Biblio

NATO strategy and policy has increasingly focused on incorporating cyber operations to support deterrence, warfighting, and intelligence objectives. However, offensive cyber operations in particular have presented a delicate challenge for the alliance. As cyber threats to NATO members continue to grow, the alliance has begun to address how it could incorporate offensive cyber operations into its strategy and policy. However, there are significant hurdles to meaningful cooperation on offensive cyber operations, in contrast with the high levels of integration in other operational domains. Moreover, there is a critical gap in existing conceptualizations of the role of offensive cyber operations in NATO policy. Specifically, NATO cyber policy has focused on cyber operations in a warfighting context at the expense of considering cyber operations below the level of conflict. In this article, we explore the potential role for offensive cyber operations not only in wartime but also below the threshold of armed conflict. In doing so, we systematically explore a number of challenges at the political/strategic as well as the operational/tactical levels and provide policy recommendations for next steps for the alliance.

Since the cyber and physical layers in the distribution system are deeply integrated, the traditional distribution system has gradually developed into the cyber-physical distribution system (CPDS), and the failures of the cyber layer will affect the reliable and safe operation of the whole distribution system. Therefore, this paper proposes an CPDS planning method considering the reliability of the cyber-physical system. First, the reliability evaluation model of CPDS is proposed. Specifically, the functional reliability model of the cyber layer is introduced, based on which the physical equipment reliability model is further investigated. Second, an optimal planning model of CPDS considering cyber-physical random failures is developed, which is solved using the Monte Carlo Simulation technique. The proposed model is tested on the modified IEEE 33-node distribution system, and the results demonstrate the effectiveness of the proposed method.

International regulations specified in WP.29 and international standards specified in ISO/SAE 21434 require security operations such as cyberattack detection and incident responses to protect vehicles from cyberattacks. To meet these requirements, many vehicle manufacturers are planning to install Intrusion Detection Systems (IDSs) in the Controller Area Network (CAN), which is a primary component of in-vehicle networks, in the coming years. Besides, many vehicle manufacturers and information security companies are developing technologies to identify attack paths related to IDS alerts to respond to cyberattacks appropriately and quickly. To develop the IDSs and the technologies to identify attack paths, it is essential to grasp normal communications performed on in-vehicle networks. Thus, our study aims to develop a technology that can easily grasp normal communications performed on in-vehicle networks. In this paper, we propose the first message source identification method that easily identifies CAN-IDs used by each Electronic Control Unit (ECU) connected to the CAN for message transmissions. We realize the proposed method by utilizing diagnostic communications and an IDS installed in the CAN (CAN-IDS). We evaluate the proposed method using an ECU installed in an actual vehicle and four kinds of simulated CAN-IDSs based on typical existing intrusion detection methods for the CAN. The evaluation results show that the proposed method can identify the CAN-ID used by the ECU for CAN message transmissions if a suitable simulated CAN-IDS for the proposed method is connected to the vehicle.

In the process of compiling the power-cut window period of the power grid equipment maintenance plan, problems such as omission of constraints are prone to occur due to excessive reliance on manual experience. In response to these problems, this paper proposes a method for mining key features of the power-cut window based on grey relational analysis. Through mining and analysis of the historical operation data of the power grid, the operation data of new energy, and the historical power-cut information of equipment, the indicators that play a key role in the arrangement of the outage window period of the equipment maintenance plan are found. Then use the key indicator information to formulate the window period. By mining the relationship between power grid operation data and equipment power outages, this paper can give full play to the big data advantages of the power grid, improve the accuracy and efficiency of the power-cut window period.

With the gradual construction and implementation of cloud computing, the information security problem of the smart grid has surfaced. Therefore, in the construction of the smart grid cloud computing platform, information security needs to be considered in planning, infrastructure, and management at the same time, and it is imminent to build an information network that is secure from terminal to the platform to data. This paper introduces the concept of cloud security technology and the latest development of cloud security technology and discusses the main strategies of cloud security construction in electric power enterprises.

This article analyzes Risk management (RM) activities against different ISO standards. The aim is to improve the coordination and interoperability of risk management activities in IT, IT services management, quality management, project management, and information security management. The ISO 31000: 2018 standard was chosen as a structured input for ISO 20000-1: 2018, ISO 21500: 2021, ISO 27000: 2018, ISO 9001: 2015 and ISO Annex SL standards relative to RM. The PDCA cycle has been chosen as one of the main methods for planning, implementing, and improving quality management systems and their processes. For a management system to be more effective, more reliable, and capable of preventing negative results, it must deal with the possible resulting risks.

This article discusses a threat and vulnerability analysis model that allows you to fully analyze the requirements related to information security in an organization and document the results of the analysis. The use of this method allows avoiding and preventing unnecessary costs for security measures arising from subjective risk assessment, planning and implementing protection at all stages of the information systems lifecycle, minimizing the time spent by an information security specialist during information system risk assessment procedures by automating this process and reducing the level of errors and professional skills of information security experts. In the initial sections, the common methods of risk analysis and risk assessment software are analyzed and conclusions are drawn based on the results of comparative analysis, calculations are carried out in accordance with the proposed model.

Swarm robotics is a network based multi-device system designed to achieve shared objectives in a synchronized way. This system is widely used in industries like farming, manufacturing, and defense applications. In recent implementations, swarm robotics is integrated with Blockchain based networks to enhance communication, security, and decentralized decision-making capabilities. As most of the current blockchain applications are based on complex consensus algorithms, every individual robot in the swarm network requires high computing power to run these complex algorithms. Thus, it is a challenging task to achieve consensus between the robots in the network. This paper will discuss the details of designing an effective consensus algorithm that meets the requirements of swarm robotics network.

Systems based on WB protection have a limited lifetime, measured in months and sometimes days. Unfortunately, to understand for how long the application will be uncompromised, if possible, only empirically. However, it is possible to make a preliminary assessment of the security of a particular implementation, depending on the methods and their number used in the implementation, it will allow reallocating resources to more effective means of protection.

Distributed wind is an electric energy resource segment with strong potential to be deployed in many applications, but special consideration of resilience and cybersecurity is needed to address the unique conditions associated with distributed wind. Distributed wind is a strong candidate to help meet renewable energy and carbon-free energy goals. However, care must be taken as more systems are installed to ensure that the systems are reliable, resilient, and secure. The physical and communications requirements for distributed wind mean that there are unique cybersecurity considerations, but there is little to no existing guidance on best practices for cybersecurity risk management for distributed wind systems specifically. This research develops an architecture for managing cyber risks associated with distributed wind systems through resilience functions. The architecture takes into account the configurations, challenges, and standards for distributed wind to create a risk-focused perspective that considers threats, vulnerabilities, and consequences. We show how the resilience functions of identification, preparation, detection, adaptation, and recovery can mitigate cyber threats. We discuss common distributed wind architectures and interconnections to larger power systems. Because cybersecurity cannot exist independently, the cyber-resilience architecture must consider the system holistically. Finally, we discuss risk assessment recommendations with special emphasis on what sets distributed wind systems apart from other distributed energy resources (DER).

With the increasing number of catastrophic weather events and resulting disruption in the energy supply to essential loads, the distribution grid operators’ focus has shifted from reliability to resiliency against high impact, low-frequency events. Given the enhanced automation to enable the smarter grid, there are several assets/resources at the disposal of electric utilities to enhances resiliency. However, with a lack of comprehensive resilience tools for informed operational decisions and planning, utilities face a challenge in investing and prioritizing operational control actions for resiliency. The distribution system resilience is also highly dependent on system attributes, including network, control, generating resources, location of loads and resources, as well as the progression of an extreme event. In this work, we present a novel multi-stage resilience measure called the Anticipate-Withstand-Recover (AWR) metrics. The AWR metrics are based on integrating relevant ‘system characteristics based factors’, before, during, and after the extreme event. The developed methodology utilizes a pragmatic and flexible approach by adopting concepts from the national emergency preparedness paradigm, proactive and reactive controls of grid assets, graph theory with system and component constraints, and multi-criteria decision-making process. The proposed metrics are applied to provide decision support for a) the operational resilience and b) planning investments, and validated for a real system in Alaska during the entirety of the event progression.

The error-resiliency of Artificial Intelligence (AI) hardware accelerators is a major concern, especially when they are deployed in mission-critical and safety-critical applications. In this paper, we propose a neuron fault tolerance strategy for Spiking Neural Networks (SNNs). It is optimized for low area and power overhead by leveraging observations made from a large-scale fault injection experiment that pinpoints the critical fault types and locations. We describe the fault modeling approach, the fault injection framework, the results of the fault injection experiment, the fault-tolerance strategy, and the fault-tolerant SNN architecture. The idea is demonstrated on two SNNs that we designed for two SNN-oriented datasets, namely the N-MNIST and IBM's DVS128 gesture datasets.

Data exfiltration is of increasing concern throughout the world. The number of incidents and capabilities of data exfiltration attacks are growing at an unprecedented rate. However, such attack vectors have not been deeply explored in the literature. This paper aims to address this gap by implementing a data exfiltration methodology, detailing some data exfiltration methods. Groups of exfiltration methods are incorporated into a program that can act as a testbed for owners of any network that stores sensitive data. The implemented methods are tested against the well-known network intrusion detection system Snort, where all of them have been successfully evaded detection by its community rule sets. Thus, in this paper, we have developed new countermeasures to prevent and detect data exfiltration attempts using these methods.

Windows Communication Foundation (WCF) is a communication framework for building connected, service-oriented applications, initially released by Microsoft as part of.NET Framework, but now open source. The WCF message-based communication is a very popular solution used for sending asynchronous messages from one service endpoint to another. Because WCF provides many functionalities it has a large-consuming development model and often the security measures implemented in applications are not proper. In this study we propose a methodology for offensive security analysis of an WCF endpoint or service, from red team perspective. A step by step approach, empirical information, and detailed analysis report of WCF vulnerabilities are presented. We conclude by proposing recommendations for mitigating attacks and securing endpoints.

Cyber supply chain (CSC) security cost effectiveness should be the first and foremost decision to consider when integrating various networks in supplier inbound and outbound chains. CSC systems integrate different organizational network systems nodes such as SMEs and third-party vendors for business processes, information flows, and delivery channels. Adversaries are deploying various attacks such as RAT and Island-hopping attacks to penetrate, infiltrate, manipulate and change delivery channels. However, most businesses fail to invest adequately in security and do not consider analyzing the long term benefits of that to monitor and audit third party networks. Thus, making cost benefit analysis the most overriding factor. The paper explores the cost-benefit analysis of investing in cyber supply chain security to improve security. The contribution of the paper is threefold. First, we consider the various existing cybersecurity investments and the supply chain environment to determine their impact. Secondly, we use the NPV method to appraise the return on investment over a period of time. The approach considers other methods such as the Payback Period and Internal Rate of Return to analyze the investment appraisal decisions. Finally, we propose investment options that ensure CSC security performance investment appraisal, ROI, and business continuity. Our results show that NVP can be used for cost-benefit analysis and to appraise CSC system security to ensure business continuity planning and impact assessment.

Spatial crowdsourcing has stimulated various new applications such as taxi calling and food delivery. A key enabler for these spatial crowdsourcing based applications is to plan routes for crowd workers to execute tasks given diverse requirements of workers and the spatial crowdsourcing platform. Despite extensive studies on task planning in spatial crowdsourcing, few have accounted for the location privacy of tasks, which may be misused by an untrustworthy platform. In this paper, we explore efficient task planning for workers while protecting the locations of tasks. Specifically, we define the Privacy-Preserving Task Planning (PPTP) problem, which aims at both total revenue maximization of the platform and differential privacy of task locations. We first apply the Laplacian mechanism to protect location privacy, and analyze its impact on the total revenue. Then we propose an effective and efficient task planning algorithm for the PPTP problem. Extensive experiments on both synthetic and real datasets validate the advantages of our algorithm in terms of total revenue and time cost.

The study of networks is an extensively investigated field of research, with networks and network structure often encoding relationships describing certain systems or processes. Critical infrastructure is understood as being a structure whose failure or damage has considerable impact on safety, security and wellbeing of society, with power systems considered a classic example. The work presented in this paper builds on the long-lasting foundations of network and complex network theory, proposing an extension in form of rebalance based flow centrality for structural vulnerability assessment and critical component identification in adaptive network topologies. The proposed measure is applied to power system vulnerability analysis, with performance demonstrated on the IEEE 30-, 57- and 118-bus test system, outperforming relevant methods from the state-of-the-art. The proposed framework is deterministic (guaranteed), analytically obtained (interpretable) and generalizes well with changing network parameters, providing a complementary tool to power system vulnerability analysis and planning.

Recent advancements in quantum information theory and quantum computation intend the possibilities of breaking the existing classical cryptographic systems. To mitigate these kinds of threats with quantum computers we need some advanced quantum-based cryptographic systems. The research orientation towards this is tremendous in recent years, and many excellent approaches have been reported. In this article, we discuss the probable approaches of the quantum cryptographic systems from implementation point of views to handle the post-quantum cryptographic attacks.

Internal attacks are one of the biggest cybersecurity issues to companies and businesses. Despite the implemented perimeter security systems, the risk of adversely affecting the security and privacy of the organization’s information remains very high. Actually, the detection of such a threat is known to be a very complicated problem, presenting many challenges to the research community. In this paper, we investigate the effectiveness and usefulness of using Autoencoder and Variational Autoencoder deep learning algorithms to automatically defend against insider threats, without human intervention. The performance evaluation of the proposed models is done on the public CERT dataset (CERT r4.2) that contains both benign and malicious activities generated from 1000 simulated users. The comparison results with other models show that the Variational Autoencoder neural network provides the best overall performance with a higher detection accuracy and a reasonable false positive rate.

We propose a human-operator guided planning approach to pushing-based manipulation in clutter. Most recent approaches to manipulation in clutter employs randomized planning. The problem, however, remains a challenging one where the planning times are still in the order of tens of seconds or minutes, and the success rates are low for difficult instances of the problem. We build on these control-based randomized planning approaches, but we investigate using them in conjunction with human-operator input. In our framework, the human operator supplies a high-level plan, in the form of an ordered sequence of objects and their approximate goal positions. We present experiments in simulation and on a real robotic setup, where we compare the success rate and planning times of our human-in-the-loop approach with fully autonomous sampling-based planners. We show that with a minimal amount of human input, the low-level planner can solve the problem faster and with higher success rates.

The emerging time-sensitive applications, such as industrial automation, smart grids, and telesurgery, pose strong demands for enabling large-scale IP-based deterministic networks. The IETF DetNet working group recently proposes a Cycle Specified Queuing and Forwarding (CSQF) solution. However, CSQF only specifies an underlying device-level primitive while how to achieve network-wide flow scheduling remains undefined. Previous scheduling mechanisms are mostly oriented to the context of local area networks, making them inapplicable to the cyclic traffic in wide area networks. In this paper, we design the Cycle Tags Planning (CTP) mechanism, a first mathematical model to enable network-wide scheduling for cyclic traffic in large-scale deterministic networks. Then, a novel scheduling algorithm named flow offset and cycle shift (FO-CS) is designed to compute the flows' cycle tags. The FO-CS algorithm is evaluated under long-distance network topologies in remote industrial control scenarios. Compared with the Naive algorithm without using FO-CS, simulation results demonstrate that FO-CS improves the scheduling flow number by 31.2% in few seconds.

The ongoing expansion of underwater Internet of Things techniques promote diverse categories of maritime intelligent systems, e.g., Underwater Acoustic Sensor Networks (UASNs), Underwater Wireless Networks (UWNs), especially multiple Autonomous Underwater Vehicle (AUV) based UWNs have produced many civil and military applications. To enhance the network management and scalability, in this paper, the technique of Software-Defined Networking (SDN) technique is introduced, leading to the paradigm of Software-Defined multi-AUV-based UWNs (SD-UWNs). With SD-UWNs, the network architecture is divided into three functional layers: data layer, control layer, and application layer, and the network administration is re-defined by a framework of software-defined beacon. To manage the network, a control model based on artificial potential field and network topology theory is constructed. On account of the efficient data sharing ability of SD-UWNs, a proactive alarming-enabled path planning scheme is proposed, wherein all potential categories of obstacle avoidance scenes are taken into account. Evaluation results indicate that the proposed SD-UWN is more efficient in scheduling the cooperative network function than the traditional approaches and can secure exact path planning.

Automatic dependent surveillance-broadcast (ADS- B) records provide an important basis and evidence for future route planning and accountability. However, due to the lack of effective support for the integrity and confidentiality of ADS-B, the air traffic control (ATC) system based on ADS-B faces serious security threats. Once the data is tampered with, it will cause immeasurable losses to society. The ADS-B data is arranged in chronological order, and the order-preserving encryption method allows users to directly search for ciphertexts by time. However, encryption alone does not guarantee the integrity of the data. The attacker can still destroy the integrity of the data by modifying the ciphertext. This paper proposes a secure ADS- B protection scheme that supports queries. We construct a dynamic order-preserving encryption (DOPE) scheme to achieve data confidentiality and sequential search of target data in the ciphertext. In addition, the scheme achieves fast integrity checking by calculating the unique verification label of the entire ciphertext, and supports blockless verification, which means that all data does not need to be transmitted during the audit phase. In the meanwhile, the auditor can verify the integrity of multiple ADS-B documents at once, which improves the computational efficiency of the audit. We analyze the integrity and security of the scheme and proved that DOPE is indistinguishable under an ordered chosen-plaintext attack (IND-OCPA). Furthermore, we conclude through performance analysis that the communication overhead is constant and computation overhead is logarithmic level. The proposed scheme is applicable to all data arranged in order, such as hospital records arranged by date and so on. At the same time, ADS-B can be used for urban vehicle monitoring and is a basic means to realize smart transportation.

Unit Commitment (UC) problem is one of the most fundamental constrained optimization problems in the planning and operation of electric power systems and electricity markets. Solving a large-scale UC problem requires a lot of computational effort which can be improved using data driven approaches. In practice, a UC problem is solved multiple times a day with only minor changes in the input data. Hence, this aspect can be exploited by using the historical data to solve the problem. In this paper, an Artificial Intelligence (AI) based approach is proposed to solve a Security Constrained UC problem. The proposed algorithm was tested through simulations on a 4-bus power system and satisfactory results were obtained. The results were compared with those obtained using IBM CPLEX MIQP solver.

This paper describes a Cyber Threat, Vulnerability and Defense Modeling and Simulation tool kit used for evaluation of systems and networks to improve cyber resiliency. This capability is used to help increase the resiliency of networks at various stages of their lifecycle, from initial design and architecture through the operation of deployed systems and networks. Resiliency of computer systems and networks to cyber threats is facilitated by the modeling of agile and resilient defenses versus threats and running multiple simulations evaluated against resiliency metrics. This helps network designers, cyber analysts and Security Operations Center personnel to perform trades using what-if scenarios to select resiliency capabilities and optimally design and configure cyber resiliency capabilities for their systems and networks.