Yuxi Cai, Georgios Fragkos, Eirini Eleni Tsiropoulou, Andreas Veneris
Many blockchain applications use decentralized oracles to trustlessly retrieve external information as those platforms are agnostic to real-world information. Some existing decentralized oracle protocols make use of majority-voting schemes to determine the outcomes and/or rewards to participants. In these cases, the awards (or penalties) grow linearly to the participant stakes, therefore voters are indifferent between voting through a single or multiple identities. Furthermore, the voters receive a reward only when they agree with the majority outcome, a tactic that may lead to herd behavior. This paper proposes an oracle protocol based on peer prediction mechanisms with non-linear staking rules. In the proposed approach, instead of being rewarded when agreeing with a majority outcome, a voter receives awards when their report achieves a relatively high score based on a peer prediction scoring scheme. The scoring scheme is designed to be incentive compatible so that the maximized expected score is achieved only with honest reporting. A non-linear stake scaling rule is proposed to discourage Sybil attacks. This paper also provides a theoretical analysis and guidelines for implementation as reference.
Bitcoin is designed to allow its users to send and receive payments under a pseudonym. In Bitcoin, A pseudonym is an address from/to which a user sends/receives coins. Owing to users anonymity, one-half of the transactions in Bitcoin are associated with illegal trading or money laundering. Hence, it is necessary to develop an approach that estimates the number of coins owned by Bitcoin users and identifies transactions which deal with illegal activities. Considering that every address involved in the Bitcoin transaction is stored permanently in the ledger. If an address is ever mapped to a user identity, every transaction wrote by that user will be linked. In this study, we proposed a new method to identify the number of coins owned by users, in particular, we estimate the number of bitcoins held by the Bitcoin creator Satoshi Nakamoto, by analyzing the mining hash rate, mining reward and transactions data.
It is widely accepted that Ethereum mining is highly centralized. Nonetheless, centralization has been mostly characterized by exclusively looking at the influence that independent miners or mining pools can have over the network. Moreover, models of mining behavior assume that miners are either unrelated or only relate via mining pools under highly structured and transparent agreements. If these assumptions and the predictions they entail were to be completely accurate, there would not be any evidence of on-chain transactions between miners, other than the ones expected from mining pool payouts. By looking at on-chain transactions between miners in the Ethereum Network we find that aside from the payouts from mining pools to small miners, there are also transactions that define relationships between mining pools, independent miners and between independent miners and mining pools. Furthermore, by characterizing the topology of the network of miner transactions, we find the emergence of highly connected clusters that control significant amounts of hashing power and exhibit relationships in the opposite direction of what theoretical models predict. This more nuanced characterization of mining centralization can help identify network vulnerabilities and inform protocol redesigns.
Emad Badawi, Guy-Vincent Jourdan, Gregor von Bochmann, Iosif-Viorel Onut
We investigate what we call the "Bitcoin Generator Scam" (BGS), a simple system in which the scammers promise to "generate" new bitcoins using the ones that were sent to them. A typical offer will suggest that, for a small fee, one could receive within minutes twice the amount of bitcoins submitted. BGS is clearly not a very sophisticated attack. The modus operandi is simply to put up some web page on which to find the address to send the money and wait for the payback. The pages are then indexed by search engines, and ready to find for victims looking for free bitcoins. We describe here a generic system to find and analyze scams such as BGS. We have trained a classifier to detect these pages, and we have a crawler searching for instances using a series of search engines. We then monitor the instances that we find to trace payments and bitcoin addresses that are being used over time. Unlike most bitcoin-based scam monitoring systems, we do not rely on analyzing transactions on the blockchain to find scam instances. Instead, we proactively find these instances through the web pages advertising the scam. Thus our system is able to find addresses with very few transactions, or even none at all. Indeed, over half of the addresses that have eventually received funds were detected before receiving any transactions. The data for this paper was collected over four months, from November 2019 to February 2020. We have found more than 1,300 addresses directly associated with the scam, hosted on over 500 domains. Overall, these addresses have received (at least) over 5 million USD to the scam, with an average of 47.3 USD per transaction.
A blockchain records transactions among users on a public ledger. It has become front and center of the technology discussion in recent years. A piece of code deployed on a ledger and executed automatically by nodes on the network is a smart contract. While smart contracts have enabled a variety of applications on blockchain, they may contain security vulnerabilities, leading to massive research on smart contract analysis. This paper presents the first comprehensive survey over smart contract analysis by collecting 391 papers, extracting 67 analysis-related ones, and classifying them into three dominant topics: static analysis for vulnerability detection, static analysis for program correctness, and dynamic analysis. We further classify each topic and conclude with key insights in terms of unsolved challenges and directions in future research.
For the management of cryptocurrencies or cryptographic tokens, many users employ a software wallet that facilitates the interaction with a blockchain in general or with on-chain programs (smart contracts) in particular. While many blockchain wallets execute their core program code off-chain, some wallets implement core functionality on-chain as smart contracts with the intent to increase trust and security by using transparent and verifiable execution.In this work, we investigate smart contracts for wallets with regard to the functionality that makes use of cryptographically secured blockchain technology. We focus on wallet contracts deployed on Ethereum, as it is the most prominent platform for tokens and smart contracts with readily available data. We aim at a better understanding of this frequently deployed group of smart contracts by analyzing characteristics of wallet contracts and grouping them into six types. To this end, we present approaches to identify wallet contracts by analyzing source code, bytecode, and execution traces extracted from transaction data. Moreover, we investigate usage scenarios and patterns. From the derived data, we extract blueprints for wallets and compile a ground truth. We provide numbers and temporal perspectives regarding the creation and use of wallets.
Taehyun Kim, Hyomin Shin, Hyung Ju Hwang, Seungwon Jeong
Steemit is a blockchain-based social media platform, where authors can get author rewards in the form of cryptocurrencies called STEEM and SBD (Steem Blockchain Dollars) if their posts are upvoted. Interestingly, curators (or voters) can also get rewards by voting others' posts, which is called a curation reward. A reward is proportional to a curator's STEEM stakes. Throughout this process, Steemit hopes "good" content will be automatically discovered by users in a decentralized way, which is known as the Proof-of-Brain (PoB). However, there are many bot accounts programmed to post automatically and get rewards, which discourages real human users from creating good content. We call this type of bot a posting bot. While there are many papers that studied bots on traditional centralized social media platforms such as Facebook and Twitter, we are the first to study posting bots on a blockchain-based social media platform. Compared with the bot detection on the usual social media platforms, the features we created have an advantage that posting bots can be detected without limiting the number or length of posts. We can extract the features of posts by clustering distances between blog data or replies. These features are obtained from the Minimum Average Cluster from Clustering Distance between Frequent words and Articles (MAC-CDFA), which is not used in any of the previous social media research. Based on the enriched features, we enhanced the quality of classification tasks. Comparing the F1-scores, the features we created outperformed the features used for bot detection on Facebook and Twitter.
In Proof-of-Work Blockchain-based systems, the ledger is kept consistent through some participants solving cryptopuzzles, usually referred to as block mining. Conventional wisdom asserts that the mining protocol is incentive-compatible. However, whether some strategic mining behaviors occur in practice or not, has been the subject of extensive debate. In this paper, we target this question by detecting anomalies in the statistics of consecutive blocks among several popular cryptocurrency systems. Firstly, we measure the inequality of mining revenue distribution in each system. Secondly, we propose a statistical method to identify the selfish mining (SM) behavior, a mining attack strategy posited by Eyal and Sirer in 2014. Our method is based on abnormal (statistically significant) high probability of continuously mining blocks. Finally, we extend our method to detect the mining cartels, in which miners secretly get together and share information about newly mined blocks. Our analysis will contribute to the research of fairness in cryptocurrency mining by providing evidence that the aforementioned strategic mining behaviors do take place in practice.
Alexandra Mai, Katharina Pfeffer, Matthias Gusenbauer, Edgar Weippl · 5 authors
Frequent reports of monetary loss, fraud, and user-caused security incidents in the context of cryptocurrencies emphasize the need for human-centered research in this domain. We contribute the first qualitative user study (N=29) on user mental models of cryptocurrency systems and the associated threat landscape. Using Grounded Theory, we reveal misconceptions affecting users' security and privacy. Our results suggest that current cryptocurrency tools (e.g., wallets and exchanges) are not capable of counteracting threats caused by these misconceptions. Hence, users frequently fail to securely manage their private keys or assume to be anonymous when they are not. Based on our findings, we contribute actionable advice, grounded in the mental models of users, to improve the usability and secure usage of cryptocurrency systems.
Summary Blockchain users are identified by addresses (public keys), which cannot be easily linked back to them without out‐of‐network information. This provides pseudo‐anonymity, which is amplified when the user generates a new address for each transaction. Since all transaction history is visible to all users in public blockchains, finding affiliation between related addresses undermines pseudo‐anonymity. Such affiliation information can be used to discriminate against addresses linked with undesired activities or can lead to de‐anonymization if out‐of‐network information becomes available. In this work, we propose an approach to undermine pseudo‐anonymity of blockchain transactions by linking together addresses that were used to deploy smart contracts, which were produced by the same authors. In our approach, we leverage stylometry techniques, widely used in the social science field for attribution of literary texts to their corresponding authors. The assumption underlying authorship attribution is the existence of a distinctive writing style, unique to an author and easily distinguishable from others. Drawing an analogy between literary text and smart contracts' source code, we explore the extent to which unique features of source code and byte code of Ethereum smart contracts can represent the coding style of smart contract developers. We show that even a small number of representative features leads to a sufficiently high accuracy in attributing smart contracts' code to its deployer's address. We further validate our approach on real‐world scammers' data and Ponzi scheme‐related contracts. Additionally, we provide an algorithm to extract distinctly contributing features per an entire dataset or per specific authors. We use this algorithm to extract and explore such features in our dataset and in the Ponzi scheme‐related dataset.
As Ethereum's smart contracts have boomed, it has become an integral part of the blockchain ecosystem. Unfortunately, some malicious users also find the opportunity to use fraudulent means to profit. A new reported approach is to lure new users or other attackers into the contract in an attempt to make a profit by exposing seemingly obvious flaws in the contract. But in fact, the contract contains a hidden trap that ultimately benefits the creator of the contract. Such contracts are known as honeypot contracts in the blockchain ecosystem. Previous studies proposed two methods to identify such smart contracts by using symbolic execution and contract behaviors. However, these methods either make it difficult to discover new categories or fail to warn users before they lose money. To solve this problem, we propose a machine learning model to detect honeypot contracts based on N-gram features and LightGBM. Extensive experiments show that our proposed model performs well in different conditions.
Many popular blockchain platforms are supporting smart contracts for building decentralized applications. However, the vulnerabilities within smart contracts have led to serious financial loss to their end users. For the EOSIO blockchain platform, effective vulnerability detectors are still limited. Furthermore, existing vulnerability detection tools can only support one blockchain platform. In this work, we present WANA, a cross-platform smart contract vulnerability detection tool based on the symbolic execution of WebAssembly bytecode. Furthermore, WANA proposes a set of test oracles to detect the vulnerabilities in EOSIO and Ethereum smart contracts based on WebAssembly bytecode analysis. Our experimental analysis shows that WANA can effectively detect vulnerabilities in both EOSIO and Ethereum smart contracts with high efficiency.
As COVID-19 has been spreading across the world since early 2020, a growing number of malicious campaigns are capitalizing the topic of COVID-19. COVID-19 themed cryptocurrency scams are increasingly popular during the pandemic. However, these newly emerging scams are poorly understood by our community. In this paper, we present the first measurement study of COVID-19 themed cryptocurrency scams. We first create a comprehensive taxonomy of COVID-19 scams by manually analyzing the existing scams reported by users from online resources. Then, we propose a hybrid approach to perform the investigation by: 1) collecting reported scams in the wild; and 2) detecting undisclosed ones based on information collected from suspicious entities (e.g., domains, tweets, etc). We have collected 195 confirmed COVID-19 cryptocurrency scams in total, including 91 token scams, 19 giveaway scams, 9 blackmail scams, 14 crypto malware scams, 9 Ponzi scheme scams, and 53 donation scams. We then identified over 200 blockchain addresses associated with these scams, which lead to at least 330K US dollars in losses from 6,329 victims. For each type of scams, we further investigated the tricks and social engineering techniques they used. To facilitate future research, we have released all the well-labelled scams to the research community.
COVID-19 is a severe global epidemic in human history. Even though there are particular medications and vaccines to curb the epidemic, tracing and isolating the infection source is the best option to slow the virus spread and reduce infection and death rates. There are three disadvantages to the existing contact tracing system: 1. User data is stored in a centralized database that could be stolen and tampered with, 2. User’s confidential personal identity may be revealed to a third party or organization, 3. Existing contact tracing systems [1][2] only focus on information sharing from one dimension, such as location-based tracing, which significantly limits the effectiveness of such systems.We propose a global COVID-19 information sharing and risk notification system that utilizes the Blockchain, Smart Contract, and Bluetooth. To protect user privacy, we design a novel Blockchain-based platform that can share consistent and non-tampered contact tracing information from multiple dimensions, such as location-based for indirect contact and Bluetooth-based for direct contact. Hierarchical smart contract architecture is also designed to achieve global agreements from users about how to process and utilize user data, thereby enhancing the data usage transparency. Furthermore, we propose a mechanism to protect user identity privacy from multiple aspects. More importantly, our system can notify the users about the exposure risk via smart contracts. We implement a prototype system to conduct extensive measurements to demonstrate the feasibility and effectiveness of our system.
Ben Weintraub, Cristina Nita-Rotaru, Stefanie Roos
Payment channel networks (PCN) enable scalable blockchain transactions without fundamentally changing the underlying distributed ledger algorithm. However, routing a payment via multiple channels in a PCN requires locking collateral for potentially long periods of time. Adversaries can abuse this mechanism to conduct denial-of-service attacks. Previous work focused on source routing, which is unlikely to remain a viable routing approach as these networks grow. In this work, we examine the effectiveness of attacks in PCNs that use routing algorithms based on local knowledge, where compromised intermediate nodes can delay or drop transactions to create denial-of-service. We focus on SpeedyMurmurs as a representative of such protocols. We identify two attacker node selection strategies; one based on the position in the routing tree, and the other on between-ness centrality. Our simulation-driven study shows that while they are both effective, the centrality-based attack approaches near-optimal effectiveness. We also show that the attacks are ineffective in less centralized networks and discuss incentives for the participants in PCNs to create less centralized topologies through the payment channels they establish among themselves.
Ben Weintraub, Cristina Nita-Rotaru, Stefanie Roos
Payment channel networks (PCN) enable scalable blockchain transactions without fundamentally changing the underlying distributed ledger algorithm. However, routing a payment via multiple channels in a PCN requires locking collateral for potentially long periods of time. Adversaries can abuse this mechanism to conduct denial-of-service attacks. Previous work on denial-of-service attacks focused on source routing, which is unlikely to remain a viable routing approach as these networks grow.
In this work we examine the effectiveness of attacks in PCNs that use routing algorithms based on local knowledge, where compromised intermediate nodes delay or drop transactions to create denial-of-service. We focus on SpeedyMurmurs as a representative of such protocols. Our attack simulations show that SpeedyMurmurs is resilient to attacks by randomly selected intermediate nodes because it dynamically adjusts using local knowledge. We further consider attackers that control a significant fractions of paths and we show that this ability to route around problematic regions becomes insufficient for such attackers. We propose methods to incentivize payment channel networks with less central nodes and more diverse paths and show through simulation that these methods effectively mitigate the identified denial-of-service attacks.
Pharmaceutical companies and other parties involved in drug supply chain face difficulties in meeting the traceability requirements from the regulatory bodies in different jurisdictions and in their capability to authenticate goods and drugs, which result in failure to combat major issues such as drugs counterfeiting. Counterfeit drugs are estimated for up to $200 billion per year in market value. This issue is contributing to the deaths of thousands of people due to use of ineffective drugs for serious illnesses. The drug supply chain is fragmented, and that prohibits establishing a global view on handling drugs from manufacturing until reaching the customer, with many loopholes that permit the introduction of counterfeit products that patients cannot authenticate. The recently introduced regulations in the US and the EU demand building interoperable electronic systems to enable tracing of drugs. Those regulations rely mainly on attaching a two-dimensional barcode for identification. However, applying these regulations and ensuring to have global view and perseverance of records and actions are not yet addressed. Blockchain’s immutability, provenance, and tokenization features provide a solution to address this and meet the requirements of authenticating drugs source and secure the supply chain. This chapter presents the use of non-fungible tokens to build a drug supply chain tool that enables identifiers creation, authentication of origin, transfer of ownership, and accessibility to drugs information by different stakeholders, including end customers.
Education Degree Fraud Detection and Student Certificate Verification using Blockchain - written by Jayesh G. Dongre , Sonali M. Tikam , Vasudha B. Gharat published on 2020/07/13 download full article with reference data and citations
A proof of work (PoW) blockchain protocol distributes rewards to its participants, called miners, according to their share of the total computational power. Sufficiently large miners can perform selfish mining - deviate from the protocol to gain more than their fair share. Such systems are thus secure if all miners are smaller than a threshold size so their best response is following the protocol. To find the threshold, one has to identify the optimal strategy for miners of different sizes, i.e., solve a Markov Decision Process (MDP). However, because of the PoW difficulty adjustment mechanism, the miners' utility is a non-linear ratio function. We therefore call this an Average Reward Ratio (ARR) MDP. Sapirshtein et al.\ were the first to solve ARR MDPs by solving a series of standard MDPs that converge to the ARR MDP solution. In this work, we present a novel technique for solving an ARR MDP by solving a single standard MDP. The crux of our approach is to augment the MDP such that it terminates randomly, within an expected number of rounds. We call this Probabilistic Termination Optimization (PTO), and the technique applies to any MDP whose utility is a ratio function. We bound the approximation error of PTO - it is inversely proportional to the expected number of rounds before termination, a parameter that we control. Empirically, PTO's complexity is an order of magnitude lower than the state of the art. PTO can be easily applied to different blockchains. We use it to tighten the bound on the threshold for selfish mining in Ethereum.