Bitcoin is a cryptocurrency that features a distributed, decentralized and trustworthy mechanism, which has made Bitcoin a popular global transaction platform. The transaction efficiency among nations and the privacy benefiting from address anonymity of the Bitcoin network have attracted many activities such as payments, investments, gambling, and even money laundering in the past decade. Unfortunately, some criminal behaviors which took advantage of this platform were not identified. This has discouraged many governments to support cryptocurrency. Thus, the capability to identify criminal addresses becomes an important issue in the cryptocurrency network. In this paper, we propose new features in addition to those commonly used in the literature to build a classification model for detecting abnormality of Bitcoin network addresses. These features include various high orders of moments of transaction time (represented by block height) which summarizes the transaction history in an efficient way. The extracted features are trained by supervised machine learning methods on a labeling category data set. The experimental evaluation shows that these features have improved the performance of Bitcoin address classification significantly. We evaluate the results under eight classifiers and achieve the highest Micro-Fl /Macro-F1 of 87% /86% with LightGBM.
International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Sinh Huynh, Kenny Tsu Wei Choo, Rajesh Krishna Balan, Youngki Lee
Can cryptocurrency mining (crypto-mining) be a practical ad-free monetization approach for mobile app developers? We conducted a lab experiment and a user study with 228 real Android users to investigate different aspects of mobile crypto-mining. In particular, we show that mobile devices have computational resources to spare and that these can be utilized for crypto-mining with minimal impact on the mobile user experience. We also examined the profitability of mobile crypto-mining and its stability as compared to mobile advertising. In many cases, the profit of mining can exceed mobile advertising's. Most importantly, our study shows that the majority (72%) of the participants are willing to allow crypto-mining as means to replace ads to trade-off for benefits such as a better user experience.
Christof Ferreira Torres, Mathis Steichen, Radu State
Modern blockchains, such as Ethereum, enable the execution of so-called smart contracts - programs that are executed across a decentralised network of nodes. As smart contracts become more popular and carry more value, they become more of an interesting target for attackers. In the past few years, several smart contracts have been exploited by attackers. However, a new trend towards a more proactive approach seems to be on the rise, where attackers do not search for vulnerable contracts anymore. Instead, they try to lure their victims into traps by deploying seemingly vulnerable contracts that contain hidden traps. This new type of contracts is commonly referred to as honeypots. In this paper, we present the first systematic analysis of honeypot smart contracts, by investigating their prevalence, behaviour and impact on the Ethereum blockchain. We develop a taxonomy of honeypot techniques and use this to build HoneyBadger - a tool that employs symbolic execution and well defined heuristics to expose honeypots. We perform a large-scale analysis on more than 2 million smart contracts and show that our tool not only achieves high precision, but is also highly efficient. We identify 690 honeypot smart contracts as well as 240 victims in the wild, with an accumulated profit of more than $90,000 for the honeypot creators. Our manual validation shows that 87% of the reported contracts are indeed honeypots.
Shayan Eskandari, Seyedehmahsa Moosavi, Jeremy Clark
We consider front-running to be a course of action where an entity benefits from prior access to privileged market information about upcoming transactions and trades. Front-running has been an issue in financial instrument markets since the 1970s. With the advent of the blockchain technology, front-running has resurfaced in new forms we explore here, instigated by blockchains decentralized and transparent nature. In this paper, we draw from a scattered body of knowledge and instances of front-running across the top 25 most active decentral applications (DApps) deployed on Ethereum blockchain. Additionally, we carry out a detailed analysis of Status.im initial coin offering (ICO) and show evidence of abnormal miners behavior indicative of front-running token purchases. Finally, we map the proposed solutions to front-running into useful categories.
In this paper we revisit the mining strategies in proof of work based cryptocurrencies and propose two strategies, we call smart and smarter mining, that in many cases strictly dominate honest mining. In contrast to other known attacks, like selfish mining, which induce zero-sum games among the miners, the strategies proposed in this paper increase miners' profit by reducing their variable costs (i.e., electricity). Moreover, the proposed strategies are viable for much smaller miners than previously known attacks, and surprisingly, an attack performed by one miner is profitable for all other miners as well. While saving electricity power is very encouraging for the environment, it is less so for the coin's security. The smart/smarter strategies expose the coin to under 50\% attacks and this vulnerability might only grow when new miners join the coin as a response to the increase in profit margins induced by these strategies.
Mehrnoosh Mirtaheri, Sami Abu-El-Haija, Fred Morstatter, Greg Ver Steeg · 5 authors
Interest surrounding cryptocurrencies, digital or virtual currencies that are used as a medium for financial transactions, has grown tremendously in recent years. The anonymity surrounding these currencies makes investors particularly susceptible to fraud---such as ``pump and dump'' scams---where the goal is to artificially inflate the perceived worth of a currency, luring victims into investing before the fraudsters can sell their holdings. Because of the speed and relative anonymity offered by social platforms such as Twitter and Telegram, social media has become a preferred platform for scammers who wish to spread false hype about the cryptocurrency they are trying to pump. In this work we propose and evaluate a computational approach that can automatically identify pump and dump scams as they unfold by combining information across social media platforms. We also develop a multi-modal approach for predicting whether a particular pump attempt will succeed or not. Finally, we analyze the prevalence of bots in cryptocurrency related tweets, and observe a significant increase in bot activity during the pump attempts.
We study selfish mining in Ethereum. The problem is combinato-rially more complex than in Bitcoin because of major differences in the reward system and a different difficulty adjustment formula. Equivalent strategies in Bitcoin do have different profitabilities in Ethereum. The attacker can either broadcast his fork one block by one, or keep them secret as long as possible and publish them all at once at the end of an attack cycle. The first strategy is damaging for substantial hashrates, and we show that the second strategy is even worse. This confirms what we already proved for Bitcoin: Selfish mining is most of all an attack on the difficulty adjustment formula. We show that the current reward for signaling uncle blocks is a weak incentive for the attacker to signal blocks. We compute the profitabilities of different strategies and find out that for a large parameter space values, strategies that do not signal blocks are the best ones. We compute closed-form formulas for the apparent hashrates for these strategies and compare them. We use a direct combinatorics analysis with Dyck words to find these closed-form formulas.
The transparent and immutable nature of blockchain provides incentives for organizations wishing to create and implement an open, decentralized governance structure. As members exercise their voting rights, a fault-tolerant record accumulates on the blockchain that can be analyzed to diagnose and intercept potential threats to the governing body. To date, there has not been a systematic study of on-chain governance with respect to voting. In this paper, we provide an analysis of blockchain governance through a case study of the first cryptocurrency to adopt on-chain voting, Dash. Our analysis introduces the key characteristics of blockchain governance, steps through a data-driven exploration of Dash's on-chain voting system, and highlights exploitable attack vectors and vulnerabilities for the subversion of Dash's on-chain voting system via a novel network analysis methodology. We then conclude with guidelines for other organizations looking to implement similar blockchain governance solutions while maintaining integrity in their operations.
We propose a token-based blockchain system that streamlines abstractions into a universal token structure. In the proposed system, each token has an identity that enables the implementation of specific rollbacks and governance that make 51% attacks unprofitable. Because the token-based bookkeeping method only verifies and updates the ownership within each transaction, the proposed system supports parallel expansion and cross-chain transactions without limit. The flexible authority management mechanism of the proposed system is regulatory-friendly, as the intensity of supervision and governance can be adapted to accommodate different application scenarios.
In proof-of-work-based (PoW-based) blockchain networks, the miners participate in a crypto-puzzle solving competition to win the reward by publishing a new block. Open mining pools attract a large number of miners for solving difficult problems together. Although the open strategy is likely to be more efficient, it makes pools susceptible to attack at the same time. In this paper, we present a game-theoretic analysis of mining pool strategy selection in order to explore the trade-off between the efficiency of openness and the vulnerability of attacks in a PoW-based blockchain network. We first model the pool mining process as a two-stage game, wherein the pools might decide whether to open or not and to attack or not. Based on the two-stage game model, we analyze the Nash equilibrium and the evolutionary stability of the mining games among pools, which uncovers the pool selection dynamics of PoW-based blockchain networks. In particular, we find that the attack behavior is the norm for a weak pool and triggers lower expected utilities when punishing the attacks more severely. Numerical simulations also support our theoretical findings as well as demonstrate the stability of the pools’ strategy selection.
Rumors and misleading information detection and prevention still represent a big challenge against social network developers and researchers. Since newsworthy information propagation is a traditional behavior of most of the users in social media, then verifying information credibility and reliability is indeed a vital security requirement for social network platforms. Due to its immutability, security, tamper-proof and P2P design, Blockchain as a powerful technology can provide a magical solution to overcome this challenge. This Paper introduces a novel blockchain approach called Proof of Credibility (PoC) for detecting fake news and blocking its propagation in social networks. The functionality of the PoC protocol has been simulated on two datasets of newsworthy tweets collected from different news sources on Twitter. The results clarified a satisfying performance and efficiency of the proposed approach in detecting rumors and blocking its propagation.
Many people talk about blockchain but very few understand its true nature and potential. Blockchain seems very exciting yet simultaneously a bit confusing, and naturally many people, businesses, and governments approach it with high expectations while also exhibiting some hesitancy. This article will deal with the use of blockchain in relation to government applications. A proper assessment of such use requires a discussion on blockchain standards, which are currently developed, or may develop in the future. Without blockchain standards, any potential use of blockchain in government will be of limited and restricted value. This would render our discussion on government applications rather limited too. Standards enable us to appreciate blockchain applications in a useful way for future applications outside the context of government. Focusing our attention to government applications is deliberate. Blockchain, obviously, provides amazing opportunities for the private sector. Over the last years there has been widespread public disbelief in many public and government institutions. Corruption, fraud, lack of transparency, alienation and disconnection of citizen from decision-making centres oblige governments to change and offer proper governance conditions for their citizens. Further, higher consumer expectations in all sectors of the economy naturally affect the expectations of citizens vis--vis their governments. For the above reasons, governments could leverage the positive features of blockchain to restore their
The present paper explores the current development of cryptocurrencies, emphasizing the concept of trust related to the blockchain technology and the digital currency market. The study offers a fundamental review of relevant research papers on Bitcoin, examining the main issues of trust among five categories of stakeholders: Governments, users, miners, exchanges and merchants. The results highlight the trust challenges on Bitcoin, reveling a unique perspective of risks on the cryptocurrency market, contagion effects, decentralisation systems or cryptocurrency regulation. The blockchain features are explained in order to better understand the Bitcoin mechanism, presenting the advantages of using such technology, concluding that Bitcoin is a product of the mistrust in financial institutions and an attempt to use alternative payment systems in a more secure way.
Applications of blockchain technologies got a lot of attention in recent years. They exceed beyond exchanging value and being a substitute for fiat money and traditional banking system. Nevertheless, being able to exchange value on a blockchain is at the core of the entire system and has to be reliable. Blockchains have built-in mechanisms that guarantee whole system's consistency and reliability. However, malicious actors can still try to steal money by applying well known techniques like malware software or fake emails. In this paper we apply supervised learning techniques to detect fraudulent accounts on Ethereum blockchain. We compare capabilities of Random Forests, Support Vector Machines and XGBoost classifiers to identify such accounts basing on a dataset of more than 300 thousands accounts. Results show that we are able to achieve recall and precision values allowing for the designed system to be applicable as an anti-fraud rule for digital wallets or currency exchanges. We also present sensitivity analysis to show how presented models depend on particular feature and how lack of some of them will affect the overall system performance.
Roy Bar-Haim, Dalia Krieger, Orith Toledo‐Ronen, Lilach Edelstein · 10 authors
Roy Bar-Haim, Dalia Krieger, Orith Toledo-Ronen, Lilach Edelstein, Yonatan Bilu, Alon Halfon, Yoav Katz, Amir Menczel, Ranit Aharonov, Noam Slonim. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.
Johannes Beck, Roberta Huang, David Lindner, Tian Guo · 7 authors
The ability to track and monitor relevant and important news in real-time is of crucial interest in multiple industrial sectors. In this work, we focus on the set of cryptocurrency news, which recently became of emerging interest to the general and financial audience. In order to track relevant news in real-time, we (i) match news from the web with tweets from social media, (ii) track their intraday tweet activity and (iii) explore different machine learning models for predicting the number of the article mentions on Twitter within the first 24 hours after its publication. We compare several machine learning models, such as linear extrapolation, linear and random forest autoregressive models, and a sequence-to-sequence neural network. We find that the random forest autoregressive model behaves comparably to more complex models in the majority of tasks.
The Dark Web is notorious for being a major distribution channel of harmful content as well as unlawful goods.Perpetrators have also used cryptocurrencies to conduct illicit financial transactions while hiding their identities.The limited coverage and outdated data of the Dark Web in previous studies motivated us to conduct an in-depth investigative study to understand how perpetrators abuse cryptocurrencies in the Dark Web.We designed and implemented MFScope, a new framework which collects Dark Web data, extracts cryptocurrency information, and analyzes their usage characteristics on the Dark Web.Specifically, MFScope collected more than 27 million dark webpages and extracted around 10 million unique cryptocurrency addresses for Bitcoin, Ethereum, and Monero.It then classified their usages to identify trades of illicit goods and traced cryptocurrency money flows, to reveal black money operations on the Dark Web.In total, using MFScope we discovered that more than 80% of Bitcoin addresses on the Dark Web were used with malicious intent; their monetary volume was around 180 million USD, and they sent a large sum of their money to several popular cryptocurrency services (e.g., exchange services).Furthermore, we present two real-world unlawful services and demonstrate their Bitcoin transaction traces, which helps in understanding their marketing strategy as well as black money operations.
Kentaroh Toyoda, P. Takis Mathiopoulos, Tomoaki Ohtsuki
Bitcoin is one of the most popular decentralized cryptocurrencies to date. However, it has been widely reported that it can be used for investment scams, which are referred to as high yield investment programs (HYIP). Although from the security forensic point of view it is very important to identify the HYIP operators' Bitcoin addresses, so far in the open technical literature no systematic method which reliably collects and identifies such Bitcoin addresses has been proposed. In this paper, a novel methodology is introduced, which efficiently collects a large number of the HYIP operators' Bitcoin addresses and identifies them based upon a novel analysis of their transactions history. In particular, a scraping-based method is first proposed which is able to collect more than 2,000 HYIP operators' Bitcoin addresses from the Internet thus providing a large number of the HYIPs' samples. Second, a supervised machine learning technique, which classifies, whether or not, specific Bitcoin addresses belong to the HYIP operators, is introduced and its performance is evaluated. The proposed classification method is based upon two novel approaches, namely the rate conversion technique that mitigates the effect of Bitcoin price volatility and the sampling technique that reduces the computational amount without sacrificing the classification performance. By employing close to 30,000 real Bitcoin addresses, extensive performance evaluation results obtained by means of computer simulation experiments have shown that the proposed methodology achieves excellent performance, i.e., 95% of the HYIP addresses can be correctly classified, while maintaining a false positive rate less than 4.9%. In order to further validate the proposed classifier's ability to detect the HYIP operators' Bitcoin addresses, our designed classifier has been tested against a recently published list of the HYIP addresses maintaining its excellent detection accuracy by achieving a 93.75% success rate.
Open access
Blockchain Technology Applications and Security
Spam and Phishing Detection
Advanced Steganography and Watermarking Techniques