Biblio

With the increasing number of electric vehicles, the scale of the market also increases. In the past, the electric vehicle market had problems such as opaque information, numerous levels and data leakage, which were criticized for the impact of the overall development and policies of the electric vehicle industry. In view of the problems existing in the transparency and security of big data management transactions of the Internet of vehicles, this paper combs the commercial operation framework of the Internet of Vehicles Platform, analyses the feasibility and necessity of establishing the token system of the Internet of Vehicles Platform, and constructs the token economic system architecture of the Internet of Vehicles Platform and its development path.

Intellectual Property Rights (IPR) results from years of research and wisdom by property owners, and it plays an increasingly important role in promoting economic development, technological progress, and cultural prosperity. Thus, we need to strengthen the degree of protection of IPR. However, as internet technology continues to open up the market for IPR, the ease of network operation has led to infringement of IPR in some cases. Intellectual property infringement has occurred in some cases. Also, Internet development's concealed and rapid nature has led to the fact that IPR infringers cannot be easily detected. This paper addresses how to protect the rights and interests of IPR holders in the context of the rapid development of the internet. This paper explains the IPR and proposes an algorithm to enhance security for a better security model to protect IPR. This proposes optimization techniques to detect intruder attacks for securing IPR, by using support vector machines (SVM), it provides better results to secure public and private intellectual data by optimizing technologies.

As IoT systems would have an economic impact, they are gaining growing interest. Millions of IoT devices are expected to join the internet of things, which will carny both major benefits and significant security threats to consumers. For IoT systems that secure data and preserve privacy of users, trust management is an essential component. IoT objects carry on the ownership settings of their owners, allowing them to interact with each other. Social relationships are believed to be important in confidence building. In this paper, we explain how to compute trust in social IoT environments using a cloud-based approach.

Today's companies are increasingly relying on Internet of Everything (IoE) to modernize their operations. The very complexes characteristics of such system expose their applications and their exchanged data to multiples risks and security breaches that make them targets for cyber attacks. The aim of our work in this paper is to provide an cybersecurity strategy whose objective is to prevent and anticipate threats related to the IoE. An economic approach is used in order to help to take decisions according to the reduction of the risks generated by the non definition of the appropriate levels of security. The considered problem have been resolved by exploiting a combinatorial optimization approach with a practical case of knapsack. We opted for a bi-objective modeling under uncertainty with a constraint of cardinality and a given budget to be respected. To guarantee a robustness of our strategy, we have also considered the criterion of uncertainty by taking into account all the possible threats that can be generated by a cyber attacks over IoE. Our strategy have been implemented and simulated under MATLAB environement and its performance results have been compared to those obtained by NSGA-II metaheuristic. Our proposed cyber security strategy recorded a clear improvment of efficiency according to the optimization of the security level and cost parametrs.

In recent times, major cybersecurity breaches and cyber fraud had huge negative impact on victim organisations. The biggest impact made on major areas of business activities. Majority of organisations facing cybersecurity adversity and advanced threats suffers from huge financial and reputation loss. The current security technologies, policies and processes are providing necessary capabilities and cybersecurity mechanism to solve cyber threats and risks. However, current solutions are not providing required mechanism for decision making on impact of cybersecurity breaches and fraud. In this paper, we are reporting initial findings and proposing conceptual solution. The paper is aiming to provide a novel model for Cybersecurity Economics and Analysis (CEA). We will contribute to increasing harmonization of European cybersecurity initiatives and reducing fragmented practices of cybersecurity solutions and also helping to reach EU Digital Single Market goal. By introducing Cybersecurity Readiness Level Metrics the project will measure and increase effectiveness of cybersecurity programs, while the cost-benefit framework will help to increase the economic and financial viability, effectiveness and value generation of cybersecurity solutions for organisation's strategic, tactical and operational imperative. The ambition of the research development and innovation (RDI) is to increase and re-establish the trust of the European citizens in European digital environments through practical solutions.

The 5G technology will play a crucial role in global economic growth through numerous industrial developments. However, it is essential to ensure the security of these developed systems, while 5G brings unique security challenges. This paper contributes explicitly to the need for an effective Vulnerability Assessment Approach (VAA) to identify and assess the vulnerabilities in 5G networks in an accurate, salable, and dynamic way. The proposed approach develops an optimized mechanism based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to analyze the vulnerabilities in 5G Edge networks from the attacker perspective while considering the dynamic and scalable Edge properties. Furthermore, we introduce a cloud-based 5G Edge security testbed to test and evaluate the accuracy, scalability, and performance of the proposed VAA.

New services and products are increasingly becoming integral parts of our daily lives rising our technological dependence, as well as our exposure to risks from cyber. Critical sectors such as transport are progressively depending on digital technologies to run their core operations and develop novel solutions to exploit the economic strengths of the European Union. However, despite the fact that the continuously increasing number of visitors, entering the European Union through land-border crossing points or seaports, brings tremendous economic benefits, novel border control solutions, such as mobile devices for passenger identification for land and sea border control, are essential to accurately identify passengers ``on the fly'' while ensuring their comfort. However, the highly confidential personal data managed by these devices makes them an attractive target for cyberattacks. Therefore, novel secure and usable user authentication mechanisms are required to increase the level of security of this kind of devices without interrupting border control activities. Towards this direction, we, firstly, discuss risk-based and adaptive authentication for mobile devices as a suitable approach to deal with the security vs. usability challenge. Besides that, a novel risk-based adaptive user authentication mechanism is proposed for mobile passenger identification devices used by border control officers at land and sea borders.

A real or potential threat to various large and small security objects, which comes from both internal and external attackers, determines one or another activities to ensure internal and external security. These actions depend on the spheres of life of state and society, which are targeted by the security threats. These threats can be conveniently classified into political threats (or threats to the existing constitutional order), economic, military, informational, technogenic, environmental, corporate, and other threats. The article discusses a model of an information system, which main criterion is the system security based on the concept of risk. When considering the model, it was determined that it possess multi-criteria aspects. Therefore the establishing the quantitative and qualitative characteristics is a complex and dynamic task. The paper proposes to use the mathematical apparatus of the teletraffic theory in one of the elements of the protected system, namely, in the end-to-end security subsystem.

DDoS attacks have grown in popularity as a tactic for potential hackers, cyber blackmailers, and cyberpunks. These attacks have the potential to put a person unconscious in a matter of seconds, resulting in severe economic losses. Despite the vast range of conventional mitigation techniques available today, DDoS assaults are still happening to grow in frequency, volume, and intensity. A new network paradigm is necessary to meet the requirements of today's tough security issues. We examine the available detection and mitigation of DDoS attacks techniques in depth. We classify solutions based on detection of DDoS attacks methodologies and define the prerequisites for a feasible solution. We present a novel methodology named D3 for detecting and mitigating DDoS attacks using cuckoo filter.

This paper focuses on the typical business scenario of intelligent factory, it includes the manufacturing process, carries out hierarchical security protection, forms a full coverage industrial control security protection network, completes multi-means industrial control security direct protection, at the same time, it utilizes big data analysis, dynamically analyzes the network security situation, completes security early warning, realizes indirect protection, and finally builds a self sensing and self-adjusting industrial network security protection system It provides a reliable reference for the development of intelligent manufacturing industry.

The digital transformation is injecting energy into economic growth and governance improvement for the China government. Digital governance and e-government services are playing a more and more important role in public management and social governance. Meanwhile, cyber-attacks and threats become the major challenges for e-government application systems. In this paper, we proposed a novel dynamic access entry scheme for web application, which provide a rapidly-changing defender-controlled attack surface based on Moving Target Defense (MTD) technology. The scheme can turn the static keywords of Uniform Resource Locator (URL) into the dynamic and random ones, which significantly increase the cost to adversaries attack. We present the prototype of the proposed scheme and evaluate the feasibility and effectiveness. The experimental results demonstrated the scheme is practical and effective.

MANET stands for Mobile ad-hoc network, which is also known as a wireless network. It provides a routable networking environment which does not have a centralized infrastructure. MANET is used in many important sectors like economic sector (corporate field), security sector (military field), education sector (video conferences and lectures), law sector (law enforcement) and many more. Even though it plays a vital role in different sectors and improves its economic growth, security is a major concern in MANET. Due to lack of inbuilt security, several attacks like data traffic attack, control traffic attack. The wormhole is a kind of control traffic attack which forms wormhole link between nodes. In this paper, we have proposed an approach to detect and get rid of the wormhole attack. The proposed approach is based on trust values, which will decide whether nodes are affected by using parameters like receiving time and data rate. On evaluation, we have concluded that the wormhole attack decreases the network's performance while using trusted approach its value increases. Means PDR and throughput return best results for the affected network while in case of end to end delay it returns similar results as of unaffected network.

Both the sophistication and scale of cyberattacks are increasing, revealing the extent of risks at which critical infrastructure and other information and communication systems are exposed. Furthermore, the introduction of IoT devices in a number of different applications, ranging from home automation to the monitoring of critical infrastructure, has created an even more complicated cybersecurity landscape. A large amount of research has been done on detecting these attacks in real time, however mitigation is left to security experts, which is time consuming and may have economic consequences. In addition, there is no public data available for action selection that could enable the use of the latest techniques in machine learning or deep learning for this area. Currently, most systems deploy a rule-based response selection methodology for mitigating detected attacks. In this paper, we introduce a situation calculus-based approach to automated response for IoT cyberattacks. The approach offers explicit semantic-rich cognitive modeling of attacks, effects and actions and supports situation inference for timely and accurate responses. We demonstrate the effectiveness of our approach for modelling and responding to cyberattacks by implementing a use case in a real-world IoT scenario.

Data tracing is an important topic in the era of digital economy when data are considered as one of the core factors in economic activities. However, the current data traceability systems often fail to obtain public trust due to their centralization and opaqueness. Blockchain possesses natural technical features such as data tampering resistance, anonymity, encryption security, etc., and shows great potential of improving the data tracing credibility. In this paper, we propose a blockchain-PROV-based multi-center data provenance solution in where the PROV model standardizes the data record storage and provenance on the blockchain automatically and intelligently. The solution improves the transparency and credibility of the provenance data, such as to help the efficient control and open sharing of data assets.

The reform of electricity marketization has bred multiple market agents. In order to maximize the total social benefits on the premise of ensuring the security of the system and taking into account the interests of multiple market agents, a bi-level optimal allocation model of distribution network with multiple agents participating is proposed. The upper level model considers the economic benefits of energy and service providers, which are mainly distributed power investors, energy storage operators and distribution companies. The lower level model considers end-user side economy and actively responds to demand management to ensure the highest user satisfaction. The K-means multi scenario analysis method is used to describe the time series characteristics of wind power, photovoltaic power and load. The particle swarm optimization (PSO) algorithm is used to solve the bi-level model, and IEEE33 node system is used to verify that the model can effectively consider the interests of multiple agents while ensuring the security of the system.

We present a privacy-preserving distributed reinforcement learning-based control scheme to address the problem of frequency control and economic dispatch in power generation systems. The proposed control approach requires neither a priori system model knowledge nor the mathematical formulation of the generation cost functions. Due to not requiring the generation cost models, the control scheme is capable of dealing with scenarios in which the cost functions are hard to formulate and/or non-convex. Furthermore, it is privacy-preserving, i.e. none of the units in the network needs to communicate its cost function and/or control policy to its neighbors. To realize this, we propose an actor-critic algorithm with function approximation in which the actor step is performed individually by each unit with no need to infer the policies of others. Moreover, in the critic step each generation unit shares its estimate of the local measurements and the estimate of its cost function with the neighbors, and via performing a consensus algorithm, a consensual estimate is achieved. The performance of our proposed control scheme, in terms of minimizing the overall cost while persistently fulfilling the demand and fast reaction and convergence of our distributed algorithm, is demonstrated on a benchmark case study.

Microgrids are a promising alternative to the traditional distribution systems due to their highly desirable features, such as, reliability, resiliency, and efficiency. This paper covers the design, simulation, and economic analysis of a theoretically designed modern, mixed-use commercial and residential building on a feeder in Charleston, SC, USA. The designed system is simulated in PSCAD/EMTDC. The system combines a natural gas CHP turbine and generator block set, solar photovoltaics (PV), and a battery energy storage system (BESS). It is planned to provide power through a DC lighting bus and an AC to several different commercial load profiles as well as 40 apartments of varying sizes. Additionally, a comprehensive economic analysis is completed with available or estimated pricing to prove the feasibility of such a project.

Traditionally, the management of power distribution networks relies on the centralized implementation of the optimal power flow and, in particular, the minimization of the generation cost and transmission losses. Nevertheless, the increasing penetration of both renewable energy sources and independent players such as ancillary service providers in modern networks have made this centralized framework inadequate. Against this background, we propose a noncooperative game-theoretic framework for optimally controlling energy storage systems (ESSs) in power distribution networks. Specifically, in this paper we address a power grid model that comprehends traditional loads, distributed generation sources and several independent energy storage providers, each owning an individual ESS. Through a rolling-horizon approach, the latter participate in the grid optimization process, aiming both at increasing the penetration of distributed generation and leveling the power injection from the transmission grid. Our framework incorporates not only economic factors but also grid stability aspects, including the power flow constraints. The paper fully describes the distribution grid model as well as the underlying market hypotheses and policies needed to force the energy storage providers to find a feasible equilibrium for the network. Numerical experiments based on the IEEE 33-bus system confirm the effectiveness and resiliency of the proposed framework.

Currently, little attention has been carried out to address the feasibility of in-network caching in Information-Centric Networking (ICN) for the design and real-world deployment of future networks. Towards this line, in this paper, we propose a beneficial caching scheme in ICN by storing no more than a specific number of replicas for each content. Particularly, to realize an optimal content distribution for deploying caches in ICN, a content can be cached either partially or as a full-object corresponding to its request arrival rate and data traffic. Also, we employ a utility-based replacement in each content node to keep the most recent and popular content items in the ICN interconnections. The evaluation results show that the proposal improves the cache hit rate and cache diversity considerably, and acts as a beneficial caching approach for network and service providers in ICN. Specifically, the proposed caching mechanism is easy to deploy, robust, and relevant for the content-based providers by enabling them to offer users high Quality of Service (QoS) and gain benefits at the same time.

The intelligent electronic devices widely deployed across the distribution network are inevitably making the feeder automation (FA) system more vulnerable to cyber-attacks, which would lead to disastrous socio-economic impacts. This paper proposes a three-stage game-theoretic framework that the defender allocates limited security resources to minimize the economic impacts on FA system while the attacker deploys limited attack resources to maximize the corresponding impacts. Meanwhile, the probability of successful attack is calculated based on the Bayesian attack graph, and a fault-tolerant location technique for centralized FA system is elaborately considered during analysis. The proposed game-theoretic framework is converted into a two-level zero-sum game model and solved by the particle swarm optimization (PSO) combined with a generalized reduced gradient algorithm. Finally, the proposed model is validated on distribution network for RBTS bus 2.

Resilience is regarded nowadays as the ideal solution that can be envisaged by sociotechnical systems for coping with potential threats and crises. This being said, gaining and maintaining this ability is not always easy, given the multitude of risks driving the adverse and challenging events. This paper aims to propose a method consecrated to the assessment of risks directly affecting resilience. This work is conducted within the framework of risk assessment and resilience engineering approaches. A 5×5 matrix, dedicated to the identification and assessment of risk factors that constitute threats to the system resilience, has been elaborated. This matrix consists of two axes, namely, the impact on resilience metrics and the availability and effectiveness of resilience planning. Checklists serving to collect information about these two attributes are established and a case study is undertaken. In this paper, a new method for identifying and assessing risk factors menacing directly the resilience of a given system is presented. The analysis of these risks must be given priority to make the system more resilient to shocks.

The article is devoted to the analysis of the use of blockchain technology for self-organization of network communities. Network communities are characterized by the key role of trust in personal interactions, the need for repeated interactions, strong and weak ties within the network, social learning as the mechanism of self-organization. Therefore, in network communities reputation is the central component of social action, assessment of the situation, and formation of the expectations. The current proliferation of virtual network communities requires the development of appropriate technical infrastructure in the form of reputation systems - programs that provide calculation of network members reputation and organization of their cooperation and interaction. Traditional reputation systems have vulnerabilities in the field of information security and prevention of abusive behavior of agents. Overcoming these restrictions is possible through integration of reputation systems and blockchain technology that allows to increase transparency of reputation assessment system and prevent attempts of manipulation the system and social engineering. At the same time, the most promising is the use of blockchain-oracles to ensure communication between the algorithms of blockchain-based reputation system and the external information environment. The popularization of blockchain technology and its implementation in various spheres of social management, production control, economic exchange actualizes the problems of using digital technologies in political processes and their impact on the formation of digital authoritarianism, digital democracy and digital anarchism. The paper emphasizes that blockchain technology and reputation systems can equally benefit both the resources of government control and tools of democratization and public accountability to civil society or even practices of avoiding government. Therefore, it is important to take into account the problems of political institutionalization, path dependence and the creation of differentiated incentives as well as the technological aspects.

The steady decline of IP transit prices in the past two decades has helped fuel the growth of traffic demands in the Internet ecosystem. Despite the declining unit pricing, bandwidth costs remain significant due to ever-increasing scale and reach of the Internet, combined with the price disparity between the Internet's core hubs versus remote regions. In the meantime, cloud providers have been auctioning underutilized computing resources in their marketplace as spot instances for a much lower price, compared to their on-demand instances. This state of affairs has led the networking community to devote extensive efforts to cloud-assisted networks - the idea of offloading network functionality to cloud platforms, ultimately leading to more flexible and highly composable network service chains.We initiate a critical discussion on the economic and technological aspects of leveraging cloud-assisted networks for Internet-scale interconnections and data transfers. Namely, we investigate the prospect of constructing a large-scale virtualized network provider that does not own any fixed or dedicated resources and runs atop several spot instances. We construct a cloud-assisted overlay as a virtual network provider, by leveraging third-party cloud spot instances. We identify three use case scenarios where such approach will not only be economically and technologically viable but also provide performance benefits compared to current commercial offerings of connectivity and transit providers.

The purpose of this study was to propose a model of development of trust in social robots. Insights in interpersonal trust were adopted from social psychology and a novel model was proposed. In addition, this study aimed to investigate the relationship among trust development and self-esteem. To validate the proposed model, an experiment using a communication robot NAO was conducted and changes in categories of trust as well as self-esteem were measured. Results showed that general and category trust have been developed in the early phase. Self-esteem is also increased along the interactions with the robot.

In order to standardize and describe vulnerability information in detail as far as possible and realize knowledge sharing, reuse and extension at the semantic level, a vulnerability ontology is constructed based on the information security public databases such as CVE, CWE and CAPEC and industry public standards like CVSS. By analyzing the relationship between vulnerability class and weakness class, inference rules are defined to realize knowledge inference from vulnerability instance to its consequence and from one vulnerability instance to another vulnerability instance. The experimental results show that this model can analyze the causal and congeneric relationships between vulnerability instances, which is helpful to repair vulnerabilities and predict attacks.