Biblio

The Extended Triple Diffie-Hellman (X3DH) protocol has been used for years as the basis of secure communication establishment among parties (i.e, humans and devices) over the Internet. However, such a protocol has several limits. It is typically based on a single trust third-party server that represents a single point of failure (SPoF) being consequently exposed to well- known Distributed Denial of Service (DDOS) attacks. In order to address such a limit, several solutions have been proposed so far that are often cost expensive and difficult to be maintained. The objective of this paper is to propose a BlockChain-Based X3DH (BCB-X3DH) protocol that allows eliminating such a SPoF, also simplifying its maintenance. Specifically, it combines the well- known X3DH security mechanisms with the intrinsic features of data non-repudiation and immutability that are typical of Smart Contracts. Furthermore, different implementation approaches are discussed to suits both human-to-human and device-to-device scenarios. Experiments compared the performance of both X3DH and BCB-X3DH.

As the unmanned aerial vehicle (UAV) can play a vital role to collect information remotely in a military battlefield, researchers have shown great interest to reveal the domain of internet of battlefield Things (IoBT). In a rescue mission on a battlefield, UAV can collect data from different regions to identify the casualty of a soldier. One of the major challenges in IoBT is to identify the soldier in a complex environment. Image processing algorithm can be helpful if proper methodology can be applied to identify the victims. However, due to the limited hardware resources of a UAV, processing task can be handover to the nearby edge computing server for offloading the task as every second is very crucial in a battlefield. Furthermore, to avoid any third-party interaction in the network and to store the data securely, blockchain can help to create a trusted network as it forms a distributed ledger among the participants. This paper proposes a UAV assisted casualty detection scheme based on image processing algorithm where data is protected using blockchain technology. Result analysis has been conducted to identify the victims on the battlefield successfully using image processing algorithm and network issues like throughput and delay has been analyzed in details using public-key cryptography.

In this paper, our objective is to focus on the recent trend of military fields where they brought Internet of Things (IoT) to have better impact on the battlefield by improving the effectiveness and this is called Internet of Battlefield Things(IoBT). Due to the requirements of high computing capability and minimum response time with minimum fault tolerance this paper proposed a decentralized IoBT architecture. The proposed method can increase the reliability in the battlefield environment by searching the reliable nodes among all the edge nodes in the environment, and by adding the fault tolerance in the edge nodes will increase the effectiveness of overall battlefield scenario. This suggested fault tolerance approach is worth for decentralized mode to handle the issue of latency requirements and maintaining the task reliability of the battlefield. Our experimental results ensure the effectiveness of the proposed approach as well as enjoy the requirements of latency-aware military field while ensuring the overall reliability of the network.

Internet of Things (IoT) has been growing exponentially in the commercial market in recent years. It is also a fact that people hold one or more computing devices at home. Many of them have been developed to operate through internet connectivity with cloud computing technologies that result in the demand for fast, robust, and secure services. In most cases, the lack of these services makes difficult the transfer of data to fulfill the devices' purposes. Under these conditions, an intermediate layer or middleware is needed to process, filter, and send data through a more efficient alternative. This paper presents the adaptive solution of a middleware architecture as an intermediate layer between smart devices and cloud computing to enhance the management of the heterogeneity of data provining from IoT devices. The proposed middleware provides easy configuration, adaptability, and bearability for different environments. Finally, this solution has been implemented in the healthcare domain, in which IoT solutions are deployed into Ambient Assisted Living (AAL) environments.

Internet of things (IoT) mainly depends on clouds to process and store their data. Clouds cannot handle the volume and velocity of data generated by IoT. IoT is delay-sensitive and resources limited. Fog computing proposed endorsing the internet of things (IoT) demands. Fog computing extends the cloud computing service to the edge of the network. Fog utilization reduces response time and network overhead while maintaining security aspects. isolation and operating system (OS) dependency achieved by using virtualization. Blockchain proposed to solve the security and privacy of fog computing. Blockchain is a decentralized, immutable ledger. fog computing with blockchain proposed as an IoT infrastructure. Fog computing adopted with lightweight blockchain in this proposed work. This adaptation endorses the IoT demands for low response time with limited resources. This paper explores system applicability. Varies from other papers that focus on one factor such as privacy or security-applicability of the proposed model achieved by concentration different IoT needs and limits. Response time and ram usage with 1000 transactions did not encroach 100s and 300MiB in the proposed model.

The recent advancements in ubiquitous sensing powered by Wireless Computing Technologies (WCT) and Cloud Computing Services (CCS) have introduced a new thinking ability amongst researchers and healthcare professionals for building secure and connected healthcare systems. The integration of Internet of Things (IoT) in healthcare services further brings in several challenges with it, mainly including encrypted communication through vulnerable wireless medium, authentication and access control algorithms and ownership transfer schemes (important patient information). Major concern of such giant connected systems lies in creating the data handling strategies which is collected from the billions of heterogeneous devices distributed across the hospital network. Besides, the resource constrained nature of IoT would make these goals difficult to achieve. Motivated by aforementioned deliberations, this paper introduces a novel approach in designing a security framework for edge-computing based connected healthcare systems. An efficient, multi-factor access control and ownership transfer mechanism for edge-computing based futuristic healthcare applications is the core of proposed framework. Data scalability is achieved by employing distributed approach for clustering techniques that analyze and aggregate voluminous data acquired from heterogeneous devices individually before it transits the to the cloud. Moreover, data/device ownership transfer scheme is considered to be the first time in its kind. During ownership transfer phase, medical server facilitates user to transfer the patient information/ device ownership rights to the other registered users. In order to avoid the existing mistakes, we propose a formal and informal security analysis, that ensures the resistance towards most common IoT attacks such as insider attack, denial of distributed service (DDoS) attack and traceability attacks.

The concept behind the Internet of Things (IoT) is taking everything and connecting to the internet so that all devices would be able to send and receive data online. Internet of Vehicles (IoV) is a key component of smart city which is an outcome of IoT. Nowadays the concept of IoT has plaid an important role in our daily life in different sectors like healthcare, agriculture, smart home, wearable, green computing, smart city applications, etc. The emerging IoV is facing a lack of rigor in data processing, limitation of anonymity, privacy, scalability, security challenges. Due to vulnerability IoV devices must face malicious hackers. Nowadays with the help of blockchain (BC) technology energy system become more intelligent, eco-friendly, transparent, energy efficient. This paper highlights two major challenges i.e. scalability and security issues. The flavor of edge computing (EC) considered here to deal with the scalability issue. A BC is a public, shared database that records transactions between two parties that confirms owners through cryptography. After a transaction is validated and cryptographically verified generates “block” on the BC and transactions are ordered chronologically and cannot be altered. Implementing BC and smart contracts technologies will bring security features for IoV. It plays a role to implement the rules and policies to govern the IoV information and transactions and keep them into the BC to secure the data and for future uses.

Fog Computing is one of the edge computing paradigms that envisages being the proximate processing and storage infrastructure for a multitude of IoT appliances. With its dynamic deployability as a medium level cloud service, fog nodes are enabling heterogeneous service provisioning infrastructure that features scalability, interoperability, and adaptability. Out of the various 5G based services possible with the fog computing platforms, security services are imperative but minimally investigated direct live. Thus, in this research, we are focused on launching security services in a fog node with an architecture capable of provisioning on-demand service requests. As the fog nodes are constrained on resources, our intention is to integrate light-weight virtualization technology such as Docker for forming the service provisioning infrastructure. We managed to launch multiple security instances configured to be Intrusion Detection and Prevention Systems (IDPSs) on the fog infrastructure emulated via a Raspberry Pi-4 device. This environment was tested with multiple network flows to validate its feasibility. In our proposed architecture, orchestration strategies performed by the security orchestrator were stated as guidelines for achieving pragmatic, dynamic orchestration with fog in IoT deployments. The results of this research guarantee the possibility of developing an ambient security service model that facilitates IoT devices with enhanced security.

Fog computing is a new distributed computing paradigm that extends the cloud to the network edge. Fog computing aims at improving quality of service, data access, networking, computation and storage. However, the security and privacy issues persist, even if many cloud solutions were proposed. Indeed, Fog computing introduces new challenges in terms of security and privacy, due to its specific features such as mobility, geo-distribution and heterogeneity etc. Blockchain is an emergent concept bringing efficiency in many fields. In this paper, we propose a new access control scheme based on blockchain technology for the fog computing with fault tolerance in the context of the Internet of Things. Blockchain is used to provide secure management authentication and access process to IoT devices. Each network entity authenticates in the blockchain via the wallet, which allows a secure communication in decentralized environment, hence it achieves the security objectives. In addition, we propose to establish a secure connection between the users and the IoT devices, if their attributes satisfy the policy stored in the blockchain by smart contract. We also address the blockchain transparency problem by the encryption of the users attributes both in the policy and in the request. An authorization token is generated if the encrypted attributes are identical. Moreover, our proposition offers higher scalability, availability and fault tolerance in Fog nodes due to the implementation of load balancing through the Min-Min algorithm.

Fog Computing provides users with the cloud facilities at the network edge. It may be assumed to be a virtual platform with adequate storage., computation and processing facilities for latency-sensitive applications. The basic difference lies with the fact that this platform is decentralized in nature. In addition., the fog systems or devices process data locally., are conveyable and are capable of being installed on heterogenous hardware. This versatility in its behavior and it being at the network edge turns the attention towards the security of the users sensitive data (in transition or at rest). In this paper., the authors have emphasized on the security of the fog level in typical Fog- IoT architecture. Various security factors (along with their subfactors) persisting at fog level are identified and discussed in detail. The authors have presented a hierarchy of fog computing security factors that is expected to help in considering security in a systematic and efficient manner. Further., the authors have also ranked the same through Analytical Hierarchy Process (AHP) and compared the results with Fuzzy-AHP (F-AHP). The results are found to be highly correlated.

The most recent and important developments in the field of computer networks are cloud and fog computing. In this study, modern cloud computer networks comprising computers, internet of things (IoT), fog servers, and cloud servers for data transmission, is investigated. A cloud computer networks can be modeled as a network with nodes and arcs, in which each arc represents a transmission line, and each node represents an IoT device, a fog server, or a cloud server. Each transmission line has several possible capacities and is regarded as a multistate. The network is termed a multi-state cloud computer network (MCCN). this study firstly constructs the mathematic model to elucidate the flow relationship among the IoT devices, edge servers, and cloud servers and subsequently develop an algorithm to evaluate the performance of the MCCN by calculating network reliability which is defined as the probability of the data being successfully processed by the MCCN.

The advent of Internet and recent technological developments have paved the way for IoT devices in different sectors. The demand for real-time response led to the development of fog computing which is now a popular computing technique. It provides processing, computing and storage at the network edge for latency-sensitive applications such as banking transactions, healthcare etc. This has further led to the pool of user's sensitive data across the web that needs to be secured. In order to find an efficient security solution, it is mandatory to prioritize amongst different fog-level security factors. The authors have therefore, adopted a fuzzy-based Analytical Hierarchy Approach (AHP) for ranking the security attributes in fog-driven IoT environment. The results have also been compared to the ones obtained from classical-AHP and are found to be correlated.

Budding technologies like IoT requires minimum latency for performing real-time applications. The IoT devices collect a huge amount of big data and stores in the cloud environment, because of its on-demand services and scalability. But processing the needed information of the IoT devices from the cloud computing environment is found to be time-sensitive one. To eradicate this issue fog computing environment was created which acts an intermediate between the IoT devices and cloud computing environment. The fog computing performs intermediate computation and storage which is needed by IoT devices and it eliminates the drawbacks of latency and bandwidth limitation faced by directly using cloud computing for storage and accessing. The fog computing even though more advantageous it is more exposed to security issues by its architecture. This paper concentrates more on the security issues met by fog computing and the present methods used by the researchers to secure fog with their pros and cons.

Fog computing or edge computing or fogging extends cloud computing to the edge of the network. It operates on the computing, storage and networking services between user-end devices and cloud computing data centres. However, in the process of caring out these operations, fog computing is faced with several security issues. These issues may be inherited from cloud computing systems or may arise due to fog computing systems alone. Some of the major gaps in providing a secure platform for the fog computing process arise from interim operational steps like authentication or identification, which often expands to large scale performance issues in fog computing. Thus, these issues and their implications on fog computing databases, and the possible available solutions are researched and provided for a better scope of future use and growth of fog computing systems by bridging the gaps of security issues in it.

In recent years, the computation security of edge computing has been raised as a major concern since the edge devices are often distributed on the edge of the network, less trustworthy than cloud servers and have limited storage/ computation/ communication resources. Recently, coded computing has been proposed to protect the confidentiality of computing data under edge device's independent attack and minimize the total cost (resource consumption) of edge system. In this paper, for the cooperative attack, we design an efficient scheme to ensure the information-theory security (ITS) of user's data and further reduce the total cost of edge system. Specifically, we take matrix multiplication as an example, which is an important module appeared in many application operations. Moreover, we theoretically analyze the necessary and sufficient conditions for the existence of feasible scheme, prove the security and decodeability of the proposed scheme. We also prove the effectiveness of the proposed scheme through considerable simulation experiments. Compared with the existing schemes, the proposed scheme further reduces the total cost of edge system. The experiments also show a trade-off between storage and communication.

IoT devices provide a rich data source that is not available in the past, which is valuable for a wide range of intelligence applications, especially deep neural network (DNN) applications that are data-thirsty. An established DNN model provides useful analysis results that can improve the operation of IoT systems in turn. The progress in distributed/federated DNN training further unleashes the potential of integration of IoT and intelligence applications. When a large number of IoT devices are deployed in different physical locations, distributed training allows training modules to be deployed to multiple edge data centers that are close to the IoT devices to reduce the latency and movement of large amounts of data. In practice, these IoT devices and edge data centers are usually owned and managed by different parties, who do not fully trust each other or have conflicting interests. It is hard to coordinate them to provide end-to-end integrity protection of the DNN construction and application with classical security enhancement tools. For example, one party may share an incomplete data set with others, or contribute a modified sub DNN model to manipulate the aggregated model and affect the decision-making process. To mitigate this risk, we propose a novel blockchain based end-to-end integrity protection scheme for DNN applications integrated with an IoT system in the edge computing environment. The protection system leverages a set of cryptography primitives to build a blockchain adapted for edge computing that is scalable to handle a large number of IoT devices. The customized blockchain is integrated with a distributed/federated DNN to offer integrity and authenticity protection services.

Regardless of the setting, edge computing has drawn much attention from both the academic and industrial communities. For edge computing, content delivery networks are both a concrete and production deployable use case. While viable at the WAN or telco edge scale, it is unclear if this extends to others, such as in home WiFi routers, as has been assumed by some. In this work-in-progress, we present an initial study on the viability of using smart edge WiFi routers as a caching location. We describe the simulator we created to test this, as well as the analysis of the results obtained. We use 1 day of e-commerce web log traffic from a public data set, as well as a sampled subset of our own site - part of an ecosystem of over 111 million users. We show that in the best case scenario, smart edge routers are inappropriate for e-commerce web caching.

At present, the on-site safety problems of substations and critical power equipment are mainly through inspection methods. Still, manual inspection is difficult, time-consuming, and uninterrupted inspection is not possible. The current safety management is mainly guaranteed by rules and regulations and standardized operating procedures. In the on-site environment, it is very dependent on manual execution and confirmation, and the requirements for safety supervision and operating personnel are relatively high. However, the reliability, the continuity of control and patrol cannot be fully guaranteed, and it is easy to cause security vulnerabilities and cause security accidents due to personnel slackness. In response to this shortcoming, this paper uses edge computing and image processing techniques to discover security risks in time and designs a deep convolution attention mechanism network to perform image processing. Then the network is cropped and compressed so that it can be processed at the edge, and the results are aggregated to the cloud for unified management. A comprehensive security assessment module is designed in the cloud to conduct an overall risk assessment of the results reported by all edges, and give an alarm prompt. The experimental results in the real environment show the effectiveness of this method.

Information-Centric Networking (ICN) has attracted much attention as a promising future network design, which presents a paradigm shift from host-centric to content-centric. However, in edge computing scenarios, there is still no specific ICN forwarding mechanism to improve transmission performance. In this paper, we propose an edge-oriented forwarding mechanism (EFM) for edge computing scenarios. The rationale is to enable edge nodes smarter, such as acting as agents for both consumers and providers to improve content retrieval and distribution. On the one hand, EFM can assist consumers: the edge router can be used either as a fast content repository to satisfy consumers’ requests or as a smart delegate of consumers to request content from upstream nodes. On the other hand, EFM can assist providers: EFM leverages the optimized in-network recovery/retransmission to detect packet loss or even accelerate the content distribution. The goal of our research is to improve the performance of edge networks. Simulation results based on ndnSIM indicate that EFM can enable efficient content retrieval and distribution, friendly to both consumers and providers.

Humans are a key part of software development, including customers, designers, coders, testers and end users. In this keynote talk I explain why incorporating human-centric issues into software engineering for next-generation applications is critical. I use several examples from our recent and current work on handling human-centric issues when engineering various `smart living' cloud- and edge-based software systems. This includes using human-centric, domain-specific visual models for non-technical experts to specify and generate data analysis applications; personality impact on aspects of software activities; incorporating end user emotions into software requirements engineering for smart homes; incorporating human usage patterns into emerging edge computing applications; visualising smart city-related data; reporting diverse software usability defects; and human-centric security and privacy requirements for smart living systems. I assess the usefulness of these approaches, highlight some outstanding research challenges, and briefly discuss our current work on new human-centric approaches to software engineering for smart living applications.

As one of the most expensive computations in public-key cryptosystems, modular exponentiation is typically out-sourced to the cloud servers. Traditional cloud-based outsourcing algorithms depend on multiple untrusted servers to guarantee the security, which may lead to vulnerability to the collusion attack. Although recent single-server multiple-requests outsourcing algorithms are more secure, they have to perform multiple requests to the single untrusted server to guarantee the security and checkability of the data, which will incur unacceptable latency and local computational costs. In comparison, the edge computing paradigm enhances security since it has multiple computational nodes, including some highly secure local computational nodes. In this paper, we propose the secure outsourcing algorithm of modular exponentiation for the edge computing paradigm. To address the dilemma that the computational resources of different nodes vary significantly, we design two lightweight algorithms to adaptively separate the modular exponentiation to the nodes based on the computational resources. To guarantee the outsourcing checkability, we propose a protocol verify the result returned from each node. We formally prove the security and checkability of our algorithm and validate the efficiency of our algorithm based on experiments and case studies.

The current trend of IoT user is toward the use of services and data externally due to voluminous processing, which demands resourceful machines. Instead of relying on the cloud of poor connectivity or a limited bandwidth, the IoT user prefers to use a cloudlet-based fog computing. However, the choice of cloudlet is solely dependent on its trust and reliability. In practice, even though a cloudlet possesses a required trusted platform module (TPM), we argue that the presence of a TPM is not enough to make the cloudlet trustworthy as the TPM supports only the primitive security of the bootstrap. Besides uncertainty in security, other uncertain conditions of the network (e.g. network bandwidth, latency and expectation time to complete a service request for cloud-based services) may also prevail for the cloudlets. Therefore, in order to evaluate the trust value of multiple cloudlets under uncertainty, this paper broadly proposes the empirical process for evaluation of trust. This will be followed by a measure of trust-based reputation of cloudlets through computational intelligence such as fuzzy logic and ant colony optimization (ACO). In the process, fuzzy logic-based inference and membership evaluation of trust are presented. In addition, ACO and its pheromone communication across different colonies are being modeled with multiple cloudlets. Finally, a measure of affinity or popular trust and reputation of the cloudlets is also proposed. Together with the context of application under multiple cloudlets, the computationally intelligent approaches have been investigated in terms of performance. Hence the contribution is subjected towards building a trusted cloudlet-based fog platform.

Multi-authority attribute-based encryption (MA-ABE) is a promising technique to achieve fine-grained access control over encrypted data in cross domain applications. However, the dynamic change of users' access privilege brings security problems, and the heavy encryption computational cost is issue for resource-constrained users in IoT. Moreover, the invalid or illegal ciphertext will waste system resources. We propose a large universe MA-CP-ABE scheme with revocation and online/offline encryption. In our scheme, an efficient revocation mechanism is designed to change users' access privilege timely. Most of the encryption operations have been executed in the user's initialization phase by adding reusable ciphertext pool besides splitting the encryption algorithm to online encryption and offline encryption. Moreover, the scheme supports ciphertext verification and only valid ciphertext can be stored and transmitted. The proposed scheme is proven statically secure under the q-DPBDHE2 assumption. The performance analysis results indicate that the proposed scheme is efficient and suitable for resource constrained users in edge computing for IoT.

Nowadays, Fog Computing gets more attention due to its characteristics. Fog computing provides more advantages in related to apply with the latest technology. On the other hand, there is an issue about the data security over processing of data. Fog Computing encounters many security challenges like false data injection, violating privacy in edge devices and integrity of data, etc. An encryption scheme called Homomorphic Encryption (HME) technique is used to protect the data from the various security threats. This homomorphic encryption scheme allows doing manipulation over the encrypted data without decrypting it. This scheme can be implemented in many systems with various crypto-algorithms. This homomorphic encryption technique is mainly used to retain the privacy and to process the stored encrypted data on a remote server. This paper addresses the terminologies of Fog Computing, work flow and properties of the homomorphic encryption algorithm, followed by exploring the application of homomorphic encryption in various public key cryptosystems such as RSA and Pailier. It focuses on various homomorphic encryption schemes implemented by various researchers such as Brakerski-Gentry-Vaikuntanathan model, Improved Homomorphic Cryptosystem, Upgraded ElGamal based Algebric homomorphic encryption scheme, In-Direct rapid homomorphic encryption scheme which provides integrity of data.

Sybil attacks are one of the most prominent security problems of trust mechanisms in a distributed network with a large number of highly dynamic and heterogeneous devices, which expose serious threat to edge computing based distributed systems. Graphbased Sybil detection approaches extract social structures from target distributed systems, refine the graph via preprocessing methods and capture Sybil nodes based on the specific properties of the refined graph structure. Graph preprocessing is a critical component in such Sybil detection methods, and intuitively, the processing methods will affect the detection performance. Thoroughly understanding the dependency on the graph-processing methods is very important to develop and deploy Sybil detection approaches. In this paper, we design experiments and conduct systematic analysis on graph-based Sybil detection with respect to different graph preprocessing methods on selected network environments. The experiment results disclose the sensitivity caused by different graph transformations on accuracy and robustness of Sybil detection methods.