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In the field of vehicular communication, the Internet of Vehicles (IoV) serves as a new era that guarantees increased connectivity, efficiency, and safety. The modern area and new technology have their challenges and constraints, though. This paper thoroughly examines these constraints significantly; we show how blockchain technology is being used to overcome them. This paper primarily explores the complexities of Blockchain-enabled Internet of Vehicles (BIoV) architectures, the applications they serve, and the robust security features they provide through a systematic literature review (SLR). In addition, we look at the several ways that blockchain and IoV might be integrated and investigate the subtle factors that should be considered when choosing consensus algorithms to maximize performance on different blockchains. This paper also addresses the methods and tools used to identify and avoid fraudulent activities in BIoV networks at a maximum level of security. It also reveals the wide range of BIoV applications and analyzes the different security levels they provide. In closing, we give an idea of the possibilities that will continue to develop the blockchain and IoV environment, reducing the roadblocks and advancing this combination toward a more secure, effective, and connected future for vehicle communication systems.

The security of Industrial Supply Chain (ISC) has emerged through the integration of Industrial Internet of Things (IIoT) and Blockchain (BC) technology. This new era involves effectively protecting IIoT systems from various threats and ensuring their smooth operation and resilience against potential cyber-attacks. Within the ISC ecosystem, combining machine learning (ML)-based security models for cyber-attack detection can play a crucial role in enhancing the ISC security and proactively identifying potential threats. This paper presents a BC-enabled ISC that embed ML security model integrated within a multi-layered approach. We conducted a comparative study and performance analysis of several ML classification techniques, with a focus on supervised methods to identify the lightweight model for cyber-attack detection suitable for deployment in resource-constrained IIoT environment. We investigate the performance of Gaussian Naive Bayes (NB), K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), and three ensemble techniques, namely Bagging, Stacking, and Boosting. The study employs the WUSTL-IIOT-2021 imbalanced dataset, which contains samples representing four types of attacks, including denial of service (DoS), SQL injection, reconnaissance, and backdoor. The paper addresses the imbalance in class representation by customizing the dataset for training and testing the ML models. Both Mutual Information (MI) and Extra-trees (ET) are applied as a one-stage ensemble feature selection. The performance of the ML models are investigated using classification accuracy (Acc), precision, recall, F1 score, Matthews correlation coefficient (MCC), model size (Mem), training time (TT) and prediction time (PT).

With the wide adoption of cryptocurrency, blockchain technologies have become the foundation of such digital currencies. However, this adoption has been accompanied by a surge in cryptocurrency fraud, causing significant losses to financial organizations and individuals. One way to mitigate these losses is to use Federated Learning (FL) techniques to detect fraudulent cryptocurrency transactions. This paper provides an overview of secure, privacy-preserving, and scalable Blockchain-based Federated Learning (BCFL) as a promising solution for slowing the exponential growth of cryptocurrency fraud. BCFL enables multiple entities to collaboratively train machine learning models for detecting fraudulent cryptocurrency transactions without sharing their private data, thus preserving privacy. However, Integrating differential privacy and Secure Multi-party computation (SMPC) models in BCFL presents an additional scalability challenge. This study provides an overview of BCFL, evaluating existing research on its security, privacy, and scalability challenges in detecting cryptocurrency fraud. The review explores existing research and various methodologies, highlighting advancements and challenges in creating effective, privacy-conscious fraud detection solutions for cryptocurrency transactions. We first discuss the current state of BCFL in fraud detection, along with its potential advantages and limitations, and then discuss the existing research gaps. In particular, this paper examines various BCFL frameworks, consensus algorithms, and block architectures, emphasizing their strengths and limitations in the context of cryptocurrency fraud detection to develop scalable and privacy-preserving solutions. We compare various solutions that address scalability and privacy challenges in BCFL, including adopting a geographically distributed cloud computing model that utilizes SMPC and lightweight consensus algorithms and protocols to manage computational overheads.

Nowadays, passwords play an important role in ensuring practical security. Password strength meters (PSMs) are typical and well-known tools developed to help users estimate password strength. Among existing PSMs, Markov-based PSMs offer high accuracy and reliability. However, these PSMs are often compared with other types of PSMs, and no study has compared their accuracy in detail. Two types of Markov models introduced in previous studies are the Simple Markov Model (SMM) and the Layered Markov Model (LMM). When calculating the probability of a password, these models consider two factors: character position and password length. However, these two models do not cover all possible cases of these two factors. In this paper, we introduce the application of two new Markov models, which can be seen as “hybrid models” of SMM and LMM, into PSMs, called Simple Markov Model with password length consideration (SMMl) and unique Layered Markov Model (uLMM), to cover all the missing cases. Then, we present two specific scenarios and compare the effectiveness of four types of Markov models in evaluating passwords based on these two scenarios. The experimental results indicate that the SMMl model, one of the two models proposed in this paper, yields the best effectiveness. This result also suggests that the number of samples of sub-string sequences obtained during the training process affects the effectiveness of password evaluation. Therefore, we conduct a detailed analysis related to this issue. These results will support us in developing new PSMs in the future.

This research paper intends to provide real-life applications of Generative AI (GAI) in the cybersecurity domain. The frequency, sophistication and impact of cyber threats have continued to rise in today’s world. This ever-evolving threat landscape poses challenges for organizations and security professionals who continue looking for better solutions to tackle these threats. GAI technology provides an effective way for them to address these issues in an automated manner with increasing efficiency. It enables them to work on more critical security aspects which require human intervention, while GAI systems deal with general threat situations. Further, GAI systems can better detect novel malware and threatening situations than humans. This feature of GAI, when leveraged, can lead to higher robustness of the security system. Many tech giants like Google, Microsoft etc., are motivated by this idea and are incorporating elements of GAI in their cybersecurity systems to make them more efficient in dealing with ever-evolving threats. Many cybersecurity tools like Google Cloud Security AI Workbench, Microsoft Security Copilot, SentinelOne Purple AI etc., have come into the picture, which leverage GAI to develop more straightforward and robust ways to deal with emerging cybersecurity perils. With the advent of GAI in the cybersecurity domain, one also needs to take into account the limitations and drawbacks that such systems have. This paper also provides some of the limitations of GAI, like periodically giving wrong results, costly training, the potential of GAI being used by malicious actors for illicit activities etc.

Sixth-generation (6G) communication is emerging as a seamless and massive connection of almost all digital devices. Vehicles, which are extensively linked with human mobility, must keep up with a certain technological pace to maintain compatibility with the 6G era. 6G is also expected to potentially revolutionize Vehicle-to-Everything (V2X) communication. However, this modernization will surface several security challenges in the complex heterogeneous architecture of V2X communication in 6G. Similarly, the expansion of V2X is expected to introduce its own unconventional security risks and vulnerabilities. Therefore, the current paper aims to provide an overview of the security challenges associated with and solutions for V2X communication in the upcoming 6G era to visualize the future of this research domain. This paper discusses the architecture and standards utilized in 6G-enabled V2X communications and provisions a comprehensive analysis of V2X security in the Confidentiality, Integrity, Availability, Authentication, and Access Control (CIA3) domains. Through this process, we analyze the impact of the emerging technological concepts of Blockchain and Federated Learning (FL) in 6G-enabled V2X communication. Therefore, we propose a Blockchain-enabled FL-based generic security architecture for V2X communication in 6G networks. Ultimately, this review highlights the key lessons that have been learned and the future research directions in the domain of security of V2X communications in the 6G including Privacy in 3D Fog Computing, Privacy in Augmented Reality, Secure Software Defined Networking (SDN), Physical Layer Security in the THz Spectrum, SUMO (Simulation of Urban MObility), and Intrusion Detection using AI.

Contemporary mobile networks, offering advanced services such as highly dependable and fast communication (URLLC) and extensive device-to-device connectivity (mMTC), are paving the way for the upcoming 6G era. These networks are expanding their capabilities beyond traditional voice and short messaging services, enabling diverse devices to connect to the cellular network. However, with this increased connectivity comes a heightened vulnerability at the radio interface, which is the primary access medium for mobile network communication. This research work focuses on safeguarding the availability of the radio interface in the face of emerging threats. Threats to radio interface availability can originate either directly from exploiting the 3GPP radio protocol stack within base stations or indirectly through the IP protocol stack carried over the user plane. In particular, this research paper delves into user plane DDoS attacks leveraging the IP protocol stack to generate excessive traffic. It introduces a novel detection method situated within the Radio Access Network (RAN). This method analyzes the patterns of radio protocols and their functionalities to identify user plane DDoS attacks initiated from User Equipment (UEs). Importantly, the method does not rely on directly inspecting user plane packets like IP packets but rather leverages the characteristics of 3GPP radio protocols (e.g., MAC, RLC, PDCP) to detect IP DDoS attacks closer to their origin. This early detection capability helps prevent DDoS traffic from propagating to the transport network. The implications of this research extend beyond the current generation of networks, as it lays a foundation for enhancing security in the forthcoming 6G networks. As 6G aims to deliver even more advanced services and connectivity across a diverse array of devices, the robust security measures proposed in this work will be instrumental in ensuring the reliability and availability of these cutting-edge networks. The analysis employed in this paper showcase the performance with accuracy of 98.9% for DDoS attack detection.

Supply chain as an industry has gone through four-fold changes in the last century. Born as a bare-bones structure in 1.0 it grew to incorporate some form of record preservation in 2.0 and then integrated communication between two entities in 3.0. Supply chain 4.0, the current one, has total global integration of multiple entities with the records digitised. But increasing entities and pipelines, means increasing complexities, overhead and soft spots. In this paper, a systematic literature review is done with the objective of analysing existing Supply Chain 4.0. The focus of the paper is the usage of blockchain technology in the electronic industry to provide a decentralised architecture. Several papers were compared on the basis of different schemas like the type of blockchain network used, platform deployed on, security of frameworks, representation of unique identity, testing authenticity, working implementation, cost of implementation, etc. The pros and cons of various privacy and security methodologies are also explored and discussed. The paper also discusses the open issues and challenges in the same area of interest. Finally, the paper outlines the future scope to be delved into as a part of the future research.

The novel Vehicular Cloud Network (VCN) is a close combination of Vehicular Ad hoc Network, Cloud Computing, and Edge Computing. However, VCN is facing the enormous challenge of security and privacy before it is generalized. To address these issues, VCN communication characteristics and security requirements are summarized first. Secondly, the paper conducts an in-depth analysis of the existing security problems and solutions in VCN based on several attack models, including routing, data, identity, DoS, and hostile attacks. And we summarize the security solutions into two categories: security defense measures based on identity authentication and access control, and security protection measures based on traffic detection and anomaly detection. Thirdly, this paper proposes a reliable VCN security architecture consisting of three types of clouds: vehicular, edge, and central clouds. Moreover, the VCN architecture combines a Vehicle Intrusion Detection and Prevention System with a Vehicular Cloud Security Management Platform to ensure the security of VCN. Last but not least, some open issues worth investigating are discussed.

Fifth-generation technology standard for broadband cellular networks, 5G, delivers a significant increase in data speeds and capacity, as well as new capabilities such as higher energy efficiency, lower latency, and the ability to connect a large number of devices. These advances come with a new set of security challenges, as 5G networks will be more complex and integrated with critical infrastructure than previous generations. In order to correctly address such challenges there is the need for fine-grained threat models, that collect a set of well-detailed threats, each of them clearly addressing a system component, taking into account how components are connected and interact with each other, the specific technology and/or the protocols are involved. A fine-grained threat model can be used to support the definition of a penetration testing plan or to identify and verify the effectiveness of technical countermeasures. This paper extends an existing automated threat modelling methodology focusing on 5G architecture and defines a process to build in a systematic way the catalogue of threats on which the technique relies. In order to obtain such results, we extended our modelling technique, in order to model 5G architectures, defined a process to extend our methodology to address additional domains and applied the approach to a concrete case study, applying our technique to a common 5G open-source architecture proposed by our industrial partner. The main contribution of this paper can be summarized as follows: 1) technique to systematically produce an extension of our modelling technique and a threat catalogue for a specific Domain; 2) 5G systems threat catalogue; 3) 5G systems graph-based modelling technique. As an additional result, we validated our approach, applying our technique in a real context and involving industrial experts for the evaluation of the generated fine-grained threat model.

In developing countries around the globe, the number of vehicle users has exceeded the optimum threshold and is increasing rapidly due to speedy economic growth. Consequently, roads are getting more congested in the cities. This has resulted in increased difficulties in meeting the minimum parking demands. Moreover, most of the parking lots are at the underground level of buildings. Due to the lack of natural light sources, there is a need for a massive power source to light up a large quantity of lighting equipment continuously. This results in waste of energy, equipment losses and causes a tremendous financial burden on property management. However, an IoT driven parking management can significantly aid in the utilization of parking resources. Availability of a sensor based smart parking spot with its location information identified and transmitted using wireless communication technology can immensely benefit the vehicle owners as well as parking spot authorities. It is a comprehensive solution both for the user and the owner of the parking space. Besides, it can fulfill the lighting requirement of the parking lot with minimized energy. Some eminent features of such IoT based systems are online reservations, user authentication for ensuring security, parking guidance, online payment, and energy saving. This paper reviews IoT driven smart parking management systems. It gives insights into various approaches for the parking system, related technologies, widely used components, communication standards and concerned system security issues. Besides, the paper gives researchers a direction for future work as it directs towards some open issues in this area.

This paper provides a comprehensive survey of different techniques for Deoxyribonucleic acid (DNA) -based cryptography and steganography. DNA-based cryptography is an emerging field that utilizes DNA molecules’ massive parallelism and vast storage capacity to encode and decode information. The field has gained significant attention in recent years due to its potential advantages over traditional cryptographic methods, such as high storage capacity, low error rate, and resistance to environmental factors. In this paper, we review three types of DNA-based cryptography: natural DNA cryptography, pseudo-DNA cryptography, and DNA-based steganography. For each technique, we discuss its advantages and limitations, as well as future directions for research. Our goal is to contribute to a better understanding of the applications and limitations of using DNA for cryptographic purposes. We believe that our analysis will be useful for researchers working on developing new techniques for secure data transmission using DNA molecules.

In recent years, there has been a trend to integrate networking and computing systems, whose management is getting increasingly complex. Resource allocation is one of the crucial aspects of managing such systems and is affected by this increased complexity. Resource allocation strategies aim to effectively maximize performance, system utilization, and profit by considering virtualization technologies, heterogeneous resources, context awareness, and other features. In such complex scenario, security and dependability are vital concerns that need to be considered in future computing and networking systems in order to provide the future advanced services, such as mission-critical applications. This paper provides a comprehensive survey of existing literature that considers security and dependability for resource allocation in computing and networking systems. The current research works are categorized by considering the allocated type of resources for different technologies, scenarios, issues, attributes, and solutions. The paper presents the research works on resource allocation that includes security and dependability, both singularly and jointly. The future research directions on resource allocation are also discussed. The paper shows how there are only a few works that, even singularly, consider security and dependability in resource allocation in the future computing and networking systems and highlights the importance of jointly considering security and dependability and the need for intelligent, adaptive and robust solutions. This paper aims to help the researchers effectively consider security and dependability in future networking and computing systems.

In this paper, we present a hardware implementation and an evaluation of the effectiveness and feasibility of a smart jamming attack that targets specific uplink physical channels to destabilize 5G communication systems. Using software defined radio (SDR), we examine the susceptibility of the 5G Physical Uplink Shared Channel (PUSCH) to a smart jamming attack as well as the impact of such an attack on user equipment (UE) throughput. The smart jamming attack is designed to exploit the radio access procedure by: 1) identifying the user’s cell radio network temporary identifier (C-RNTI); 2) decoding the physical downlink control channel (PDCCH) information containing the specific UE resources; and 3) generating a quadrature phase shift keying (QPSK) modulated Orthogonal Frequency-Division Multiplexing (OFDM) waveform to effectively deny uplink access. A detailed description of the implementation of the overall testbed used to evaluate and quantify the effectiveness of the custom implementation of a smart jamming attack is presented in this paper. The evaluation results show that the smart jamming attack successfully denied uplink access by reducing the throughput of a specific UE by 100%.

The inherent security and computational demands of classical consensus protocols are often presumed impervious to various forms of attack. However, quantum computing advancements present considerable threats to the assumed attack resistance of classical security strategies currently deployed within blockchain systems. Adopting consensus algorithms fortified with post-quantum security measures can significantly enhance traditional blockchains’ privacy and security dimensions. Notable advantages of such post-quantum solutions in blockchain consensus include accelerated transaction verification, clarified mining authorship, and resilience against quantum attacks. Yet, a comprehensive analysis of the implications of post-quantum solutions for blockchain consensus is notably absent in the existing scholarly discourse. This paper aims to bridge this gap by systematically reviewing Post-Quantum Blockchain Consensus (PQBC). The four primary contributions of this study are (i) a systematic approach to presenting a comprehensive overview of PQBC, (ii) the systematic selection and analysis of 29 key studies from an initial pool of 1192 papers, (iii) a critical review of methods, enhancements to security, scalability, trust, and privacy, as well as the evaluation employed for PQBC, and (iv) a discussion of primary gaps and prospective directions for future PQBC research.

For energy management and billing purposes, advanced metering infrastructure (AMI) requires periodic transmission of consumer power consumption readings by smart meters to the electric utility (EU). An efficient way for collecting readings is the change-and-transmit approach (CAT AMI) whereby readings are only transmitted if there is an enough change in consumption readings. CAT AMI, however, is plagued by malicious consumers who hack their smart meters to illegally lower their electricity bills by falsifying their readings. These attacks on the AMI could have bad economic consequences and impair the performance of the power grid if these readings are used for managing the grids. Machine learning models can be used to detect false readings but this requires disclosing consumers’ CAT readings to the EU to evalaute the model. However, disclosing the consumers’ readings jeopardizes consumers’ privacy due to the fact that these readings can reveal sensitive information about consumers’ lifestyles, e.g., their presence or absence, the appliances they use, etc. The problems of detecting power theft while protecting the consumers’ privacy in CAT AMI is investigated in this paper. First, a dataset of actual readings to generate a benign dataset is developped followed by proposing new cyber-attacks tailored for CAT AMI to generate malicious samples. Then, two deep-learning detectors using a baseline model (CNN) and a CNN-GRU model are trained to detect power thefts in CAT AMI. To preserve consumers’ privacy, the paper develops an approach to enable the EU to evaluate the detector using encrypted data without being able to learn the readings. Extensive experiments were carried out to assess our proposal, and the results indicate that our proposal is capable of accurately identifying malicious consumers with acceptable overhead while preserving the privacy of the consumers. Specifically, comparing to CNN model, our CNN-GRU model increases the detection rate from 93.85% to 97.14% and $HD$ from to 87.7% to 94.28%, respectively.

The Fifth Generation (5G) of mobile networks offers new and advanced services with stricter requirements. Multi-access Edge Computing (MEC) is a key technology that enables these new services by deploying multiple devices with computing and storage capabilities at the edge of the network, close to end-users. MEC enhances network efficiency by reducing latency, enabling real-time awareness of the local environment, allowing cloud offloading, and reducing traffic congestion. New mission-critical applications require high security and dependability, which are rarely addressed alongside performance. This survey paper fills this gap by presenting 5G MEC’s three aspects: security, dependability, and performance. The paper provides an overview of MEC, introduces taxonomy, state-of-the-art, and challenges related to each aspect. Finally, the paper presents the challenges of jointly addressing these three aspects.

This paper surveys security threats to 5G-enabled wireless access networks for social robots in public spaces (SRPS). The use of social robots (SR) in public areas requires specific Quality of Service (QoS) planning to meet its unique requirements. Its 5G threat landscape entails more than cybersecurity threats that most previous studies focus on. This study examines the 5G wireless RAN for SRPS from three perspectives: SR and wireless access points, the ad hoc network link between SR and user devices, and threats to SR and users’ communication equipment. The paper analyses the security threats to confidentiality, integrity, availability, authentication, authorisation, and privacy from the SRPS security objectives perspective. We begin with an overview of SRPS use cases and access network requirements, followed by 5G security standards, requirements, and the need for a more representative threat landscape for SRPS. The findings confirm that the RAN of SRPS is most vulnerable to physical, side-channel, intrusion, injection, manipulation, and natural and malicious threats. The paper presents existing mitigation to the identified attacks and recommends including physical level security (PLS) and post-quantum cryptography in the early design of SRPS. The insights from this survey will provide valuable risk assessment and management input to researchers, industrial practitioners, policymakers, and other stakeholders of SRPS.

The main innovations for next-generation cellular networks are in the Radio Access Network (RAN). Here, the base station functionalities are split between a Radio Unit (RU) and a Distributed Unit (DU), resulting in a virtualized architecture where functions can be centralized close to the core for performance improvement and function extendibility. The fronthaul is the interface between RUs and DUs. It transports very sensitive data and is constrained by strict performance requirements. The clear-text nature of the fronthaul protocols and its direct encapsulation over Ethernet exposes the fronthaul to Layer 2 threats and vulnerabilities that can significantly threaten the operation of the RAN. This paper presents a detailed analysis of the transport network security in the fronthaul. It describes the threats and vulnerabilities that the fronthaul is exposed to and their overall network impact, thereby, elucidating the urgent need for Layer 2 security mechanisms. This paper introduces MACsec as a potential solution to protect the fronthaul. It outlines MACsec’s capabilities and limitations for threats protection, and its implementation challenges in the fronthaul network. Finally, this paper proposes three hardware architectures to fully secure the fronthaul using MACsec and evaluates their feasibility in Field-Programmable Gate Array (FPGA) devices and their impact on the network performance.

In recent years, the use of Software Defined Networking (SDN) has increased due to various network management requirements. Using SDN in computer network applications has brought several benefits to users, including lower operational costs, better hardware management, flexibility, and centralized network deployment. On the other hand, the Internet of Things (IoT) is another rapidly growing technology. Distributed and dynamic infrastructures are two critical characteristics of IoT. These characteristics lead to some challenges while using SDN in IoT in terms of security and privacy. In this paper, we address security and privacy issues and solutions for SDN-based IoT systems. We analyze the techniques used for defense in previous works to achieve an acceptable level of security and privacy protection in SDN-based IoT systems. In the data plane, SDN-based IoT papers have considered hashing and encryption techniques, in the control plane, certificate authority and access control have been analyzed, and in the application plane, attack detection, and authentication have been discussed. We also provide a statistical analysis of the existing work. This analysis shows that researchers have focused on certain areas more than others in recent years. The final analysis also highlights issues that previous researchers have ignored.

Machine learning (ML) and the internet of things (IoT) are among the most booming research directions. Smart cities, smart campuses (SCs), smart homes, smart cars, early warning systems (EWSs), etc.; or it could be called “Smart x” systems are implemented using ML and IoT. Those systems will alter how various world entities communicate with one another. This paper spots light on the significant roles of the IoT in SS. Also, it focuses on the importance of ML in IoT-based SS. Besides, an overview of smartness and IoT is presented. Then, this paper offers ML benchmarking along with a taxonomy that categorizes the ML models into linear and non-linear ones depending on the problem type (classification or regression). Afterward, the commonly utilized evaluation metrics are provided. In addition, this paper considers the trust techniques used for mitigating different security aspects in IoT networks, which play a crucial part in regulating the new era of communication. Moreover, two case studies devoting ML for IoT-based SS, namely IoT-based SC and IoT-based EWS, are considered for data collection and manipulation with guided research directions. Finally, the paper presents effective recommendations of ML’s significant roles in SC and earthquake EWS for interested scholars.

With recent advances in information and communication technology (ICT), the bleeding edge concept of digital twin (DT) has enticed the attention of many researchers to revolutionize the entire modern industries. DT concept refers to a digital representation of a physical entity that is able to reflect its physical behavior by applying platforms and bidirectional interaction of data in real-time. The remarkable deployment of the internet of things in the power grid has led to reliable access to information that improves its performance and equips it with a powerful tool for real-time data management and analysis. This paper aims to trace the continuous investigation and propose practical ideas in originating and developing DT technology, according to various application domains of power systems, and also describes the proposed solutions to deal with the challenges associated with DT. Indeed, with the development of modern cities, different energy layers such as transportation systems, smart grids, and microgrids have emerged facing various issues that challenge the multi-dimensional energy management system. For example, in transportation systems, traffic is a major problem that requires real-time management, planning, and analysis. In power grids, remote data transfer within the grid and also various analyzes needing real data are just some of the current challenges in the field. These problems can be cracked by providing and analyzing a real twin framework in each section. All in all, this paper aims to survey different applications of DT in the development of the various aspects of energy management within a city including transportation systems, power grids, and microgrids. Besides, the security of DT technology based on ML is discussed. It also provides a complete view for the readers to be able to develop and deploy a DT technology for various power system applications.

With the rapid increase in cyber attacks on industrial control systems, the significance of the application of cyber security controls and the evaluation of security against such attacks has also increased. Among them, cyber attacks on nuclear power plants (NPPs) can cause not only economic loss, but also human casualties. Thus, the application of cyber security controls is necessary for mitigating security threats, especially to NPPs. However, currently, there are limited resources pertaining to information protection, which is essential to uniformly deploy all the controls required to meet cyber security regulations. To overcome this challenge, effective cyber security controls need to be identified and adequate information protection resources must be allocated to each NPP. Although NPPs apply a differential security control according to its characteristics based on NEI 13–10 (Cyber Security Control Assessments), this alone is not only insufficient in reflecting the latest security threats, but also fails to confirm whether the security controls have actually mitigated such threats. To address this challenge, the Electric Power Research Institute (ETRI) developed the technical assessment methodology (TAM), which can be used to generate a quantitative score by assessing the effects of potential cyber attacks on an asset and the relevant security controls. This methodology allows for the application of differential security control based on the score to identify whether the security controls have actually mitigated the risks. Considering this context, the purpose of this paper is to conduct a comparative analysis of the results derived from applying security controls and assessing risks using only NEI 13–10 as well as both NEI 13–10 and TAM on the plant protection system of the nuclear power reactor APR1400. Furthermore, this paper discusses the scopes for subsequent research by addressing the limitations of the TAM and considerations for its use.

This paper investigates facilitating remote collection of a patient’s data in distributed system while protecting the security of the data, preserving the privacy of the patient’s ID, and preventing inference attack. The paper presents a novel framework called SPID stand for a Secure, ID Privacy, and Inference Threat Prevention Mechanisms for Distributed Systems. In designing this framework, we make the following novel contributions. The SPID presents a novel architecture that supports the use of a distributed set of servers owned by different service providers. The SPID allows the patient to access these servers using certificates generated by the patient. The SPID allows the patient to select one server to be the home server, and select a number of servers to be the foreign servers. The patient uses the foreign servers to upload data. The home server is responsible for collecting the patient’s data from the foreign servers and sending them to the healthcare provider. The SPID proposes a method for efficient verification of each request from the patient without searching in the server’s database for the verification key. This is done by using some of the Elliptic Curves Cryptography (ECC) properties. The SPID has been analyzed using a bench-marking tool and evaluated using queuing theory. The evaluation results indicate an efficient performance when the number of servers increases. We uses Shannon entropy method to measure the likelihood of the inference attack.

Over the past two years, the spread of COVID-19 has spurred the use of information and communication technologies (ICT) in aid of healthcare. The need to guarantee continuity to care has promoted research and industry activities aimed at developing solutions for the digitalization of the procedures to be performed to provide health services, even in emergency scenarios. Digital collection, transmission, and processing of health data represent the starting point for fulfilling this innovation process but also bring heterogeneous challenges. These motivations led to the elaboration of this work, which analyzes innovative and technological tools for the development of digital health (eHealth) through the collection of multisectoral literature, produced thanks to the cooperation of varied research groups, thus providing a multidisciplinary survey. Since digital health is expected to be one of the leading applications of the sixth-generation (6G) wireless cellular networks, this paper covers the related telecommunications aspects. Furthermore, the exploitation of artificial intelligence paradigms to elaborate massive amounts of biological data is examined. Given the extreme sensitivity of health data, this paper also investigates security and privacy issues. In particular, the main techniques and approaches to guarantee security properties (i.e., anonymity, responsibility, authentication, confidentiality, integrity, non-repudiation, and revocability) are studied. Applications involving innovative electromagnetic systems for healthcare and assisted living services are described to provide an example of an eHealth scenario leveraging ICT. Finally, the telemedicine-related regulations of the European Commission are analyzed, with particular reference to the General Data Protection Regulation (GDPR).

The integration of medical equipment into the Internet of Things (IoT) led to the introduction of Internet of Medical Things (IoMT). Variation of IoT devices have been equipped in medical facilities. These devices provided convenience to healthcare provider since they can continuously monitor their patients in real-time, while allowing them to have greater physical flexibility and mobility. However, users of healthcare services (such as patients and medical staff) often are less concerned about security issues associated with IoT. These alleviate existing problems and jeopardize the lives of their patients by making them susceptible to attacks. Furthermore, IoMT applications have direct access to healthcare services because it handles sensitive patient information. Therefore, it is extremely important to preserve and establish the security and privacy of IoMT. This further justifies the need to investigate and address the related issues. Despite existing literature on security and privacy mechanisms, the domain still requires more attention. Therefore, this paper aims to discuss the security and privacy principles, as well as challenges associated with IoMT. Besides, a comprehensive analysis of privacy and security solutions for IoMT is also presented. In addition, we introduced a novel taxonomy of IoMT security and privacy based on cyber security principles such as “govern,” “protect,” and “detect”. In conclusion, this paper provides a discussion on existing challenges and future direction for researchers.

As an indispensable part of intelligent transportation system, a traffic-sensitive navigation system can assist drivers in avoiding traffic congestion by providing navigation services. The navigation service provider (NSP) utilizes road condition information from nearby vehicles collected by roadside units (RSUs) to guide a vehicle through an optimal route provided by the navigation services. This paper mainly focuses on tackling three key issues in such navigation system. Firstly, it is essential to ensure the privacy of the vehicles’ personal identifiable information (PII). Secondly, the location, start point destination and route of the vehicle should not be leaked to RSUs and also should not be linked to its PII by RSUs and NSP. Thirdly, the process of vehicles obtaining the navigation services should be lightweight. Because of such problems, this paper proposes a dual privacy-preserving lightweight navigation system for vehicular networks through designing a novel signature scheme and combining other cryptographic primitives to keep dual privacy including PII and route related information. And the correctness analysis of the proposed system, security proof of the designed signature scheme adopted in the proposed system, and privacy analysis of the system are thoroughly provided. In addition, the performance of the proposed system is evaluated and compared with the existing systems to illustrate that the proposed system is efficient.

Model accuracy is the traditional metric employed in machine learning (ML) applications. However, privacy, fairness, and robustness guarantees are crucial as ML algorithms increasingly pervade our lives and play central roles in socially important systems. These four desiderata constitute the pillars of Trustworthy ML (TML) and may mutually inhibit or reinforce each other. It is necessary to understand and clearly delineate the trade-offs among these desiderata in the presence of adversarial attacks. However, threat models for the desiderata are different and the defenses introduced for each leads to further trade-offs in a multilateral adversarial setting (i.e., a setting attacking several pillars simultaneously). The first half of the paper reviews the state of the art in TML research, articulates known multilateral trade-offs, and identifies open problems and challenges in the presence of an adversary that may take advantage of such multilateral trade-offs. The fundamental shortcomings of statistical association-based TML are discussed, to motivate the use of causal methods to achieve TML. The second half of the paper, in turn, advocates the use of causal modeling in TML. Evidence is collected from across the literature that causal ML is well-suited to provide a unified approach to TML. Causal discovery and causal representation learning are introduced as essential stages of causal modeling, and a new threat model for causal ML is introduced to quantify the vulnerabilities introduced through the use of causal methods. The paper concludes with pointers to possible next steps in the development of a causal TML pipeline.

Many security standards and cryptographic solutions exist for different applications such as agriculture, aircraft, banking systems and etc. but a more effective and efficient solution can be given by combining existing technologies with blockchain. This work addresses the problems of previous works such as scalability, immutability, robustness, network latency, auditability, and traceability. Satoshi Nakamoto introduced Blockchain (BC) to tackle the Address Resolution Protocol (ARP) spoofing attacks, Distributed Denial of Service (DDoS), phishing problems and various security issues. Blockchain is a technology that stores the data using a chain of blocks in an encrypted form with hashing algorithms. It uses the decentralized architecture to store the information that helps users and customers to have transparency on records. The data is stored in a distributed ledger that is tamperproof and immutable. To amalgamate the research done so far, this paper presents a systematic review of ten different applications and tools used in blockchain. The applications include academics and education, agriculture, aircraft, banking, car sharing, e-voting, healthcare, Internet of Things (IoT), Intellectual Property Rights (IPR), and Supplychain (SC). Moreover, this paper presented a taxonomy for these applications and analyzed the implementation of tools used in different domains. Different open issues and challenges and key takeaways of blockchain technology were also highlighted. Hence, this paper helps give a new insight into working with blockchain and deciding on appropriate tools and approaches for a particular application.

Physical layer security (PLS) has become a popular topic in the research community as a complement to traditional security schemes. Particularly, by taking advantage of the channel’s symmetry, a robust ecosystem of security applications has developed in wireless communications. However, in power line communications (PLC), the general lack of channel symmetry has limited and hampered the development of physical layer techniques. In this paper, we address the cited constraint, by deriving a symmetric virtual channel impulse response (CIR) from the corresponding asymmetric PLC channel state information (CSI). In view of this, we propose a low-complexity and, to the best of our knowledge, the first CIR multilevel quantization algorithm that can be used in various PLC applications. While we contextualize our solution within the PLS domain, it has a wide applicability. Specifically, we start by analyzing the PLC channel path delays reciprocity, its relationship with topology changes, and its independence from all other power line characteristics. Then, the quality and viability of the proposed scheme are quantified, by comparing the bit mismatch rate (BMR) with its one-level quantization counterpart. The simulation results, under different topologies, confirm the performance of the proposed approach that can reduce the BMR by at least three orders of magnitude with respect to the one-level quantization, for noise levels below 90 dBuV. Finally, we conclude the paper by showing a few applications of the proposed solution and highlighting some future research directions.

Supply chain management (SCM) is a core corporate activity responsible for moving commodities and services from one point to another through a variety of stakeholders. The traditional SCM is based on a centralized approach managed at the central headquarter, and all other sub-offices get instructions from the main office. Some major issues with present SCM systems are security, transactional transparency, traceability, stakeholder involvement, product counterfeiting, additional delays, fraud, and instabilities. Blockchain (BC) emerges as a technology that can manage the data and build trust efficiently and transparently. It can also aid in transaction authorization and verification in the supply chain or payments without a third party. To address the present SCM issues, BC technology is a feasible solution. Motivated by the aforementioned considerations, in this paper, we present a survey on the adoption of BC in SCM. This paper undertakes a comprehensive analysis of the literature on BC characteristics, implementations, and business consequences in various SCM. This Blockchain-centered study, in particular, discloses the research state and delineates future research directions by studying and analyzing 97 up-to-date publications highlighting BC’s supply chain uses. Transparency and traceability, information sharing, product anti-counterfeiting, and building trust are the major aspects propelling BC’s implementation in SCM. Further, we analyzed various applications of SCM in which BC can be used as a probable technology to secure all transactions. Then, we have highlighted open issues and research challenges for adopting BC technology in SCM that open the doors for beginners eager to start work in this amazing area.

This paper proposes a blockchain-based supply chain information operation method to solve the current shortage of business information sharing in the supply chain. These approaches include the idea of blockchain application to realize the sharing of supply chain information, the supply chain architecture of production enterprises based on blockchain, and the hierarchical model of supply chain information flow based on blockchain. This paper proposes the information block recording method of internal and external data sources in the supply chain, including the composition structure and the analysis of the multisource data inside and outside the supply chain. In addition, the multisource data information block recording method is also proposed. Building a blockchain-based supply chain system can improve the integration and reconstruction capabilities of the supply chain. The information recording of the internal data of the supply chain and the external related multisource data in the block can improve the learnability of the supply chain. The internal and external information block connection of the supply chain system is the further integration and reconstruction of the supply chain information resources in the blockchain system. The information storage and access control of the blockchain-based supply chain is an information security guarantee system in the supply chain information sharing environment. Through the method of this study, the system can form a business architecture system based on blockchain-based supply chain information sharing.

Nowadays, health insurance has become an essential part of people’s lives as the number of health issues increases. Healthcare emergencies can be troublesome for people who can’t afford huge expenses. Health insurance helps people cover healthcare services expenses in case of a medical emergency and provides financial backup against indebtedness risk. Health insurance and its several benefits can face many security, privacy, and fraud issues. For the past few years, fraud has been a sensitive issue in the health insurance domain as it incurs high losses for individuals, private firms, and governments. So, it is essential for national authorities and private firms to develop systems to detect fraudulent cases and payments. A high volume of health insurance data in electronic form is generated, which is highly sensitive and attracts malicious users. Motivated by these facts, we present a systematic survey for Artificial Intelligence (AI) and blockchain-enabled secure health insurance fraud detection in this paper. This paper presents a taxonomy of various security issues in health insurance. We proposed a blockchain and AI-based secure and intelligent system to detect health insurance fraud. Then, a case study related to health insurance fraud is presented. Finally, the open issues and research challenges in implementing the blockchain and an AI-empowered health insurance fraud detection system is presented.

The concept of big data security is introduced in this article along with many features. It illustrates the need for security in healthcare systems as the volume of data increases continuously over the period of time. The necessity of big data security as well as several big data analytics phases highlighted. It covers many big data privacy-preserving strategies. Many digital storage solutions being used in today’s world are designed to work only with fixed format of the data. This paper introduces some methods for maintaining metadata obliviousness. The oblivious RAM technology mentioned in the research article address security concerns and it can be handled with the daily increase in data in several industries. Security needs are introduced at many phases of big data creation, such as information extraction, storage systems, and analytics of the information. Additionally, it presents several data recovery methods for recovering original data in the event of a data crash. This paper covers several data categorization methods for sorting data into normal and sensitive categories as well as methods for anomaly detection. It discusses the advantages and disadvantages of various security measures.

Future wireless networks (5G and beyond), also known as Next Generation or NextG, are the vision of forthcoming cellular systems, connecting billions of devices and people together. In the last decades, cellular networks have dramatically grown with advanced telecommunication technologies for high-speed data transmission, high cell capacity, and low latency. The main goal of those technologies is to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, smart cities, smart grids, advanced manufacturing, and many more. The key motivation of NextG networks is to meet the high demand for those applications by improving and optimizing network functions. Artificial Intelligence (AI) has a high potential to achieve these requirements by being integrated into applications throughout all network layers. However, the security concerns on network functions of NextG using AI-based models, i.e., model poisoning, have not been investigated deeply. It is crucial to protect the next-generation cellular networks against cybersecurity threats, especially adversarial attacks. Therefore, it needs to design efficient mitigation techniques and secure solutions for NextG networks using AI-based methods. This paper proposes a comprehensive vulnerability analysis of deep learning (DL)-based channel estimation models trained with the dataset obtained from MATLAB’s 5G toolbox for adversarial attacks and defensive distillation-based mitigation methods. The adversarial attacks produce faulty results by manipulating trained DL-based models for channel estimation in NextG networks while mitigation methods can make models more robust against adversarial attacks. This paper also presents the performance of the proposed defensive distillation mitigation method for each adversarial attack. The results indicate that the proposed mitigation method can defend the DL-based channel estimation models against adversarial attacks in NextG networks.

Next-generation communication networks, also known as NextG or 5G and beyond, are the future data transmission systems that aim to connect a large amount of Internet of Things (IoT) devices, systems, applications, and consumers at high-speed data transmission and low latency. Fortunately, NextG networks can achieve these goals with advanced telecommunication, computing, and Artificial Intelligence (AI) technologies in the last decades and support a wide range of new applications. Among advanced technologies, AI has a significant and unique contribution to achieving these goals for beamforming, channel estimation, and Intelligent Reflecting Surfaces (IRS) applications of 5G and beyond networks. However, the security threats and mitigation for AI-powered applications in NextG networks have not been investigated deeply in academia and industry due to being new and more complicated. This paper focuses on an AI-powered IRS implementation in NextG networks along with its vulnerability against adversarial machine learning attacks. This paper also proposes the defensive distillation mitigation method to defend and improve the robustness of the AI-powered IRS model, i.e., reduce the vulnerability. The results indicate that the defensive distillation mitigation method can significantly improve the robustness of AI-powered models and their performance under an adversarial attack.

The field of adversarial machine learning has experienced a near exponential growth in the amount of papers being produced since 2018. This massive information output has yet to be properly processed and categorized. In this paper, we seek to help alleviate this problem by systematizing the recent advances in adversarial machine learning black-box attacks since 2019. Our survey summarizes and categorizes 20 recent black-box attacks. We also present a new analysis for understanding the attack success rate with respect to the adversarial model used in each paper. Overall, our paper surveys a wide body of literature to highlight recent attack developments and organizes them into four attack categories: score based attacks, decision based attacks, transfer attacks and non-traditional attacks. Further, we provide a new mathematical framework to show exactly how attack results can fairly be compared.

The emergence of the Internet of Things (IoT) has enabled billions of devices that collect large amounts of data to be connected. Therefore, IoT security has fundamental requirements. One critical aspect of IoT security is data integrity. Cryptographic hash functions are cryptographic primitives that provide data integrity services. However, due to the limitations of IoT devices, existing cryptographic hash functions are not suitable for all IoT environments. As a result, researchers have proposed various lightweight cryptographic hash function algorithms. In this paper, we discuss advanced lightweight cryptographic hash functions for highly constrained devices, categorize design trends, analyze cryptographic aspects and cryptanalytic attacks, and present a comparative analysis of different hardware and software implementations. In the final section of this paper, we highlight present research challenges and suggest future research topics related to the design of lightweight cryptographic hash functions.

To this day, abstention rates continue to rise, largely due to the need to travel to vote. This is why remote e-voting will increase the turnout by allowing everyone to vote without the need to travel. It will also minimize the risks and obtain results in a faster way compared to a traditional vote with paper ballots. In fact, given the high stakes of an election, a remote e-voting solution must meet the highest standards of security, reliability, and transparency to gain the trust of citizens. In literature, several remote e-voting solutions based on blockchain technology have been proposed. Indeed, the blockchain technology is proposed today as a new technical infrastructure for several types of IT applications because it allows to remove the TTP and decentralize transactions while offering a transparent and fully protected data storage. In addition, it allows to implement in its environment the smart-contracts technology which is used to automate and execute agreements between users. In this paper, we are interested in reviewing the most revealing e-voting solutions based on blockchain technology.

The fifth-generation technology is called to support the next generation of wireless services and realize the “Internet of Everything” through Machine-Type Communications and Cellular Internet of Things optimizations. As part of these optimizations, Release 15 introduced a new mechanism, known as Early Data Transmission (EDT), that allows the transmission of data during the Random Access procedure. This feature, intended particularly for infrequent and small data transmissions, aims to reduce the latency and the power consumption of user equipments. Nonetheless, despite the importance of this novelty and the general agreement about its effectiveness, there are few papers in the literature that provide insight into its implementation and analyze the advantages and disadvantages of its two different implementation options (Control and User Plane). Moreover, although security is recognized as a crucial aspect for the correct deployment of this technology, we have not found any paper that reviews the security issues and features of this mechanism. As a consequence of such a lack of papers and the complexity of mobile network protocols, there is a divide between security experts and EDT researchers, that prevents the easy development of security schemes. To overcome this important gap, this paper offers a tutorial of EDT and its security, analyzing its main vulnerabilities and concluding with a set of recommendations for researchers and manufacturers. In particular, due to the simplifications in the protocols done by EDT, vulnerabilities such as packet injection, replay attacks and injection of fake values to disable EDT have been found.

In the last two decades, the number of strategies for planning the maintenance of power systems have increased considerably. As societal dependence on power system infrastructure continues to grow, there is an increased need to identify the best practices in the field of power system maintenance planning to ensure the continued reliable operation of the grid. This paper presents a comprehensive and systematic state-of-the-art review of advances in power system maintenance and the significance for the field of power system reliability. As the main contributions, this paper systematically organizes the published literature, and analyses the most relevant milestones in the context of power systems adequacy and security enhancement, producing a taxonomy of the different maintenance strategies. This includes detailing existing approaches for power system maintenance planning, and providing clear definitions, models, methods, and characteristics of maintenance policy. The review also includes the most relevant standards employed in the power system maintenance field. Finally, areas requiring further research are identified alongside emerging trends in power system maintenance, to inform industry practice and support further research.

The popularity and usage of Cloud computing is increasing rapidly. Several companies are investing in this field either for their own use or to provide it as a service for others. One of the results of Cloud development is the emergence of various security problems for both industry and consumer. One of the ways to secure Cloud is by using Machine Learning (ML). ML techniques have been used in various ways to prevent or detect attacks and security gaps on the Cloud. In this paper, we provide a Systematic Literature Review (SLR) of ML and Cloud security methodologies and techniques. We analyzed 63 relevant studies and the results of the SLR are categorized into three main research areas: (i) the different types of Cloud security threats, (ii) ML techniques used, and (iii) the performance outcomes. We have defined 11 Cloud security areas. Moreover, distributed denial-of-service (DDoS) and data privacy are the most common Cloud security areas, with a 16% level of use and 14%respectively. On the other hand, we found 30 ML techniques used, some used hybrid and others as standalone. The most popular ML used is SVM in both hybrid and standalone models. Furthermore, 60% of the papers compared their models with other models to prove the efficiency of their proposed model. Moreover, 13 different evaluation metrics were enumerated. The most applied metric is true positive rate and least used is training time. Lastly, from 20 datasets found, KDD and KDD CUP'99 are the most used among relevant studies.

Cyber-physical systems (CPS) are interconnected architectures that employ analog and digital components as well as communication and computational resources for their operation and interaction with the physical environment. CPS constitute the backbone of enterprise (e.g., smart cities), industrial (e.g., smart manufacturing), and critical infrastructure (e.g., energy systems). Thus, their vital importance, interoperability, and plurality of computing devices make them prominent targets for malicious attacks aiming to disrupt their operations. Attacks targeting cyber-physical energy systems (CPES), given their mission-critical nature within the power grid infrastructure, can lead to disastrous consequences. The security of CPES can be enhanced by leveraging testbed capabilities in order to replicate and understand power systems operating conditions, discover vulnerabilities, develop security countermeasures, and evaluate grid operation under fault-induced or maliciously constructed scenarios. Adequately modeling and reproducing the behavior of CPS could be a challenging task. In this paper, we provide a comprehensive overview of the CPS security landscape with an emphasis on CPES. Specifically, we demonstrate a threat modeling methodology to accurately represent the CPS elements, their interdependencies, as well as the possible attack entry points and system vulnerabilities. Leveraging the threat model formulation, we present a CPS framework designed to delineate the hardware, software, and modeling resources required to simulate the CPS and construct high-fidelity models that can be used to evaluate the system's performance under adverse scenarios. The system performance is assessed using scenario-specific metrics, while risk assessment enables the system vulnerability prioritization factoring the impact on the system operation. The overarching framework for modeling, simulating, assessing, and mitigating attacks in a CPS is illustrated using four representative attack scenarios targeting CPES. The key objective of this paper is to demonstrate a step-by-step process that can be used to enact in-depth cybersecurity analyses, thus leading to more resilient and secure CPS.

The widespread use of Unmanned Aerial Vehicles (UAV) has made the security and computing resource application efficiency of UAV a hot topic in the security field of the Internet of Things. In this paper, an optimized lightweight identity security authentication protocol, Optimized Identity Authentication Protocol (ODIAP) is proposed for Internet of Drones (IoD) networks. The protocol is targeted to the security risks faced by IoD networks, and proposes the security authentication mechanism consisting of 3 phases and 7 authentication processes, which enables the protocol has both forward and backward security, and can resist mainstream network attacks. Meanwhile, this paper fully considers the computational load and proposes the identity information generation and verification method based on the Chinese residual theorem, which reduces the computational load of resource-constrained nodes and shifts the complex computational process to server nodes with abundant computational resources. Moreover, after security protocol analysis and tool verification based on the automated security verification tool Proverif, the protocol in this paper has complete security. At the same time, the performance analysis and comparison with other mainstream protocols shows that this protocol effectively optimizes the use of computing resources without compromising security.

In the last decade, researchers, practitioners and companies struggled for devising mechanisms to detect cyber-security threats. Among others, those efforts originated rule-based, signature-based or supervised Machine Learning (ML) algorithms that were proven effective for detecting those intrusions that have already been encountered and characterized. Instead, new unknown threats, often referred to as zero-day attacks or zero-days, likely go undetected as they are often misclassified by those techniques. In recent years, unsupervised anomaly detection algorithms showed potential to detect zero-days. However, dedicated support for quantitative analyses of unsupervised anomaly detection algorithms is still scarce and often does not promote meta-learning, which has potential to improve classification performance. To such extent, this paper introduces the problem of zero-days and reviews unsupervised algorithms for their detection. Then, the paper applies a question-answer approach to identify typical issues in conducting quantitative analyses for zero-days detection, and shows how to setup and exercise unsupervised algorithms with appropriate tooling. Using a very recent attack dataset, we debate on i) the impact of features on the detection performance of unsupervised algorithms, ii) the relevant metrics to evaluate intrusion detectors, iii) means to compare multiple unsupervised algorithms, iv) the application of meta-learning to reduce misclassifications. Ultimately, v) we measure detection performance of unsupervised anomaly detection algorithms with respect to zero-days. Overall, the paper exemplifies how to practically orchestrate and apply an appropriate methodology, process and tool, providing even non-experts with means to select appropriate strategies to deal with zero-days.

There is an increasing drive to provide improved levels of trust within an Internet-of-Things (IoTs) environments, but the devices and sensors used tend to be limited in their capabilities for dealing with traditional cryptography methods. Resource constraints and security are often the two major concerns of IIoT (Industrial IoT applications and big data generation at the present time. The strict security measures are often not significantly resource-managed and therefore, negotiation normally takes place between these. Following this, various light-weight versions of generic security primitives have been developed for IIoT and other resource-constrained sustainability. In this paper, we address the authentication concerns for resource-constrained environments by designing an efficient authentication protocol. Our authentication scheme is based on LiSP (light-weight Signcryption Protocol); however, some further customization has been performed on it to make it more suitable for IIoT-like resource-constrained environments. We use Keccack as the hash function in the process and Elli for light-weight public-key cryptography. We name our authentication scheme: Extended light-weight Signcryption Protocol with Keccack (LiSP-XK). The paper outlines a comparative analysis on our new design of authentication against a range of state-of-the-art schemes. We find the suitability of LiSP-XK for IIoT like environments due to its lesser complexity and less energy consumption. Moreover, the signcryption process is also beneficial in enhancing security. Overall the paper shows that LiSP-XK is overall 35% better in efficiency as compared to the other signcryption approaches.

With the increased number and reduced cost of smart devices, Internet of Things (IoT) applications such as smart home (SHome) are increasingly popular. Owing to the characteristics of IoT environments such as resource constrained devices, existing authentication solutions may not be suitable to secure these environments. As a result, a number of authentication solutions specifically designed for IoT environments have been proposed. This paper provides a critical analysis of existing authentication solutions. The major contributions of the paper are as follows. First, it presents a generic model derived from an SHome use-case scenario. Secondly, based on the model, it performs a threat analysis to identify possible means of attacks. The analysis leads to the specification of a set of desirable security requirements for the design of authentication solutions for SHome. Thirdly, based on the requirements, existing authentication solutions are analysed and some ideas for achieving effective and efficient authentication in IoT environments are proposed.

Blockchain technology is becoming increasingly attractive to the next generation, as it is uniquely suited to the information era. Blockchain technology can also be applied to the Internet of Things (IoT). The advancement of IoT technology in various domains has led to substantial progress in distributed systems. Blockchain concept requires a decentralized data management system for storing and sharing the data and transactions in the network. This paper discusses the blockchain concept and relevant factors that provide a detailed analysis of potential security attacks and presents existing solutions that can be deployed as countermeasures to such attacks. This paper also includes blockchain security enhancement solutions by summarizing key points that can be exploited to develop various blockchain systems and security tools that counter security vulnerabilities. Finally, the paper discusses open issues relating to and future research directions of blockchain-IoT systems.

Vehicular ad hoc Networks (VANETs) are an emerging technology with robust applications in Intelligent Transport System. It consists of smart vehicles and roadside infrastructure which communicate through open access wireless networks. The rapid growth in vehicles results in VANETs becoming large-scale, dynamic, heterogeneous and it is possible for the attacker to harm vehicular communication which leads to life-endangering situations. VANETs must ensure secured vehicular communication using strong privacy-preserving and authentication mechanisms. In addition, efficiency is also a major concern in VANETs. Numerous studies have been discussed in literature for VANETs privacy and security. Nevertheless, no one covered the privacy and security issues as a holistic view. In this paper, we have given a detailed background overview of VANETs. Details of different possible attacks in VANET are also given in this paper. We have classified privacy and authentication schemes into four major groups with their security mechanisms, security requirements, strength, limitations, attacks countermeasures and performance measures. Finally, we have discussed some open issues in the field of VANETs security.

Blockchain has recently attracted significant academic attention in research fields beyond the financial industry. In the Internet of Things (IoT), blockchain can be used to create a decentralized, reliable, and secure environment. The use of blockchain in IoT applications is still in its early stages, particularly at the low end of the computing spectrum. As a result, the future roadmap is hazy, and several challenges and questions must be addressed. Several articles combining blockchain technology with IoT have recently been released, but they are limited to shallow technological potential discussions, with very few providing an in- depth examination of the complexities of implementing blockchain technology for IoT. Therefore, this paper aims to coherently and comprehensively provide current cutting-edge efforts in this direction. It provides a literature review of IoT and blockchain integration by examining current research issues and trends in the applications of blockchain-related approaches and technologies within the IoT security context. We have surveyed published articles from 2017 to 2021 on blockchain-based solutions for IoT security, taking into consideration different security areas and then, we have organized the available articles according to these areas. The surveyed articles have been chronologically organized in tables for better clarity. In this paper, we try to investigate the vital issues and challenges to the integration of IoT and blockchain, and then investigate the research efforts that have been conducted so far to overcome these challenges.