The pool-hopping attack casts down the expected profits of both the mining pool and honest miners in Blockchain. The mainstream countermeasures, namely PPS (pay-per-share) and PPLNS (pay-per-last-N-share), can hedge pool hopping, but pose a risk to the pool as well as the cost to miners. In this study, we apply the zero-determinant (ZD) theory to design a novel pooled mining which offers an incentive mechanism for motivating non-memorial and memorial evolutionary miners not to switch in pools strategically. In short, our hopping-proof pooled mining has three unique features: 1) fee-free. No fee is charged if the miner does not hop. 2) wide applicability. It can be employed in both prepaid and postpaid mechanisms. 3) fairness. Even the pool can dominate the game with any miner, he has to cooperate when the miner does not hop among pools. The fairness of our scheme makes it have long-term sustainability. To the best of our knowledge, we are the first to propose a hopping-proof pooled mining with the above three natures simultaneously. Both theoretical and experimental analyses demonstrate the effectiveness of our scheme.
Abstract When bitcoin was released by the mysterious Satoshi Nakamoto in 2008, few could have predicted that it would attract as much attention as it has today. It has spawned a veritable host of other cryptocurrencies, including ether on the upstart Ethereum network, which boasts smart contract functionality. The underlying blockchain technology has also attracted attention, with some within the blockchain community suggesting that it can solve such diverse problems as secured digital voting to tracking food provenance. In the legal context, blockchains have been envisaged as capable of revolutionising registries for assets ranging from land to intellectual property, modernising clearing and settlement, and even fundamentally transforming the contracting process. This article critically evaluates the popular claims surrounding the potential of blockchain technologies to disrupt the legal system by separating hype from fact.
Knowledge is formalized information that is cited or used in logical inference. The growth of knowledge sharing in the field of education helps people understand and grow their potential. Individuals are willing to share knowledge when they are certain to benefit from reciprocity or build a reputation. Individuals spend enormous amounts of time, resources and scarifications to gain knowledge. Individuals are currently sharing their expertise through forums, vlogs, videos, and trainings. Nevertheless, there is a variety of untrusted and unvalidated knowledge available for a subject from the recipient's point of view. To grasp what they need, it requires multiple resources. There are no direct benefits for people who make, comment or vote for these resources. In this research, the researcher implemented a distributed knowledge sharing system centered on Blockchain to allow multiple individuals to contribute their knowledge by building a resource that is moderated, verified, and community structured. Each creator, commenter and voter of knowledge will receive rewards, and the knowledge invested will gain it forever. The researchers used cryptography and knowledge economy in addition to stellar blockchain to make this process secure and more trustable. In this paper, the proposed system is realistic, efficient and has an ever-earning model.
Jaehong Ahn, Mingyu Park, Hyungsik Shin, Jeongyeup Paek
E-commerce has become a crucial part of our life allowing us to buy products, request services, and transfer money easily with a press of a button. As such, establishing immutable trust and reputation of entities that are resilient to manipulation by the malicious are critical in today’s online systems. In this work, we propose a model for calculating trust and reputation using the values stored on blockchain ledger. The model is applied to blockchain-based online payment systems which have a characteristic of immutability by preventing data manipulation. The model normalizes user evaluations based on each user’s personal evaluation criteria that changes over time. In addition, the model derives reputation of, and trust between, users by applying psychological factors. We evaluate our model using not only simulated transaction data but also on real Bitcoin transaction-based dataset to show that our model is able to derive stable values from immutable transactions on blockchain-based online payment systems. Our model has been built into a live commercial blockchain service platform, and new application developments are underway.
The proliferation of unreliable and biased information is a significant\nproblem on the Internet. To assess the credibility of the information retrieved\nfrom news websites and other sources, users often resort to social platforms\nlooking for confirmation with trustworthy parties. However, users may be faced\nwith considerable obstacles posed by the platform provider, who can prevent\naccess to certain content. This paper presents DClaims, a system that provides\na censorship-resistant distributed service for the exchange of information over\nthe Internet using web annotations. DClaims' fully decentralized architecture\nrelies on Inter-Planetary File System (IPFS) and Ethereum blockchain, both of\nwhich offer desirable censorship resistant properties. DClaims is implemented\nas a web annotations browser extension which allows for the classification of\nnews articles, on news websites. From our evaluation of the system, we conclude\nthat a large scale implementation of the system is practical and economically\nviable.\n
There exist many voting solutions, which have different benefits and issues. The most significant difficulties faced by such systems are lack of transparency and auditability. Recently developed blockchain technology may be a solution to these issues. The paper describes the use of intelligent agents and multi-agent system concept for Auditable Blockchain Voting System (ABVS), which integrates e-voting process with blockchain technology into one supervised non-remote internet voting system which is end-to-end verifiable.
The Domain Name System (DNS) is one of the most fundamental infrastructures of the Internet. However, due to its design philosophy and implementation architecture, the current DNS still suffers from the centralization problem and the data authenticity problem. In this paper, we analyze these two problems and propose a blockchain-based naming system called blockDNS to solve them simultaneously. In blockDNS, domain names can be applied and transferred freely in a decentralized way. Moreover, a lightweight verification mechanism is also proposed coupled with blockDNS. The verification mechanism allows website clients to verify the authenticity of resolution results with few overheads. Simulation results show that, compared to the Simplified Payment Verification method, blockDNS can cut down the overheads for data authenticity verification from 4.955KBytes to 380Bytes. Moreover, we also present an implementation case of blockDNS, which is compatible with the current naming system and can be deployed incrementally.
Chinmay Ingle, Ashwin Samudre, Param Bhavsar, P. S. Vidap
Auditing is the inspection of the processes and work done during a period of time whereas compliance is the process of complying to the rules. In Service management space various organizations provide and use various services in the form of transactions and are dependent on each other. An organization may not use only one service to provide its services to the client, it may use multiple services like database, storage, processing, etc. Due to this involvement of multiple parties in the business, it is very tedious to manage these licenses and also more difficult to audit this system. Each organization have stored information at their own data centers, hence it is very hard to guarantee the trust between these organizations. To address this issue we propose a blockchain based solution to ensure transparency and security in the transactions between these organizations.
A U Mentsiev, V S Magomadov, M Z Ashakhanova, A U Mentsiev · 5 authors
Abstract This research gives a brief insight into one of the most widely used technologies known as Blockchain. The paper lays the groundwork of how Blockchain functions and how it has quickly become a network with millions of users throughout the world. This study discusses the most important use of Blockchain technology, which is the enhancement of the cyber-security industry. Blockchain has revolutionized the cyber-security industry by introducing a system which is not bound to any four walls and is widely distributed all around the world, seeking refuge on millions of user servers. This decentralized system helps Blockchain defend cyber networks against security threats and attacks such as malware, phishing attacks, and DDoS.
Temidayo Abayomi-Zannu, Isaac Odun-Ayo, T. F. Barka
Abstract Voting is fundamental to any consensus-based society and is one of the most critical functions of democracy. Mobile voting (m-voting) was utilized as a means for voters to easily and conveniently cast their votes using their mobile devices which have been the most adopted means of communication but has a major problem which is safely securing the casted votes and avoiding any form of tampering. In this paper, we propose an m-voting framework that utilizes blockchain technology to securely store the casted votes and multi-factor authentication to authenticate the voters before they cast their votes while also providing an easily accessible, secure and transparent m-voting system.
Leveraging the transparency, immutability, and security of Blockchain technology for the drug supply chain, a variety of researches on counterfeit medicines has recently implemented. However, the majority of Blockchain-based medicine management proposals lack practicality. Additionally, the problem of cloning of drug has not yet been resolved explicitly. In this paper, we propose a novel Blockchain-based product ownership management method for anti-counterfeit medicine system to resist the cloning of drug and improve the practical applicability. Analysis and evaluation results of our proposed system outperform the related proposals based on criteria about a practical application, anti-clone, low cost, customer-oriented, and scalability. Furthermore, experimental implementation on a small scale shows that our proposed system works appropriately in a real environment.
Ayman Alkhalifah, Alex Ng, Mohammad Jabed Morshed Chowdhury, A. S. M. Kayes · 5 authors
The establishment of Bitcoin in 2008 has introduced Blockchain technology to become the predominant new ways in digital transactions in many fields, such as energy, healthcare, and financial services. Although blockchain technology promised many advantages, it has suffered from various attacks with significant financial loses. We analyse 65 cybersecurity incidents that have been happening on the blockchain networks between 2011 and the first half-year of 2019. We categorize those incidents against the key cybersecurity vulnerabilities in blockchain technology, which showed an upward trend of an increasing number of attacks which dues to lack of security controls in the digital currency exchange and vulnerability in smart contracts. This result of this research highlights the needs in a major overhaul of the blockchain technology.
Blockchain is a distributed ledger system composed of P2P network proposed as an electronic cash system which can transfer money without a trusted third party. Blockchain has high tamper resistance by the structure which cannot modify a transaction by everyone including the creator of it. However, it also becomes a problem that Blockchain system cannot modify fraudulent transaction already approved. This problem means once an illegal transaction occurs, the damage expands. It is necessary to detect the transaction by the anomaly detection and modify it before approval in order to suppress the damage. However, existing anomaly detection methods of Blockchain need the processing for all the past transactions in Blockchain. The execution time exceeds the approval interval of the major Blockchain system (Ethereum). In this paper, we propose an anomaly detection method using a fixed size user-centric subgraph which is extracted from whole graph made from all the transactions, to prevent the increase of the execution time. Furthermore, to accelerate the anomaly detections, we propose the subgraph structure which is suitable for GPU processing so that all of the subgraph making, the feature extraction, and the anomaly detection are performed in GPU. When the number of transactions is 300 million, our proposed method archives 195 times faster than the existing GPU-based method and the execution time is shorter than the approval interval of the Ethereum. In terms of accuracy, the true positive rate is significantly higher than the existing method in the case of small scale transactions because the local anomaly can be detected by the subgraph with locality. And the rate in the case of large scale and the false positive rate are close to the existing method.
Ever since Bitcoin got its popularity, cryptocurrencies becomes the first choice of investors, tech enthusiasts, and criminals. Nowadays it became a prominent tool for money laundering, hawala and criminal payment system. Money laundering is the process of converting illegal black money to legal white money. This can be done by various ways like transferring money from one account to another via multiple intermediate accounts. Detecting this complex activity is very difficult and challenging, because of the high volume of transactions and bitcoins data structure. There are many methods proposed in different papers to detect money laundering in the conventional banking system. In this study, we proposed a framework to convert the bitcoin's transnational data into a similar data frame of bank's user database, which is used by some existing state of the art intelligent systems to detect the abnormal cluster of transactions and user behavior.
Abstract The paper proposes the exploration, identification and development of a Java solution for extracting the sentiment related to the cryptocurrencies phenomenon, from the content of the posts of certain popular social networks. Detecting the positive, neutral or negative character of the sentiment is adopted as a relevant method of establishing the nature of the human perception on the topical issue defined by cryptocurrencies.
Executing, verifying and enforcing credible transactions on permissionless blockchains is done using smart contracts. A key challenge with smart contracts is ensuring their correctness and security. To address this challenge, we present a fully automated technique, SolAnalyser, for vulnerability detection over Solidity smart contracts that uses both static and dynamic analysis. Analysis techniques in the literature rely on static analysis with a high rate of false positives or lack support for vulnerabilities like out of gas, unchecked send, timestamp dependency. Our tool, SolAnalyser, supports automated detection of 8 different vulnerability types that currently lack wide support in existing tools, and can easily be extended to support other types. We also implemented a fault seeding tool that injects different types of vulnerabilities in smart contracts. We use the mutated contracts for assessing the effectiveness of different analysis tools. Our experiment uses 1838 real contracts from which we generate 12866 mutated contracts by artificially seeding 8 different vulnerability types. We evaluate the effectiveness of our technique in revealing the seeded vulnerabilities and compare against five existing popular analysis tools - Oyente, Securify, Maian, SmartCheck and Mythril. This is the first large scale evaluation of existing tools that compares their effectiveness by running them on a common set of contracts. We find that our technique outperforms all five existing tools in supporting detection of all 8 vulnerability types and in achieving higher precision and recall rate. SolAnalyser was also faster in analysing the different vulnerabilities than any of the existing tools in our experiment.
Abeer ElBahrawy, Laura Alessandretti, Leonid Rusnac, Daniel Goldsmith · 6 authors
Dark markets are commercial websites that use Bitcoin to sell or broker transactions involving\ndrugs, weapons, and other illicit goods. Being illegal, they do not offer any user protection, and\nseveral police raids and scams have caused large losses to both customers and vendors over the past\nyears. However, this uncertainty has not prevented a steady growth of the dark market phenomenon\nand a proliferation of new markets. The origin of this resilience have remained unclear so far, also due\nto the difficulty of identifying relevant Bitcoin transaction data. Here, we investigate how the dark\nmarket ecosystem re-organises following the disappearance of a market, due to factors including raids\nand scams. To do so, we analyse 24 episodes of unexpected market closure through a novel datasets\nof 133 million Bitcoin transactions involving 31 dark markets and their users, totalling 4 billion USD.\nWe show that coordinated user migration from the closed market to coexisting markets guarantees\noverall systemic resilience beyond the intrinsic fragility of individual markets. The migration is\nswift, efficient and common to all market closures. We find that migrants are on average more active\nusers in comparison to non-migrants and move preferentially towards the coexisting market with\nthe highest trading volume. Our findings shed light on the resilience of the dark market ecosystem\nand we anticipate that they may inform future research on the self-organisation of emerging online\nmarkets.
Jiajing Wu, Qi Yuan, Dan Lin, Wei You · 7 authors
Recently, blockchain technology has become a topic in the spotlight but also a hotbed of various cybercrimes. Among them, phishing scams on blockchain have been found making a notable amount of money, thus emerging as a serious threat to the trading security of the blockchain ecosystem. In order to create a favorable environment for investment, an effective method for detecting phishing scams is urgently needed in the blockchain ecosystem. To this end, this paper proposes an approach to detect phishing scams on Ethereum by mining its transaction records. Specifically, we first crawl the labeled phishing addresses from two authorized websites and reconstruct the transaction network according to the collected transaction records. Then, by taking the transaction amount and timestamp into consideration, we propose a novel network embedding algorithm called trans2vec to extract the features of the addresses for subsequent phishing identification. Finally, we adopt the oneclass support vector machine (SVM) to classify the nodes into normal and phishing ones. Experimental results demonstrate that the phishing detection method works effectively on Ethereum, and indicate the efficacy of trans2vec over existing state-of-the-art algorithms on feature extraction for transaction networks. This work is the first investigation on phishing detection on Ethereum via network embedding and provides insights into how features of large-scale transaction networks can be embedded.
Mining attacks allow attackers to gain an unfair share of the mining reward by deviating from the honest mining strategy in the Bitcoin system. Among the most well-known are block withholding (BWH), fork after withholding (FAW), and selfish mining. In this paper, we propose two new strategies: power adjusting and bribery racing, and introduce two novel mining attacks, Power Adjusting Withholding (PAW) and Bribery Selfish Mining (BSM) adopting the new strategies. Both attacks can increase the reward of attackers. Furthermore, we show PAW can avoid the "miner's dilemma" in BWH attacks. BSM introduces a new "venal miner's dilemma", which results in all targets (bribes) willing to help the attacker but getting less reward finally. Quantitative analyses and simulations are conducted to verify the effectiveness of our attacks. We propose some countermeasures to mitigate the new attacks, but a practical and efficient solution remains to be an open problem.
Christof Ferreira Torres, Mathis Baden, Robert Norvill, Hugo Jonker
In recent years, smart contracts have suffered major exploits, losing millions of dollars. Unlike traditional programs, smart contracts cannot be updated once deployed. Though various tools were proposed to detect vulnerable smart contracts, they all fail to protect contracts that have already been deployed on the blockchain. Moreover, they focus on vulnerabilities, but do not address scams (e.g., honeypots). In this work, we introduce Æ GIS, a tool that shields smart contracts and users on the blockchain from being exploited. To this end, ÆGIS reverts transactions in real-time based on pattern matching. These patterns encode the detection of malicious transactions that trigger exploits or scams. New patterns are voted upon and stored via a smart contract, thus leveraging the benefits of tamper-resistance and transparency provided by blockchain. By allowing its protection to be updated, the smart contract acts as a smart shield.