Communication systems: An introduction to signals and noise in electrical communication

The present increase of attention toward blockchain-based systems is currently reaching a tipping point with the corporate focus shifting from exploring the technology potential to creating Distributed Ledger Technology (DLT)-based systems. In light of a significant number of already existing blockchain applications driven by the Internet of Things (IoT) evolution, the developers are still facing a lack of tools and instruments for appropriate and efficient performance evaluation and behavior observation of different blockchain architectures. This paper aims at providing a systematic review of current blockchain evaluation approaches and at identifying the corresponding utilization challenges and limitations. First, we outline the main metrics related to the blockchain evaluation. Second, we propose the blockchain modeling and analysis classification based on the critical literature review. Third, we extend the review with publicly accessible industrial tools. Next, we analyze the selected results for each of the proposed classes and outline the corresponding limitations. Finally, we identify current challenges of the blockchain analysis from the system evaluation perspective, as well as provide future perspectives.

https://www.mdpi.com/1424-8220/20/12/3358/pdf

Blockchain Evaluation Approaches: State-of-the-Art and Future Perspective.

Challenges of blockchain in new generation energy systems and future outlooks

The first step to realise the true potential of blockchain systems is to explain the associated security risks and vulnerabilities. These risks and vulnerabilities, exploited by the threat agent to affect the valuable assets and services. In this work, we use a security risk management (SRM) domain model and develop a framework to explore two security risks – Sybil and Double-spending – that are observed and considered most concerning security risks within blockchain systems. The framework illustrates the protected assets or assets to secure, the classification of threats that the attacker can trigger using Sybil attack, the identification of threats that cause Double-spending, the vulnerabilities of identified threats, and their countermeasures. We evaluated a newly built framework by exploring Sybil and Double-spending risks in Ethereum-based healthcare applications. We also recognise the various other security and implementation challenges of blockchain that hinder the acceptance of blockchain-enabled solutions. Furthermore, we discuss the permissioned blockchain systems making an appearance in industry-level enterprises and how permissioned blockchain systems control these challenges. We conclude the paper and outline the future work that aims to build an ontology-based blockchain security reference model. The results of this work could help blockchain developers, practitioners, and other associated stakeholders to communicate about Sybil and Double-spending risks, what security countermeasures should be introduced, and what security and implementation challenges are emerging in blockchain systems.

/pdf/exploring-sybil-and-double-spending-risks-in-blockchain-1u51kckt0t.pdf

Exploring Sybil and Double-Spending Risks in Blockchain Systems

Recently, the blockchain technology has attracted widespread attention due to its advantageous features, e.g., decentralization, transparency, traceability, and immutability. To make full use of renewable energy resources, new generation energy systems advocate the deep integration of information technology in real-world energy projects, among which blockchain has become one of the most used technologies. However, with the continuous development of related studies and projects, blockchain has begun to expose more and more limitations. As a result, the application of energy blockchain, i.e., blockchain applied in energy systems, is facing various challenges caused by these limitations. This paper briefly reviews popular application scenarios of energy blockchain, analyzes generic limitations of blockchain and their impacts on energy systems, and looks into possible directions that could deal with these limitations for future blockchain-based energy systems. We argue that blockchain is not a panacea for energy systems because blockchain’s component technologies have their own generic issues, and that the application of energy blockchain should be accompanied with improvement measures that conform to practical requirements of energy systems. As far as we know, this paper is one of the few works that deeply analyze the shortcomings of the blockchain technology and potential countermeasures in the energy field. 

Our aim in this paper is to investigate the profitability of double-spending (DS) attacks that manipulate an a priori mined transaction in a blockchain. It was well understood that a successful DS attack is established when the proportion of computing power an attacker possesses is higher than that of the honest network. What is not yet well understood is how threatening a DS attack with less than 50% computing power used can be. Namely, DS attacks at any proportion can be a threat as long as the chance to make a good profit exists. Profit is obtained when the revenue from making a successful DS attack is greater than the cost of carrying out one. We have developed a novel probability theory for calculating a finitetime attack probability. This can be used to size up attack resources needed to obtain the profit. The results enable us to derive a sufficient and necessary condition on the value of a transaction targeted by a DS attack. Our result is quite surprising: we theoretically show how a DS attack at any proportion of computing power can be made profitable. Given one’s transaction value, the results can also be used to assess the risk of a DS attack. An example of profitable DS attack against BitcoinCash is provided.

Profitable Double-Spending Attacks

In this letter, we propose a multimodal method for improving radio frequency (RF) fingerprinting performance that uses multiple features cultivated from RF signals. Combining multiple features, including a falling transient feature that has not previously been used in RF fingerprinting studies, we aim to demonstrate that the proposed method results in improved accuracy. We show that a sparse representation-based classification (SRC) scheme can be a good platform for combining multiple features. The experimental results on RF signals acquired from eight walkie-talkies show that the RF fingerprinting accuracy of the proposed method improves significantly as the number of features increases.

/pdf/multimodal-sparse-representation-based-classification-scheme-44lxpw7l6a.pdf

Multimodal Sparse Representation-Based Classification Scheme for RF Fingerprinting

Our aim in this paper is to investigate the profitability of double-spending (DS) attacks that manipulate an a priori mined transaction in a blockchain. It was well understood that a successful DS attack is established when the proportion of computing power an attacker possesses is higher than that the honest network does. What is not yet well understood is how threatening a DS attack with less than 50% computing power used can be. Namely, DS attacks at any proportion can be of a threat as long as the chance to making a good profit exists. Profit is obtained when the revenue from making a successful DS attack is greater than the cost of carrying out one. We have developed a novel probability theory for calculating a finite time attack probability. This can be used to size up attack resources needed to obtain the profit. The results enable us to derive a sufficient and necessary condition on the value of a transaction targeted by a DS attack. Our result is quite surprising: we theoretically show that DS attacks at any proportion of computing power can be made profitable. Given one's transaction size, the results can also be used to assess the risk of a DS attack. An example of the attack resources is provided for the BitcoinCash network.

The error-correction code proof-of-work (ECCPoW) algorithm is based on a low-density parity-check (LDPC) code. ECCPoW can impede the advent of mining application-specific integrated circuits (ASICs) with its time-varying puzzle generation capability. Previous research studies on ECCPoW algorithm have presented its theory and implementation on Bitcoin. In this study, we have not only designed ECCPoW for Ethereum, called ETH-ECC, but have also implemented, simulated, and validated it. In the implementation, we have explained how ECCPoW algorithm has been integrated into Ethereum 1.0 as a new consensus algorithm. Furthermore, we have devised and implemented a new method for controlling the difficulty level in ETH-ECC. In the simulation, we have tested the performance of ETH-ECC using a large number of node tests and demonstrated that the ECCPoW Ethereum works well with automatic difficulty-level change capability in real-world experimental settings. In addition, we discuss how stable the block generation time (BGT) of ETH-ECC is. Specifically, one key issue we intend to investigate is the finiteness of the mean of ETH-ECC BGT. Owing to a time-varying cryptographic puzzle generation system in ECCPoW algorithm, BGT in the algorithm may lead to a long-tailed distribution. Thus, simulation tests have been performed to determine whether BGT distribution is not heavy-tailed and has a finite mean. If the distribution is heavy-tailed, stable transaction confirmation cannot be guaranteed. In the validation, we have presented statistical analysis results based on the two-sample Anderson–Darling test and discussed how the BGT distribution follows an exponential distribution which has a finite mean. Our implementation is available for download at https://github.com/cryptoecc/ETH-ECC .

/pdf/error-correction-code-proof-of-work-on-ethereum-27bstk2d4m.pdf

Error-Correction Code Proof-of-Work on Ethereum

A modulated wideband converter (MWC) has been introduced as a sub-Nyquist sampler that exploits a set of fast alternating pseudo random (PR) signals. Through parallel analog channels, an MWC compresses a multiband spectrum by mixing it with PR signals in the time domain, and acquires its sub-Nyquist samples. Previously, the ratio of compression was fully dependent on the specifications of PR signals. That is, to further reduce the sampling rate without information loss, faster and longer-period PR signals were needed. However, the implementation of such PR signal generators results in high power consumption and large fabrication area. In this paper, we propose a novel aliased modulated wideband converter (AMWC), which can further reduce the sampling rate of MWC with fixed PR signals. The main idea is to induce intentional signal aliasing at the analog-to-digital converter (ADC). In addition to the first spectral compression by the signal mixer, the intentional aliasing compresses the mixed spectrum once again. We demonstrate that AMWC reduces the number of analog channels and the rate of ADC for lossless sub-Nyquist sampling without needing to upgrade the speed or the period of PR signals. Conversely, for a given fixed number of analog channels and sampling rate, AMWC improves the performance of signal reconstruction.

Jehyuk Jang

Papers

Profitable Double-Spending Attacks

Multimodal Sparse Representation-Based Classification Scheme for RF Fingerprinting

Profitable Double-Spending Attacks

Error-Correction Code Proof-of-Work on Ethereum

Intentional Aliasing Method to Improve Sub-Nyquist Sampling System