One of the major concerns regarding currently proposed public blockchain systems relates to the feasible transaction processing rate. It is common for such systems to limit this rate to maintain the required levels of security and decentralisation. State channels are an approach to overcome this limitation, as they aim to decrease the required on-chain transactions for a given application and thus indirectly increase the capacity (in terms of applications) of public blockchain systems. In the present paper, we propose a state channel design that, through the use of RSA accumulators, operates on a compact state structure. This scheme is optimal for applications whose state includes large sets of elements. The novel state channel design is presented by analysing all state channel operations and how they have to be revised. The security of the design is discussed, while a practical use case scenario regarding the use of the design for an on-chain asset (e.g., non-fungible tokens) exchange application is also analysed.

The Internet of Things constitutes the latest paradigm shift in computing. Billions of devices sense the real world and store produced data in the cloud. Existing security models, approaches and solutions are not able to sufficiently protect modern computing infrastructures, consisting of numerous low power devices that are characterized by high diversity in both hardware and software. In this paper a new approach to IoT security, based on a distributed multi-agent system is presented. We use a lightweight agent in each one of multiple IoT installations (e.g. smarthomes), in order to collaboratively detect security events and prevent probable attacks. A simulation is conducted in order to evaluate the performance of the proposed methodology. In particular the methodology is used to minimize the effects of distributed denial of service attacks, conducted by the use of IoT devices botnets, such as the mirai botnet attacks, spotted recently.

The rapid growth of connected devices and the sensitive data they generate poses a significant challenge for manufacturers seeking to comprehensively protect their devices from attack. This paper presents a study and its results from the correlation of the supply current of a smart device to its functional characteristics in order to detect a manufacturing or an operational anomaly. This concept was originated from the fact that most of the available smart devices in the market (connected to the Internet, establishing thus the so-called Internet of Things - IoT), are Application Specific devices thus having limited functionality. Issues on IoT applications, urging for solutions, are security, availability, and reliability. Awareness of the typical operation of a smart device in terms of operation and functionality may prove valuable, since any deviation may warn for an operational or functional anomaly. In this paper, the deviation (either increase or decrease) of the supply current is exploited for this reason. This work aims to affect the intrusion detection process for IoT and proposes for consideration new inputs of interest with a collateral interest of study. The results present 100% of attack detection.

The Internet of Things constitutes the latest paradigm shift in computing. We are soon going to be surrounded by billions of devices that will sense the real world and store produced data in the cloud. These data will probably be used in ways that the users will not be able to control, so their privacy violation risk will increase dramatically. In this paper a decentralized, peer to peer communication protocol for securing communication among devices of the Internet of Things is proposed. The protocol is based on the blockchain technology, which is adapted to satisfy the requirements of the IoT.

Machine learning and especially deep learning are appropriate for solving multiple problems in various domains. Training such models though, demands significant processing power and requires large data-sets. Federated learning is an approach that merely solves these problems, as multiple users constitute a distributed network and each one of them trains a model locally with his data. This network can cumulatively sum up significant processing power to conduct training efficiently, while it is easier to preserve privacy, as data does not leave its owner. Nevertheless, it has been proven that federated learning also faces privacy and integrity issues. In this paper a general enhanced federated learning framework is presented. Users may provide data or the required processing power or participate just in order to train their models. Homomorphic encryption algorithms are employed to enable model training on encrypted data. Blockchain technology is used as smart contracts coordinate the work-flow and the commitments made between all participating nodes, while at the same time, tokens exchanges between nodes provide the required incentives for users to participate in the scheme and to act legitimately. © 2019 by SCITEPRESS - Science and Technology Publications, Lda.

The H2020 European research project Safe-Guarding Home IoT Environments with Personalised Real-time Risk Control (GHOST) aims to develop a cyber-security layer on IoT smart home installations. The proposed system analyses packet-level data flows for building patterns of communications between IoT devices and external entities. To ensure non-repudiation, integrity and authentication of the data captured, they are stored in a Blockchain, a distributed ledger network, as digitally-signed transactions. Since the data can potentially include sensitive user information, it is imperative to promote trust by informing users about the operating principles of the network as well as to request the acceptance of a consent form by them. This paper presents the design and implementation of a Forms of Consent application, a Distributed Application that interacts with a set of Smart Contracts deployed on a private Ethereum network. The application is being developed as part of the GHOST project.

Blockchain is one of the most hyped technologies of recent years. It promises to restructure the way applications are developed and to replace the widely client-server model used today. While the technology is promising the ecosystem of tools and development methodologies are still in their early stages. In this paper a proof of concept blockchain application is developed, regarding the exchange of data between different universities in different countries, with respect to the Erasmus program. A decentralized application has been designed from ground up and is proposed to be installed in multiple nodes located in all participating universities. Ethereum platform has been chosen and the required smart contracts have been implemented. Additionally an easy to use web interface have been developed in order to be usable for end users. We have evaluated both the development process and the efficiency of the resulting implementation.

The European research project GHOST challenges the traditional cyber security solutions for the Internet of Things (IoT) sector by exploiting novel technologies, such as blockchain, to provide resilience and integrity of decision making on the communication exchange in a smart home context. When it comes to novel cyber security solutions for extremely heterogeneous environments like IoT and smart homes, the key focus is typically given to the understanding of network activities and elimination of suspicious traffic. The GHOST project adds an extra dimension to this approach by integrating blockchain technology at its core decision mechanism. On a daily basis, each GHOST installation is encountering malicious behaviour and suspicious IoT communications, where easy information sharing with other installations, as well as decentralised decision making, are mandatory features for the efficient protection of the end-user. GHOST's Smart Contracts (SC) are designed to tackle in an easy, yet productive way, the reporting on suspicious IP addresses which the IoT devices in a smart home are trying to communicate with. Two variations of blacklisting smart contracts are presented in this paper, covering a diverse spectrum of possible attack vectors while closely following the Privacy by Design (PbD) principles. A reputation scoring scheme for malicious IPs reporting is integrated in the SC, uncovering the implementation details on the penalisation of existing entries in case of malicious behaviour of reporting devices.