Biblio

The Internet of Things poses some of the biggest security challenges in the present day. Companies, users and infrastructures are constantly under attack by malicious actors. Increasingly, attacks are being launched by hacking into one vulnerable device and hence disabling entire networks resulting in great loss. A strong identity management framework can help better protect these devices by issuing a unique identity and managing the same through its lifecycle. Identity of Things (IDoT) is a term that has been used to describe the importance of device identities in IoT networks. Since the traditional identity and access management (IAM) solutions are inadequate in managing identities for IoT, the Identity of Things (IDoT) is emerging as the solution for issuance of Identities to every type of device within the IoT IAM infrastructure. This paper presents the survey of recent research works proposed in the area of device identities and various commercial solutions offered by organizations specializing in IoT device security.

Fog computing is defined as a decentralized infrastructure that locations storage and processing aspects at the side of the cloud, the place records sources such as software customers and sensors exist. The Fog Computing is the time period coined via Cisco that refers to extending cloud computing to an area of the enterprise’s network. Thus, it is additionally recognized as Edge Computing or Fogging. It allows the operation of computing, storage, and networking offerings between give up units and computing facts centers. Fog computing is defined as a decentralized infrastructure that locations storage and processing aspects at the side of the cloud, the place records sources such as software customers and sensors exist. The fog computing Intelligence as Artificial Intelligence (AI) is furnished by way of Fog Nodes in cooperation with Clouds. In Fog Nodes several sorts of AI studying can be realized - such as e.g., Machine Learning (ML), Deep Learning (DL). Thanks to the Genius of Fog Nodes, for example, we communicate of Intelligent IoT.

This paper examines how the extent of the network has expanded from the traditional computer Internet to the field of edge computing based on mobile communication technology with the in-depth development of the mobile Internet and the Internet of Things. In particular, the introduction of 5G has enabled massive edge computing nodes to build a high-performance, energy-efficient and low-latency mobile edge computing architecture. Traditional network security technologies and methods are not fully applicable in this environment. The focus of this paper is on security protection for edge networks. Using virtualized networks builds a dynamic heterogeneous redundancy security model (i.e., DHR). It first designs and evaluates the DHR security model, then constructs the required virtualized heterogeneous entity set, and finally constructs a DHR-based active defense scheme. Compared with existing network security solutions, the security protection technology of the edge network studied this time has a better protective effect against the unknown security threats facing the edge network.

With the continues growth of our technology, majority in our sectors are becoming smart and one of its great applications is in agriculture, which we call it as smart farming. The application of sensors, IoT, artificial intelligence, networking in the agricultural setting with the main purpose of increasing crop production and security level. With this advancement in farming, this provides a lot of privileges like remote monitoring, optimization of produce and too many to mention. In light of the thorough systematic analysis performed in this study, it was discovered that Edge-of-things is a potential computing scheme that could boost an artificial intelligence for intelligent remote farm security and intrusion detection pre-alarm system over other computing schemes. Again, the purpose of this study is not to replace existing cloud computing, but rather to highlight the potential of the Edge. The Edge architecture improves end-user experience by improving the time-related response of the system. response time of the system. One of the strengths of this system is to provide time-critical response service to make a decision with almost no delay, making it ideal for a farm security setting. Moreover, this study discussed the comparative analysis of Cloud, Fog and Edge in relation to farm security, the demand for a farm security system and the tools needed to materialize an Edge computing in a farm environment.

The safety of the power system is inherently vital, due to the high risk of the electronic power system. In the wave of digitization in recent years, many power systems have been digitized to a certain extent. Under this circumstance, network security is particularly important, in order to ensure the normal operation of the power system. However, with the development of the Internet, network security issues are becoming more and more serious. Among all kinds of network attacks, the Distributed Denial of Service (DDoS) is a major threat. Once, attackers used huge volumes of traffic in short time to bring down the victim server. Now some attackers just use low volumes of traffic but for a long time to create trouble for attack detection. There are many methods for DDoS detection, but no one can fully detect it because of the huge volumes of traffic. In order to better detect DDoS and make sure the safety of electronic power system, we propose a novel detection method based on neural network. The proposed model and its service are deployed to the edge cloud, which can improve the real-time performance for detection. The experiment results show that our model can detect attacks well and has good real-time performance.

The main intention of edge computing is to improve network performance by storing and computing data at the edge of the network near the end user. However, its rapid development largely ignores security threats in large-scale computing platforms and their capable applications. Therefore, Security and privacy are crucial need for edge computing and edge computing based environment. Security vulnerabilities in edge computing systems lead to security threats affecting edge computing networks. Therefore, there is a basic need for an intrusion detection system (IDS) designed for edge computing to mitigate security attacks. Due to recent attacks, traditional algorithms may not be possibility for edge computing. This article outlines the latest IDS designed for edge computing and focuses on the corresponding methods, functions and mechanisms. This review also provides deep understanding of emerging security attacks in edge computing. This article proves that although the design and implementation of edge computing IDS have been studied previously, the development of efficient, reliable and powerful IDS for edge computing systems is still a crucial task. At the end of the review, the IDS developed will be introduced as a future prospect.

Cloud computing is a unified management and scheduling model of computing resources. To satisfy multiple resource requirements for various application, edge computing has been proposed. One challenge of edge computing is cross-domain data security sharing problem. Ciphertext policy attribute-based encryption (CP-ABE) is an effective way to ensure data security sharing. However, many existing schemes focus on could computing, and do not consider the features of edge computing. In order to address this issue, we propose a cross-domain data security sharing approach for edge computing based on CP-ABE. Besides data user attributes, we also consider access control from edge nodes to user data. Our scheme first calculates public-secret key peer of each edge node based on its attributes, and then uses it to encrypt secret key of data ciphertext to ensure data security. In addition, our scheme can add non-user access control attributes such as time, location, frequency according to the different demands. In this paper we take time as example. Finally, the simulation experiments and analysis exhibit the feasibility and effectiveness of our approach.

While 5G Edge Computing along with IoT technology has transformed the future of healthcare data transmission, it presents security vulnerabilities and risks when transmitting patients' confidential information. Currently, there are very few reliable security solutions available for healthcare data that routes through SDN routers in 5G Edge Computing. These solutions do not provide cryptographic security from IoT sensor devices. In this paper, we studied how 5G edge computing integrated with IoT network helps healthcare data transmission for remote medical treatment, explored security risks associated with unsecured data transmission, and finally proposed a cryptographic end-to-end security solution initiated at IoT sensor devices and routed through SDN routers. Our proposed solution with cryptographic security initiated at IoT sensor goes through SDN control plane and data plane in 5G edge computing and provides an end-to-end secured communication from IoT device to doctor's office. A prototype built with two-layer encrypted communication has been lab tested with promising results. This analysis will help future security implementation for eHealth in 5G and beyond networks.

The increasing data generation rate and the proliferation of deep learning applications have led to the development of machine learning-as-a-service (MLaaS) platforms by major Cloud providers. The existing MLaaS platforms, however, fall short in protecting the clients’ private data. Recent distributed MLaaS architectures such as federated learning have also shown to be vulnerable against a range of privacy attacks. Such vulnerabilities motivated the development of privacy-preserving MLaaS techniques, which often use complex cryptographic prim-itives. Such approaches, however, demand abundant computing resources, which undermine the low-latency nature of evolving applications such as autonomous driving.To address these challenges, we propose SCLERA–an efficient MLaaS framework that utilizes trusted execution environment for secure execution of clients’ workloads. SCLERA features a set of optimization techniques to reduce the computational complexity of the offloaded services and achieve low-latency inference. We assessed SCLERA’s efficacy using image/video analytic use cases such as scene detection. Our results show that SCLERA achieves up to 23× speed-up when compared to the baseline secure model execution.

While cloud-based deep learning benefits for high-accuracy inference, it leads to potential privacy risks when exposing sensitive data to untrusted servers. In this paper, we work on exploring the feasibility of steganography in preserving inference privacy. Specifically, we devise GHOST and GHOST+, two private inference solutions employing steganography to make sensitive images invisible in the inference phase. Motivated by the fact that deep neural networks (DNNs) are inherently vulnerable to adversarial attacks, our main idea is turning this vulnerability into the weapon for data privacy, enabling the DNN to misclassify a stego image into the class of the sensitive image hidden in it. The main difference is that GHOST retrains the DNN into a poisoned network to learn the hidden features of sensitive images, but GHOST+ leverages a generative adversarial network (GAN) to produce adversarial perturbations without altering the DNN. For enhanced privacy and a better computation-communication trade-off, both solutions adopt the edge-cloud collaborative framework. Compared with the previous solutions, this is the first work that successfully integrates steganography and the nature of DNNs to achieve private inference while ensuring high accuracy. Extensive experiments validate that steganography has excellent ability in accuracy-aware privacy protection of deep learning.

This article describes an analysis of the key technologies currently applied to improve the quality, efficiency, safety and sustainability of Smart Grid systems and identifies the tools to optimize them and possible gaps in this area, considering the different energy sources, distributed generation, microgrids and energy consumption and production capacity. The research was conducted with a qualitative methodological approach, where the literature review was carried out with studies published from 2019 to 2022, in five (5) databases following the selection of studies recommended by the PRISMA guide. Of the five hundred and four (504) publications identified, ten (10) studies provided insight into the technological trends that are impacting this scenario, namely: Internet of Things, Big Data, Edge Computing, Artificial Intelligence and Blockchain. It is concluded that to obtain the best performance within Smart Grids, it is necessary to have the maximum synergy between these technologies, since this union will enable the application of advanced smart digital technology solutions to energy generation and distribution operations, thus allowing to conquer a new level of optimization.

Recent trends in the convergence of edge computing and artificial intelligence (AI) have led to a new paradigm of “edge intelligence”, which are more vulnerable to attack such as data and model poisoning and evasion of attacks. This paper proposes a white-box poisoning attack against online regression model for edge intelligence environment, which aim to prepare the protection methods in the future. Firstly, the new method selects data points from original stream with maximum loss by two selection strategies; Secondly, it pollutes these points with gradient ascent strategy. At last, it injects polluted points into original stream being sent to target model to complete the attack process. We extensively evaluate our proposed attack on open dataset, the results of which demonstrate the effectiveness of the novel attack method and the real implications of poisoning attack in a case study electric energy prediction application.

Recently, unmanned aerial vehicle (UAV) swarm has been advocated to provide diverse data-centric services including data relay, content caching and computing task offloading in vehicular networks due to their flexibility and conveniences. Since only offloading computing tasks to edge computing devices (ECDs) can not meet the real-time demand of vehicles in peak traffic flow, this paper proposes to combine edge computing and UAV swarm for cooperative task offloading in vehicular networks. Specifically, we first design a cooperative task offloading framework that vehicles' computing tasks can be executed locally, offloaded to UAV swarm, or offloaded to ECDs. Then, the selection of offloading strategy is formulated as a mixed integer nonlinear programming problem, the object of which is to maximize the utility of the vehicle. To solve the problem, we further decompose the original problem into two subproblems: minimizing the completion time when offloading to UAV swarm and optimizing the computing resources when offloading to ECD. For offloading to UAV swarm, the computing task will be split into multiple subtasks that are offloaded to different UAVs simultaneously for parallel computing. A Q-learning based iterative algorithm is proposed to minimize the computing task's completion time by equalizing the completion time of its subtasks assigned to each UAV. For offloading to ECDs, a gradient descent algorithm is used to optimally allocate computing resources for offloaded tasks. Extensive simulations are lastly conducted to demonstrate that the proposed scheme can significantly improve the utility of vehicles compared with conventional schemes.

Distributed computation and AI processing at the edge has been identified as an efficient solution to deliver real-time IoT services and applications compared to cloud-based paradigms. These solutions are expected to support the delay-sensitive IoT applications, autonomic decision making, and smart service creation at the edge in comparison to traditional IoT solutions. However, existing solutions have limitations concerning distributed and simultaneous resource management for AI computation and data processing at the edge; concurrent and real-time application execution; and platform-independent deployment. Hence, first, we propose a novel three-layer architecture that facilitates the above service requirements. Then we have developed a novel platform and relevant modules with integrated AI processing and edge computer paradigms considering issues related to scalability, heterogeneity, security, and interoperability of IoT services. Further, each component is designed to handle the control signals, data flows, microservice orchestration, and resource composition to match with the IoT application requirements. Finally, the effectiveness of the proposed platform is tested and have been verified.

Edge computing is a prospective notion for expanding the potential of cloud computing. It is vital to maintaining a decent atmosphere free of all forms of security and breaches in order to continue utilizing computer services. The security concerns surrounding the edge computing environment has been impeded as a result of the security issues that surround the area. Many researchers have looked into edge computing security issues, however, not all have thoroughly studied the needs. Security requirements are the goals that specify the capabilities and operations that a process that is carried out by a system in order to eliminate various security flaws. The purpose of this study is to give a complete overview of the many different artificial intelligence technologies that are now being utilized for edge computing security with the intention of aiding research in the future in locating research potential. This article analyzed the most recent research and shed light on the following topics: state-of-the-art techniques used to combat security threats, technological trends used by the method, metrics utilize to assess the techniques' ability, and opportunities of research for future researchers in the area of artificial intelligence for edge computing security.

In this paper, we present the architecture of a Smart Industry inspired platform designed for Agriculture 4.0 applications and, specifically, to optimize an ecosystem of SW and HW components for animal repelling. The platform implementation aims to obtain reliability and energy efficiency in a system aimed to detect, recognize, identify, and repel wildlife by generating specific ultrasound signals. The wireless sensor network is composed of OpenMote hardware devices coordinated on a mesh network based on the 6LoWPAN protocol, and connected to an FPGA-based board. The system, activated when an animal is detected, elaborates the data received from a video camera connected to FPGA-based hardware devices and then activates different ultrasonic jammers belonging to the OpenMotes network devices. This way, in real-time wildlife will be progressively moved away from the field to be preserved by the activation of specific ultrasonic generators. To monitor the daily behavior of the wildlife, the ecosystem is expanded using a time series database running on a Cloud platform.

Mobile Edge Computing may become a prevalent platform to support applications where mobile devices have limited compute, storage, energy and/or data privacy concerns. In this paper, we study the efficient provisioning and management of compute resources in the Edge-to-Cloud continuum for different types of real-time applications with timeliness requirements depending on application-level update rates and communication/compute delays. We begin by introducing a highly stylized network model allowing us to study the salient features of this problem including its sensitivity to compute vs. communication costs, application requirements, and traffic load variability. We then propose an online decentralized service placement algorithm, based on estimating network delays and adapting application update rates, which achieves high service availability. Our results exhibit how placement can be optimized and how a load-balancing strategy can achieve near-optimal service availability in large networks.

Edge computing (EC) is a new developing computing technology where data are collected, and analysed nearer to the edge or sources of the data. Cloud to the edge, intelligent applications and analytics are part of the IoT applications and technology. Edge computing technology aims to bring cloud computing features near to edge devices. For time-sensitive applications in cloud computing, architecture massive volume of data is generated at the edge and stored and analysed in the cloud. Cloud infrastructure is a composition of data centres and large-scale networks, which provides reliable services to users. Traditional cloud computing is inefficient due to delay in response, network delay and congestion as simultaneous transactions to the cloud, which is a centralised system. This paper presents a literature review on cloud-based edge computing technologies for delay-sensitive applications and suggests a conceptual model of edge computing architecture. Further, the paper also presents the implementation of QoS support edge computing paradigm in Python for further research to improve the latency and throughput for time-sensitive applications.

Dew Computing (DC) is a comparatively modern field with a wide range of applications. By examining how technological advances such as fog, edge and Dew computing, and distributed intelligence force us to reconsider traditional Cloud Computing (CC) to serve the Internet of Things. A new dew estimation theory is presented in this article. The revised definition is as follows: DC is a software and hardware cloud-based company. On-premises servers provide autonomy and collaborate with cloud networks. Dew Calculation aims to enhance the capabilities of on-premises and cloud-based applications. These categories can result in the development of new applications. In the world, there has been rapid growth in Information and Communication Technology (ICT), starting with Grid Computing (GC), CC, Fog Computing (FC), and the latest Edge Computing (EC) technology. DC technologies, infrastructure, and applications are described. We’ll go through the newest developments in fog networking, QoE, cloud at the edge, platforms, security, and privacy. The dew-cloud architecture is an option concerning the current client-server architecture, where two servers are located at opposite ends. In the absence of an Internet connection, a dew server helps users browse and track their details. Data are primarily stored as a local copy on the dew server that starts the Internet and is synchronized with the cloud master copy. The local dew pages, a local online version of the current website, can be browsed, read, written, or added to the users. Mapping between different Local Dew sites has been made possible using the dew domain name scheme and dew domain redirection.

Cloud computing is a modern type of service that provides each consumer with a large-scale computing tool. Different cyber-attacks can potentially target cloud computing systems, as most cloud computing systems offer services to so many people who are not known to be trustworthy. Therefore, to protect that Virtual Machine from threats, a cloud computing system must incorporate some security monitoring framework. There is a tradeoff between the security level of the security system and the performance of the system in this scenario. If a strong security is required then a stronger security service using more rules or patterns should be incorporated and then in proportion to the strength of security, it needs much more computing resources. So the amount of resources allocated to customers is decreasing so this research work will introduce a new way of security system in cloud environments to the VM in this research. The main point of Fog computing is to part of the cloud server's work in the ongoing study tells the step-by-step cloud server to change gigantic information measurement because the endeavor apps are relocated to the cloud to keep the framework cost. So the cloud server is devouring and changing huge measures of information step by step so it is rented to keep up the problem and additionally get terrible reactions in a horrible device environment. Cloud computing and Fog computing approaches were combined in this paper to review data movement and safe information about MDHC.

By taking advantages of graphs and potential functions, a security reinforcement method for edge computing terminals is proposed in this paper. A risk graph of the terminal security protection system is constructed, and importance of the security protection and risks of the terminals is evaluated according to the topological potential of the graph nodes, and the weak points of the terminal are located, and the corresponding reinforcement method is proposed. The simulation experiment results show that the proposed method can upgrade and strengthen the key security mechanism of the terminal, improve the performance of the terminal security protection system, and is beneficial to the security management of the edge computing system.

Compared with other virtualization technologies, container technology is widely used in edge computing because of its low cost, high reliability, high flexibility and fast portability. However, the use of container technology can alleviate the pressure of massive data, but also bring complex and diverse security problems. Reliable information security risk assessment method is the key to ensure the smooth application of container technology. According to the risk assessment theory, a security risk assessment method for edge computing containers based on dynamic game theory is proposed. Aiming at the complex container security attack and defense process, the container system's security model is constructed based on dynamic game theory. By combining the attack and defense matrix, the Nash equilibrium solution of the model is calculated, and the dynamic process of the mutual game between security defense and malicious attackers is analyzed. By solving the feedback Nash equilibrium solution of the model, the optimal strategies of the attackers are calculated. Finally, the simulation tool is used to solve the feedback Nash equilibrium solution of the two players in the proposed model, and the experimental environment verifies the usability of the risk assessment method.

As a supplement to the cloud computing model, the edge computing model can use edge servers and edge devices to coordinate information processing on the edge of the network to help Internet of Thing (IoT) data storage, transmission, and computing tasks. In view of the complex and changeable situation of edge computing IoT scenarios, this paper proposes a multi-dimensional trust evaluation factor selection scheme. Improve the traditional trusted modeling method based on direct/indirect trust, introduce multi-dimensional trusted decision attributes and rely on the collaboration of edge servers and edge device nodes to infer and quantify the trusted relationship between nodes, and combine the information entropy theory to smoothly weight the calculation results of multi-dimensional decision attributes. Improving the current situation where the traditional trusted assessment scheme's dynamic adaptability to the environment and the lack of reliability of trusted assessment are relatively lacking. Simulation experiments show that the edge computing IoT multi-dimensional trust evaluation model proposed in this paper has better performance than the trusted model in related literature.

Big data continues to grow in the manufacturing domain due to increasing interconnectivity on the shop floor in the course of the fourth industrial revolution. The optimization of machines based on either real-time or historical machine data provides benefits to both machine producers and operators. In order to be able to make use of these opportunities, it is necessary to access the machine data, which can include sensitive information such as intellectual property. Employing the use case of machine tools, this paper presents a solution enabling industrial data sharing and cloud collaboration while protecting sensitive information. It employs the edge computing paradigm to apply differential privacy to machine data in order to protect sensitive information and simultaneously allow machine producers to perform the necessary calculations and analyses using this data.

The goal of the edge computing framework is to solve the problem of management and control in the access of massive 5G terminals in the power Internet of things. Firstly, this paper analyzes the needs of IOT agent in 5G ubiquitous connection, equipment management and control, intelligent computing and other aspects. In order to meet with these needs, paper develops the functions and processes of the edge computing framework, including unified access of heterogeneous devices, protocol adaptation, edge computing, cloud edge collaboration, security control and so on. Finally, the performance of edge computing framework is verified by the pressure test of 5G wireless ubiquitous connection.