Blockchain (BC) technology provides a secure distributed transactional database, that can enhance the security and privacy of decentralized systems and applications, e.g. distributed identity, supply chain and Internet of Things (IoT). The most known secure consensus mechanism for permissionless BCs is the Proof-of-Wok (PoW) algorithm. Many argue that the fastest approach to mine new blocks in PoW-based BC networks (although too energy consuming) is Brute-forcing the nonce. In this paper, we demonstrate how a well-trained Machine Learning (ML) model can find more accurate initial nonce values for this problem aiming to decrease the total energy consumption. We attempt to identify linear relationships between inputs and outputs of the classical mining processes, which typically use pure Brute-force techniques to solve the problem. Then, we integrate two ML models, namely SGDRegressor and LinearRegressor with PolynomialFeatures, into a classical mining method to predict the solution. For this, we use more than 780k+ real Bitcoin blocks for training and testing. We mathematically formalize our analysis and propose equations to predict and score the total enhancement, for any ML model deployment, compared to classical mining. We experimentally prove that our method can mine faster than the classical method. Furthermore, we discuss the implications on the node level and the network level, including the allowance for taking over the system by controlling a portion of only 35.5% out of the total computational power of the network. Finally, we apply our method in an integrated IoT-Fog-Blockchain system to enhance the fairness among participating miners.

Thousands of researchers and practitioners around the world neglect foundational understanding of the most trusted cryptocurrency platform, Bitcoin. According to the typical tendency of taking the discussion of Bitcoin's security for granted, it is usually confidently stated that, technically, Bitcoin is one of the most secure platforms for investment. In this paper, we revisit the fundamental calculations and assumptions of this widely adopted platform. We break down the original calculations in order to better understand the underlying assumptions of the Bitcoin proposal. Accordingly, we set open research questions, and we highlight expected scenarios upon the violation of each assumption. Furthermore, we propose a novel formalization of the Bitcoin mining problem using the Birthday Paradox, which we utilize to propose new concepts for benchmarking the security of Bitcoin, including the Critical Difficulty and Critical Difficulty per given portion. Our calculations indicated that it would be profitable to launch Partial Pre-Image attacks on Bitcoin once the mining puzzle difficulty reaches 56 leading zeros. Additionally, we discuss how Quantum Computing can be used to attack Bitcoin, and the implications of Sharding on the security of Bitcoin. The main objective of this work is to highlight, demystify, and justify the confusion between the realistic relative trust versus the common unconditional trust in Bitcoin.

Trusted online credential management solutions are needed for instant and practical verification. Most of the available frameworks targeting this field violate the privacy of end-users or lack sufficient solutions in terms of security and Quality-of-Service (QoS). In this paper, we propose a Privacy-aware Fog-enhanced Blockchain-based online credential solution, namely PriFoB. Our proposed solution adopts a public permissioned Blockchain model with different reliable encryption schemes, standardized Zero-Knowledge-Proofs (ZKPs) and Digital Signatures (DS) within a Fog-Blockchain integrated framework, which is also GDPR compliant. We deploy both the Proof-of-Authority (PoA) and the Signatures-of-Work (SoW) consensus algorithms for efficient and secure handling of Verifiable Credentials (VCs) and global accreditation of VC issuers, respectively. Furthermore, we propose a novel three-dimensional DAG-based model of the Distributed Ledger (3DDL) and provide ready-to-deploy PriFoB implementation. We discuss insights regarding the utilization and the potential of PriFoB, and evaluate it in terms of security, privacy, latency, throughput and power utilization. We analyze its performance in different layers of Fog-enabled cloud architecture with simulation and emulation, and we show that PriFoB outperforms several Blockchain-based solutions utilizing Ethereum, Hyperledger Fabric, Hyperledger Besu and Hyperledger Indy platforms.

A lot of hard work and years of research are still needed for developing successful Blockchain (BC) applications. Although it is not yet standardized, BC technology was proven as to be an enhancement factor for security, decentralization, and reliability, leading to be successfully implemented in cryptocurrency industries. Fog computing (FC) is one of the recently emerged paradigms that needs to be improved to serve Internet of Things (IoT) environments of the future. As hundreds of projects, ideas, and systems were proposed, one can find a great R\&D potential for integrating BC and FC technologies. Examples of organizations contributing to the R\&D of these two technologies, and their integration, include Linux, IBM, Google, Microsoft, and others. To validate an integrated Fog-Blockchain protocol or method implementation, before the deployment phase, a suitable and accurate simulation environment is needed. Such validation should save a great deal of costs and efforts on researchers and companies adopting this integration. Current available simulation environments facilitate Fog simulation, or BC simulation, but not both. In this paper, we introduce a Fog-Blockchain simulator, namely FoBSim, with the main goal is to ease the experimentation and validation of integrated Fog-Blockchain approaches. According to our proposed workflow of simulation, we implement different Consensus Algorithms (CA), different deployment options of the BC in the FC architecture, and different functionalities of the BC in the simulation. Furthermore, technical details and algorithms on the simulated integration are provided. We validate FoBSim by describing the technologies used within FoBSim, highlighting FoBSim novelty compared to the state-of-the-art, discussing the event validity in FoBSim, and providing a clear walk-through validation. Finally, we simulate two case studies, then present and analyze the obtained results.