Blockchain has numerous benefits such as decentralisation, persistency, anonymity and auditability. There is a wide spectrum of blockchain applications ranging from cryptocurrency, financial services, risk management, internet of things (IoT) to public and social services. Although a number of studies focus on using the blockchain technology in various application aspects, there is no comprehensive survey on the blockchain technology in both technological and application perspectives. To fill this gap, we conduct a comprehensive survey on the blockchain technology. In particular, this paper gives the blockchain taxonomy, introduces typical blockchain consensus algorithms, reviews blockchain applications and discusses technical challenges as well as recent advances in tackling the challenges. Moreover, this paper also points out the future directions in the blockchain technology.

Blockchain challenges and opportunities: a survey

A systematic literature review of blockchain-based applications: Current status, classification and open issues

In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloud-based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL.

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Federated Learning in Mobile Edge Networks: A Comprehensive Survey

(1) Whereas the objectives of the Community, as laid down in the Treaty, as amended by the Treaty on European Union, include creating an ever closer union among the peoples of Europe, fostering closer relations between the States belonging to the Community, ensuring economic and social progress by common action to eliminate the barriers which divide Europe, encouraging the constant improvement of the living conditions of its peoples, preserving and strengthening peace and liberty and promoting democracy on the basis of the fundamental rights recognized in the constitution and laws of the Member States and in the European Convention for the Protection of Human Rights and Fundamental Freedoms;

Some Preliminary Comments on the DIRECTIVE 95/46/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data.

In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications. Traditional cloudbased Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL

This paper presents the first generalized reputation system that can be applied to multiple networks that is based on the blockchain. We first discuss current reputation systems, conducting a critical analysis of their current security vulnerabilities, before looking at how new blockchain based technologies are used. We propose an innovative new reputation system that is based on blockchain technologies which aims to solve many unanswered questions in today's current generation reputation systems. We then consider the limitations of such a system, before using simulations and analyses to demonstrate methods of overcoming these limitations. We conclude by suggesting areas for future studies, and summarizing our findings.

/pdf/rep-on-the-block-a-next-generation-reputation-system-based-3cb0r2x8i9.pdf

Rep on the block: A next generation reputation system based on the blockchain

This paper presents a possible solution to a fundamental limitation facing all blockchain-based systems; scalability. We propose a temporal rolling blockchain which solves the problem of its current exponential growth, instead replacing it with a constant fixed-size blockchain. We conduct a thorough analysis of related work and present a formal analysis of the new rolling blockchain, comparing the results to a traditional blockchain model to demonstrate that the deletion of data from the blockchain does not impact on the security of the proposed blockchain model before concluding our work and presenting future work to be conducted.

/pdf/a-temporal-blockchain-a-formal-analysis-18qn4l5p9w.pdf

A Temporal Blockchain: A Formal Analysis

This paper presents the first generalized reputation system that can be applied to multiple networks that is based on the blockchain. We first discuss current reputation systems, conducting a critical analysis of their current security vulnerabilities, before looking at how new blockchain-based technologies are used. We propose an innovative new reputation system that is based on blockchain technologies and which aims to solve many unanswered questions in the current generation of reputation systems, as well as blockchain systems in general. We then consider the limitations of such a system, before using simulations and analyses to demonstrate methods of overcoming these limitations, and in doing so, provide a possible solution to a fundamental issue in blockchain-based networks; scalability. We conclude by suggesting areas for future studies, and summarizing our findings.

/pdf/rep-on-the-roll-a-peer-to-peer-reputation-system-based-on-a-3qmowtmizd.pdf

Rep on the roll:a peer to peer reputation system based on a rolling blockchain

With cryptocurrencies and blockchain-based networks being increasingly used for more and more applications, a fundamental issue is now being noticed; scalability. In this paper, we conduct what we believe the first long-term assessment of the two largest blockchain-based networks; Bitcoin and Ethereum. Using historic data, we model how their growth could be over the next three years and proposed a model, a temporal blockchain, to reduce the network size and increase scalability.

An Analysis into the Scalability of Bitcoin and Ethereum

Countering the proliferation of malware has been for recent years one of the top priorities for governments, businesses, critical infrastructure, and end users. Despite the apparent evolvement of anti-virus (AV) systems, malicious authors have managed to create a sense of insecurity amongst computer users. Security controls do not appear to be sufficiently strong to stop malware proliferating. There seems to be a disconnect between public reports on AV tests and what people are experiencing on the daily basis. In this research, we are testing the efficiency of AV products and their ability to detect malicious files commonly known as malware. We manually generated payloads from five malware frameworks freely available to download and use. We use two modes of tests during our experiments. We manually installed a selection of AV systems in one first instance. We also use an online framework for testing malicious files. The findings in this study show that many antivirus systems were not able to achieve a higher score than 80% detection rate. Certain attack frameworks were much more successful in generating payloads that were not detectable by AV systems. We conclude that AV systems have their roles to play as they are the most common first line of defense, but more work is needed to successfully detect most malware the first day of their release.

Richard Dennis

Papers

Rep on the block: A next generation reputation system based on the blockchain

A Temporal Blockchain: A Formal Analysis

Rep on the roll:a peer to peer reputation system based on a rolling blockchain

An Analysis into the Scalability of Bitcoin and Ethereum

Evaluation of AV systems against modern malware