Biblio

Privacy is a fundamental concern that confronts systems dealing with sensitive data. The lack of robust solutions for defining and enforcing privacy measures continues to hinder the general acceptance and adoption of these systems. Edge computing has been recognized as a key enabler for privacy enhanced applications, and has opened new opportunities. In this paper, we propose a novel privacy model based on context-aware edge computing. Our model leverages the context of data to make decisions about how these data need to be processed and managed to achieve privacy. Based on a scenario from the eHealth domain, we show how our generalized model can be used to implement and enact complex domain-specific privacy policies. We illustrate our approach by constructing real world use cases involving a mobile Electronic Health Record that interacts with, and in different environments.

With the increasing popularity and adoption of IoT technology, fog computing has been used as an advancement to cloud computing. Although trust management issues in cloud have been addressed, there are still very few studies in a fog area. Trust is needed for collaborating among fog nodes and trust can further improve the reliability by assisting in selecting the fog nodes to collaborate. To address this issue, we present a provenance based trust mechanism that traces the behavior of the process among fog nodes. Our approach adopts the completion rate and failure rate as the process provenance in trust scores of computing workload, especially obvious measures of trustworthiness. Simulation results demonstrate that the proposed system can effectively be used for collaboration in a fog environment.

Upon the new paradigm of Cellular Internet of Things, through the usage of technologies such as Narrowband IoT (NB-IoT), a massive amount of IoT devices will be able to use the mobile network infrastructure to perform their communications. However, it would be beneficial for these devices to use the same security mechanisms that are present in the cellular network architecture, so that their connections to the application layer could see an increase on security. As a way to approach this, an identity management and provisioning mechanism, as well as an identity federation between an IoT platform and the cellular network is proposed as a way to make an IoT device deemed worthy of using the cellular network and perform its actions.

Resolving distributed attacks benefits from collaboration between networks. We present three approaches for the same multi-domain defensive action that can be applied in such an alliance: 1) Counteract Everywhere, 2) Minimize Countermeasures, and 3) Minimize Propagation. First, we provide a formula to compute efficiency of a defense; then we use this formula to compute the efficiency of the approaches under various circumstances. Finally, we discuss how task execution order and timing influence defense efficiency. Our results show that the Minimize Propagation approach is the most efficient method when defending against the chosen attack.

Future networks require fast information response time, scalable content distribution, security and mobility. In order to enable future Internet many key enabling technologies have been proposed such as Edge computing (EC) and Named Data Networking (NDN). In EC substantial compute and storage resources are placed at the edge of the network, in close proximity to end users. Similarly, NDN provides an alternative to traditional host centric IP architecture which seems a perfect candidate for distributed computation. Although NDN with EC seems a promising approach for enabling future Internet, it can cause various challenges such as expiry time of the Pending Interest Table (PIT) and non-trivial computation of the edge node. In this paper we discuss the expiry time and non-trivial computation in NDN based EC. We argue that if NDN is integrated in EC, then the PIT expiry time will be affected in relation with the processing time on the edge node. Our analysis shows that integrating NDN in EC without considering PIT expiry time may result in the degradation of network performance in terms of Interest Satisfaction Rate.

Two-factor authentication has been widely used due to the vulnerabilities associated with the traditional password-based authentication. One-Time Password (OTP) plays an important role in authentication protocol. However, a variety of security problems have been challenging the security of OTP, and improvements are introduced to solve it. This paper reviews several schemes to implement and modify the OTP, a comparison among the popular OTP algorithms is presented. A smart grid architecture with edge computing is shown. The authentication techniques in the smart grid are analyzed.

With the rapid increase in the use of mobile devices in people's daily lives, mobile data traffic is exploding in recent years. In the edge computing environment where edge servers are deployed around mobile users, caching popular data on edge servers can ensure mobile users' fast access to those data and reduce the data traffic between mobile users and the centralized cloud. Existing studies consider the data cache problem with a focus on the reduction of network delay and the improvement of mobile devices' energy efficiency. In this paper, we attack the data caching problem in the edge computing environment from the service providers' perspective, who would like to maximize their venues of caching their data. This problem is complicated because data caching produces benefits at a cost and there usually is a trade-off in-between. In this paper, we formulate the data caching problem as an integer programming problem, and maximizes the revenue of the service provider while satisfying a constraint for data access latency. Extensive experiments are conducted on a real-world dataset that contains the locations of edge servers and mobile users, and the results reveal that our approach significantly outperform the baseline approaches.

In the network security risk assessment on critical information infrastructure of smart city, to describe attack vectors for predicting possible initial access is a challenging task. In this paper, an attack vector evaluation model based on weakness, path and action is proposed, and the formal representation and quantitative evaluation method are given. This method can support the assessment of attack vectors based on known and unknown weakness through combination of depend conditions. In addition, defense factors are also introduced, an attack vector evaluation model of integrated defense is proposed, and an application example of the model is given. The research work in this paper can provide a reference for the vulnerability assessment of attack vector.

This study has built a simulation of a smart home system by the Alibaba ECS. The architecture of hardware was based on edge computing technology. The whole method would design a clear classifier to find the boundary between regular and mutation codes. It could be applied in the detection of the mutation code of network. The project has used the dataset vector to divide them into positive and negative type, and the final result has shown the RBF-function SVM method perform best in this mission. This research has got a good network security detection in the IoT systems and increased the applications of machine learning.

With the rapid development of Internet of Things applications, the power Internet of Things technologies and applications covering the various production links of the power grid "transmission, transmission, transformation, distribution and use" are becoming more and more popular, and the terminal, network and application security risks brought by them are receiving more and more attention. Combined with the architecture and risk of power Internet of Things, this paper first proposes the overall security protection technology system and strategy for power Internet of Things; then analyzes terminal identity authentication and authority control, edge area autonomy and data transmission protection, and application layer cloud fog security management. And the whole process real-time security monitoring; Finally, through the analysis of security risks and protection, the technical difficulties and directions for the security protection of the Internet of Things are proposed.

Edge and Fog Computing will be increasingly pervasive in the years to come due to the benefits they bring in many specific use-case scenarios over traditional Cloud Computing. Nevertheless, the security concerns Fog and Edge Computing bring in have not been fully considered and addressed so far, especially when considering the underlying technologies (e.g. virtualization) instrumental to reap the benefits of the adoption of the Edge paradigm. In particular, these virtualization technologies (i.e. Containers, Real Time Operating Systems, and Unikernels), are far from being adequately resilient and secure. Aiming at shedding some light on current technology limitations, and providing hints on future research security issues and technology development, in this paper we introduce the main technologies supporting the Edge paradigm, survey existing issues, introduce relevant scenarios, and discusses benefits and caveats of the different existing solutions in the above introduced scenarios. Finally, we provide a discussion on the current security issues in the introduced context, and strive to outline future research directions in both security and technology development in a number of Edge/Fog scenarios.

The Information-Centric Network possessing the content-centric features, is the innovative architecture of the next generation of network. Collaborating with fog computing characterized by its strong edge power, ICN will become the development trend of the future network. The emergence of Information-Centric Multimedia Network (ICMN) can meet the increasing demand for transmission of multimedia streams in the current Internet environment. The data transmission has become more delay-constrained and convenient because of the distributed storage, the separation between the location of information and terminals, and the strong cacheability of each node in ICN. However, at the same time, the security of the multimedia streams in the delivery process still requires further protection against wiretapping, interception or attacking. In this paper, we propose the delay-constrained green security communications for ICMN based on chaotic encryption and fog computing so as to transmit multimedia streams in a more secure and time-saving way. We adapt a chaotic cryptographic method to ICMN, implementing the encryption and decryption of multimedia streams. Meanwhile, the network edge capability to process the encryption and decryption is enhanced. Thanks to the fog computing, the strengthened transmission speed of the multimedia streams can fulfill the need for short latency. The work in the paper is of great significance to improve the green security communications of multimedia streams in ICMN.

Being the revolutionary future networking architecture, information-centric networking (ICN) conducts network distribution based on content, which is ideally suitable for Internet of things (IoT). With the rapid growth of network traffic, compared to the conventional IoT, information-centric Internet of things (IC-IoT) is expected to provide users with the better satisfaction of the network quality of service (QoS). However, due to IC-IoT requirements of low latency, large data volume, marginalization, and intelligent processing, it urgently needs an efficient content distribution system. In this paper, we propose an edge learning based green content distribution scheme for IC-IoT. We implement intelligent path selection based on decision tree and edge calculation. Moreover, we apply distributed coding based content transmission to enhance the speed and recovery capability of content. Meanwhile, we have verified the effectiveness and performance of this scheme based on a large number of simulation experiments. The work of this paper is of great significance to improve the efficiency and flexibility of content distribution in IC-IoT.

Information-centric networking (ICN) is a novel networking architecture with subscription-based naming mechanism and efficient caching, which has abundant semantic features. However, existing defense studies in ICN fails to isolate or block efficiently novel content threats including malicious penetration and semantic obfuscation for the lack of researches considering ICN semantic features. More importantly, to detect potential threats, existing security works in ICN fail to use semantic reasoning to construct security knowledge-based defense mechanism. Thus ICN needs a smart and content-based defense mechanism. Current works are not able to block content threats implicated in semantics. Additionally, based on traditional computing resources, they are incompatible with ICN protocols. In this paper, we propose smart reasoning based content threat defense for semantics knowledge enhanced ICN. A fog computing based defense mechanism with content semantic awareness is designed to build ICN edge defense system. In addition, smart reasoning algorithms is proposed to detect implicit knowledge and semantic relations in packet names and contents with context communication content and knowledge graph. On top of inference knowledge, the mechanism can perceive threats from ICN interests. Simulations demonstrate the validity and efficiency of the proposed mechanism.

Nowadays, the Internet of Things (IoT) is a consolidated reality. Smart homes are equipped with a growing number of IoT devices that capture more and more information about human beings lives. However, manufacturers paid little or no attention to security, so that various challenges are still in place. In this paper, we propose a novel approach to secure IoT systems that combines the concept of Security-by-Contract (S×C) with the Fog computing distributed paradigm. We define the pillars of our approach, namely the notions of IoT device contract, Fog node policy and contract-policy matching, the respective life-cycles, and the resulting S×C workflow. To better understand all the concepts of the S×C framework, and highlight its practical feasibility, we use a running case study based on a context-aware system deployed in a real smart home.

The increasing integration of information and communication technologies has undoubtedly boosted the efficiency of Critical Infrastructures (CI). However, the first wave of IoT devices, together with the management of enormous amount of data generated by modern CIs, has created serious architectural issues. While the emerging Fog and Multi-Access Edge Computing (FMEC) paradigms can provide a viable solution, they also bring inherent security issues, that can cause dire consequences in the context of CIs. In this paper, we analyze the applications of FMEC solutions in the context of CIs, with a specific focus on related security issues and threats for the specific while broad scenarios: a smart airport, a smart port, and a smart offshore oil and gas extraction field. Leveraging these scenarios, a set of general security requirements for FMEC is derived, together with crucial research challenges whose further investigation is cornerstone for a successful adoption of FMEC in CIs.

The paper presents a conceptual framework for security embedded task offloading requirements for IoT-Fog based future communication networks. The focus of the paper is to enumerate the need of embedded security requirements in this IoT-Fog paradigm including the middleware technologies in the overall architecture. Task offloading plays a significant role in the load balancing, energy and data management, security, reducing information processing and propagation latencies. The motivation behind introducing the embedded security is to meet the challenges of future smart networks including two main reasons namely; to improve the data protection and to minimize the internet disturbance and intrusiveness. We further discuss the middleware technologies such as cloudlets, mobile edge computing, micro datacenters, self-healing infrastructures and delay tolerant networks for security provision, optimized energy consumption and to reduce the latency. The paper introduces concepts of system virtualization and parallelism in IoT-Fog based systems and highlight the security features of the system. Some research opportunities and challenges are discussed to improve secure offloading from IoT into fog.

With the rapid development of Internet of things (IOT) and big data, the number of network terminal devices and big data transmission are increasing rapidly. Traditional cloud computing faces a great challenge in dealing with this massive amount of data. Fog computing which extends the computing at the edge of the network can provide computation and data storage. Attribute based-encryption can effectively achieve the fine-grained access control. However, the computational complexity of the encryption and decryption is growing linearly with the increase of the number of attributes. In order to reduce the computational cost and guarantee the confidentiality of data, distributed access control with outsourced computation in fog computing is proposed in this paper. In our proposed scheme, fog device takes most of computational cost in encryption and decryption phase. The computational cost of the receiver and sender can be reduced. Moreover, the private key of the user is generated by multi-authority which can enhance the security of data. The analysis of security and performance shows that our proposed scheme proves to be effective and secure.

Fog computing extends cloud computing technology to the edge of the infrastructure to support dynamic computation for IoT applications. Reduced latency and location awareness in objects' data access is attained by displacing workloads from the central cloud to edge devices. Doing so, it reduces raw data transfers from target objects to the central cloud, thus overcoming communication bottlenecks. This is a key step towards the pervasive uptake of next generation IoT-based services. In this work we study efficient orchestration of applications in fog computing, where a fog application is the cascade of a cloud module and a fog module. The problem results into a mixed integer non linear optimisation. It involves multiple constraints due to computation and communication demands of fog applications, available infrastructure resources and it accounts also the location of target IoT objects. We show that it is possible to reduce the complexity of the original problem with a related placement formulation, which is further solved using a greedy algorithm. This algorithm is the core placement logic of FogAtlas, a fog computing platform based on existing virtualization technologies. Extensive numerical results validate the model and the scalability of the proposed algorithm, showing performance close to the optimal solution with respect to the number of served applications.

Internet of Things (IoT) stack models differ in their architecture, applications and needs. Hence, there are different approaches to apply IoT; for instance, it can be based on traditional data center or based on cloud computing. In fact, Cloud-based IoT is gaining more popularity due to its high scalability and cost effectiveness; hence, it is becoming the norm. However, Cloud is usually located far from the IoT devices and some recent research suggests using Fog-Based IoT by using a nearby light-weight middleware to bridge the gap and to provide the essential support and communication between devices, sensors, receptors and the servers. Therefore, Fog reduces centrality and provides local processing for faster analysis, especially for the time-sensitive applications. Thus, processing is done faster, giving the system flexibility for faster response time. Fog-Based Internet of Things security architecture should be suitable to the environment and provide the necessary measures to improve all security aspects with respect to the available resources and within performance constraints. In this work, we discuss some of these challenges, analyze performance of Fog based IoT and propose a security scheme based on MQTT protocol. Moreover, we present a discussion on security-performance tradeoffs.

Researchers and industry experts are looking at how to improve a shopper's experience and a store's revenue by leveraging and integrating technologies at the edges of the network, such as Internet-of-Things (IoT) devices, cloud-based systems, and mobile applications. The integration of IoT technology can now be used to improve purchasing incentives through the use of electronic coupons. Research has shown that targeted electronic coupons are the most effective and coupons presented to the shopper when they are near the products capture the most shoppers' dollars. Although it is easy to imagine coupons being broadcast to a shopper's mobile device over a low-power wireless channel, such a solution must be able to advertise many products, target many individual shoppers, and at the same time, provide shoppers with their desired level of privacy. To support this type of IoT-enabled shopping experience, we have designed Aggio, an electronic coupon distribution system that enables the distribution of localized, targeted coupons while supporting user privacy and security. Aggio uses cryptographic mechanisms to not only provide security but also to manage shopper groups e.g., bronze, silver, and gold reward programs) and minimize resource usage, including bandwidth and energy. The novel use of cryptographic management of coupons and groups allows Aggio to reduce bandwidth use, as well as reduce the computing and energy resources needed to process incoming coupons. Through the use of local coupon storage on the shopper's mobile device, the shopper does not need to query the cloud and so does not need to expose all of the details of their shopping decisions. Finally, the use of privacy preserving communication between the shopper's mobile device and the CouponHubs that are distributed throughout the retail environment allows the shopper to expose their location to the store without divulging their location to all other shoppers present in the store.

Fog and Edge computing provide a large pool of resources at the edge of the network that may be used for distributed computing. Fog infrastructure heterogeneity also results in complex configuration of distributed applications on computing nodes. Linux containers are a mainstream technique allowing to run packaged applications and micro services. However, running applications on remote hosts owned by third parties is challenging because of untrusted operating systems and hardware maintained by third parties. To meet such challenges, we may leverage trusted execution mechanisms. In this work, we propose a model for distributed computing on Fog infrastructures using Linux containers secured by Intel's Software Guard Extensions (SGX) technology. We implement our model on a Docker and OpenSGX platform. The result is a secure and flexible approach for distributed computing on Fog infrastructures.

Augmented reality (AR) technologies are rapidly being adopted across multiple sectors, but little work has been done to ensure the security of such systems against potentially harmful or distracting visual output produced by malicious or bug-ridden applications. Past research has proposed to incorporate manually specified policies into AR devices to constrain their visual output. However, these policies can be cumbersome to specify and implement, and may not generalize well to complex and unpredictable environmental conditions. We propose a method for generating adaptive policies to secure visual output in AR systems using deep reinforcement learning. This approach utilizes a local fog computing node, which runs training simulations to automatically learn an appropriate policy for filtering potentially malicious or distracting content produced by an application. Through empirical evaluations, we show that these policies are able to intelligently displace AR content to reduce obstruction of real-world objects, while maintaining a favorable user experience.

Recently Vehicular Cloud Computing (VCC) has become an attractive solution that support vehicle's computing and storing service requests. This computing paradigm insures a reduced energy consumption and low traffic congestion. Additionally, VCC has emerged as a promising technology that provides a virtual platform for processing data using vehicles as infrastructures or centralized data servers. However, vehicles are deployed in open environments where they are vulnerable to various types of attacks. Furthermore, traditional cryptographic algorithms failed in insuring security once their keys compromised. In order to insure a secure vehicular platform, we introduce in this paper a new decoy technology DT and user behavior profiling (UBP) as an alternative solution to overcome data security, privacy and trust in vehicular cloud servers using a fog computing architecture. In the case of a malicious behavior, our mechanism shows a high efficiency by delivering decoy files in such a way making the intruder unable to differentiate between the original and decoy file.

The convergence of access networks in the fifth-generation (5G) evolution promises multi-tier networking infrastructures for the successes of various applications realizing the Internet-of-Everything era. However, in this context, the support of a massive number of connected devices also opens great opportunities for attackers to exploit these devices in illegal actions against their victims, especially within the distributed denial-of-services (DDoS) attacks. Nowadays, DDoS prevention still remains an open issue in term of performance improvement although there is a significant number of existing solutions have been proposed in the literature. In this paper, we investigate the advances of multi-access edge computing (MAEC), which is considered as one of the most important emerging technologies in 5G networks, in order to provide an effective DDoS prevention solution (referred to be MAEC-X). The proposed MAEC-X architecture and mechanism are developed as well as proved its effectiveness against DDoS attacks through intensive security analysis.