the firmware program, design an automatic vulnerability exploitability judgment plug-in for rules and procedures, and provide power on this basis The method and specific process of the firmware vulnerability detection of the IoT terminal. The effectiveness of the method is verified by an example.

Achieving agile and resilient autonomous capabilities for cyber defense requires moving past indicators and situational awareness into automated response and recovery capabilities. The objective of the AlphaSOC project is to use state of the art sequential decision-making methods to automatically investigate and mitigate attacks on cyber physical systems (CPS). To demonstrate this, we developed a simulation environment that models the distributed navigation control system and physics of a large ship with two rudders and thrusters for propulsion. Defending this control network requires processing large volumes of cyber and physical signals to coordi-nate defensive actions over many devices with minimal disruption to nominal operation. We are developing a Reinforcement Learning (RL)-based approach to solve the resulting sequential decision-making problem that has large observation and action spaces.

In this paper, we tried to summarize the practical experience of information security audits of nuclear power plants' automated process control system (I&C). The article presents a methodology for auditing the information security of instrumentation and control systems for nuclear power plants. The methodology was developed taking into account international and national Russian norms and rules and standards. The audit taxonomy, classification lifecycle are described. The taxonomy of information security audits shows that form, objectives of the I&C information security audit, and procedures can vary widely. A conceptual program is considered and discussed in details. The distinctive feature of the methodology is the mandatory consideration of the impact of information security on nuclear safety.

In the recent years, we have witnessed quite notable cyber-attacks targeting industrial automation control systems. Upgrading their cyber security is a challenge, not only due to long equipment lifetimes and legacy protocols originally designed to run in air-gapped networks. Even where multiple data sources are available and collection established, data interpretation usable across the different data sources remains a challenge. A modern hydro power plant contains the data sources that range from the classical distributed control systems to newer IoT- based data sources, embedded directly within the plant equipment and deeply integrated in the process. Even abundant collected data does not solve the security problems by itself. The interpretation of data semantics is limited as the data is effectively siloed. In this paper, the relevance of semantic integration of diverse data sources is presented in the context of a hydro power plant. The proposed semantic integration would increase the data interoperability, unlocking the data siloes and thus allowing ingestion of complementary data sources. The principal target of the data interoperability is to support the data-enhanced cyber security in an operational hydro power plant context. Furthermore, the opening of the data siloes would enable additional usage of the existing data sources in a structured semantically enriched form.