Alkhansaa A. Abuhashim, Hassan A. Shafei, Chiu C. Tan
This paper proposes a global blockchain-based vaccination certification. To facilitate issuing and verifying certificates globally, the framework is lightweight on the user's side. The essential certification operations, issuing certificate, verifying certificate, and verifying vaccine, are implemented preserving user's privacy. Our framework's smart contracts design is implemented on the Ethereum blockchain test network to support indexing and querying the certification data. The experimental results show the effectiveness of the proposed approach in improving the performance of the primary functionalities. The results show the retrieving time of certificate information is efficient no matter how long the chain is, compared with scanning the blocks to find the target data.
Xucan Chen, Wei Cheng, Marie Ouellet, Yuan Li · 6 authors
Darknet markets are e-commerce websites operating on the darknet and have grown rapidly in recent years. Darknet only allow cryptocurrencies as the payment methods, making it hard for law enforcement to trace those illicit transactions. In this paper, we present a method to identify vendors’ bitcoin addresses by matching vendors’ feedback reviews with bitcoin transactions in the public ledger. The problem is decomposed into two steps in formulation. In Step 1, we solve a bounding box matching between the set of feedback reviews and bitcoin transactions. In Step 2, we find the bitcoin addresses with a maximum coverage of the reviews. Baseline algorithm for Step 1 runs in quadratic time thus we develop a K-D tree to accelerate the computing. Problem in Step 2 is NP-hard thus we develop a greedy algorithm with an approximation ratio of (1 − 1/e) based on the submodular property of the objective function. We further propose a cost-effective algorithm to accelerate both steps effectively. Comprehensive experimental results have demonstrated the effectiveness and efficiency of the proposed method.
Demand forecasting, supply acquisition in healthcare supply chains are significant concerns spanning various organizations and bodies, rendering a crucial backbone to medical services necessary for everyday living. A global pandemic resulting in critical demand for medications and vaccines was an eye-opener in the current era. The intrinsic complexity among the bodies involved in the supply chain results in a lack of data transparency, security, privacy, and reliable communication. The counterfeited drug is an outcome of such limitations that adversely affects a global population. Consecutively, fair allocation and distribution of drugs and vaccines to administer them to a global mass equally is also a significant concern. Blockchain as technology grants an essential platform to track and manage transactions among communicating parties in the supply chain using a peer-to-peer, secured, distributed ledger, removing the need for intermediaries or entrusted third parties. Most existing studies focus on tracking and tracing supply chain systems in a centralized manner, leading to transparency, authenticity, data privacy, and authenticity concerns in healthcare supply chains. In this article, we propose a FAIR blockchain-based approach deploying smart contracts leading to transparent traceability of data and transactions in the healthcare supply chain between the communicating parties We propose an approach that allows fair allocation and distribution of vaccines as per the demand generated from the global population. We present a system architecture and algorithm representing the communication between parties that governs our proposed approach. We have computed network performance based and blockchain based evaluation of the proposed system. We have calculated the communication and computation cost of 1152 bits and 12.6 ms respectively.
Dylan Eck, Adam Torek, Steve Cutchin, Gaby G. Dagher
Bitcoin is a decentralized cryptocurrency that enables entities to transfer funds psuedo-anonymously. Investigative services like the FBI, however, have been able to subvert this using various techniques. To counter this, the Bitcoin community uses several methods to obscure transactions, including shared send transactions among others. We attempt to peer into these transactions through a pairing of address clustering and shared send untangling and test our experimental results through path finding between addresses. We then implement our methodology to test its effectiveness. Our findings show that while using clustering can improve path-finding results and shared send untangling, we recommend applying heuristics in combination to increase effectiveness.
Júlia Almeida Valadares, Vinícius Cunha Oliveira, José Eduardo de Azevedo Sousa, Heder S. Bernardino · 7 authors
Ethereum is one of the largest blockchain platforms currently that has become a digital business environment for users. This platform is designed to allow decentralized transactions between anonymous users. Thus, the development of methods to identify user behavior profiles, keeping their identities anonymous, has the potential to leverage business on this platform. In this work, we investigate the use of machine learning to classify a user profile as professional or common based on the attributes of their transactions. This classification is challenging due to the small fraction of publicly labeled users in Ethereum and still the considerably smaller fraction of professional users. To conduct this investigation, we train models considering carefully balanced sets of transactions with labeled users. Our results show high performance models for the classification of profiles, achieving a performance greater than 90% for accuracy, precision, and other related measures. In addition, we have identified the most relevant features in transactions for this classification.
Deepesh Chaudhari, Rachit Agarwal, Sandeep K. Shukla
The temporal aspect of blockchain transactions enables us to study the address's behavior and detect if it is involved in any illicit activity. However, due to the concept of change addresses (used to thwart replay attacks), temporal aspects are not directly applicable in the Bitcoin blockchain. Several pre-processing steps should be performed before such temporal aspects are utilized. We are motivated to study the Bitcoin transaction network and use the temporal features such as burst, attractiveness, and inter-event time along with several graph-based properties such as the degree of node and clustering coefficient to validate the applicability of already existing approaches known for other cryptocurrency blockchains on the Bitcoin blockchain. We generate the temporal and non-temporal feature set and train the Machine Learning (ML) algorithm over different temporal granularities to validate the state-of-the-art methods. We study the behavior of the addresses over different time granularities of the dataset. We identify that after applying change-address clustering, in Bitcoin, existing temporal features can be extracted and ML approaches can be applied. A comparative analysis of results show that the behavior of addresses in Ethereum and Bitcoin is similar with respect to in-degree, out-degree and inter-event time. Further, we identify 3 suspects that showed malicious behavior across different temporal granularities. These suspects are not marked as malicious in Bitcoin.
Blockchain networks are mainly used in financial verification processes, the most famous of which is the Bitcoin network consisting of a huge number of nodes. Expansion of blockchain technology, however, to other areas, such as healthcare, arts, culture, and entertainment, is being considered internationally. Especially important challenges for ensuring the reliability of blockchain networks and the security of peer-to-peer networks are shortening the block propagation times throughout a blockchain network to reduce fork and preventing malicious eclipse attacks against targeted nodes in a network. Previous methods have tried to increase the block propagation speed at the expense of imposing a higher burden on each node and a higher risk of eclipse attack. This paper proposes a new neighbor selection method based on the neighbor's regional information. That is, each node has a relatively small number neighbors located outside its region. By using this simple method, the distribution of blocks throughout the network becomes faster and the random neighbor selection nature in a blockchain network is kept intact; thus, risk of eclipse attack is low.
The growing popularity of Blockchain networks attracts also malicious and hacking users. Effectively detecting inappropriate and malicious activity should thus be a top priority for safeguarding blockchain networks and services. Blockchain behavior analysis can be used to detect unusual account activities or time periods with network-wide irregular properties. Thus, optimized anomaly detection based on historical data is an essential task for securing transactions and services. However, processing the complete blockchain history can be slow and costly due to its large size and rapid growth. In this paper we suggest addressing this challenge by analyzing summarized blocks data structures, called sketches, rather than the entire blockchain. Sketches are common data structures used in computer systems and blockchain networks, to allow compact data representation while supporting efficient executions of particular queries. We study how sketches can be used to detect suspicious accounts or time periods without the need to maintain or go through the entire blockchain data. We design solutions for the major known attacks and conduct experiments to evaluate them based on real Ethereum data. We compare the accuracy, run-time and memory usage of our algorithms with traditional detection algorithms relying on the complete blockchain data. Our results indicate that sketch-based anomaly detection methods can provide a practical scalable solution for detecting anomalies in blockchain networks.
Smart contracts based on the Ethereum blockchain network have attracted attention from finance, media, and academic domains. As a result, smart contracts have been targeted by cyber attackers for the purpose of cryptocurrency theft. The smart contract honeypot is a commonly used attack method. An attacker who makes a honeypot lures other weak attackers who target vulnerable contracts by seeming to have exploitable flaws. The honeypot attacker then steals cryptocurrency from the weak attackers using a hidden trap. In this paper, we propose a machine-learning model that can detect such honeypots with high performance and prevent theft before it occurs. We use a term-frequency inverse document-frequency method to extract feature words and word2vec to learn distributed representations for the Solidity bytecode. As a result, we achieved higher PR-AUC scores in honeypot detection compared with previous efforts. Based on this, we demonstrate that the smart contract code contains useful information for honeypot detection. Furthermore, our proposed method works without using features that become available after theft. Hence, the method enables us to predict incidents and reduce the number of honeypot victims.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Currently, the blockchain technology has been widely applied to various industries, and has attracted wide attention. However, because of its unique anonymity, digital currency has become a haven for all kinds of cyber crimes. It has been reported that Ethereum frauds provide huge profits, and pose a serious threat to the financial security of the Ethereum network. To create a desired financial environment, an effective method is urgently needed to automatically detect and identify Ethereum frauds in the governance of the Ethereum system. In view of this, this paper proposes a method for detecting Ethereum frauds by mining Ethereum-based transaction records. Specifically, web crawlers are used to capture labeled fraudulent addresses, and then a transaction network is reconstructed based on the public transaction book. Then, an amount-based network embedding algorithm is proposed to extract node features for identifying fraudulent transactions. At last, the graph convolutional network model is used to classify addresses into legal addresses and fraudulent addresses. The experimental results show that the system for detecting fraudulent transactions can achieve the accuracy of 95%, which reflects the excellent performance of the system for detecting Ethereum fraudulent transactions.
Recently, phishing scams have posed a significant threat to blockchains. Phishing detectors direct their efforts in hunting phishing addresses. Most of the detectors extract target addresses’ transaction behavior features by random walking or constructing static subgraphs. The random walking methods, unfortunately, usually miss structural information due to limited sampling sequence length, while the static subgraph methods tend to ignore temporal features lying in the evolving transaction behaviors. More importantly, their performance undergoes severe degradation when the malicious users intentionally hide phishing behaviors. To address these challenges, we propose TEGDetector, a dynamic graph classifier that learns the evolving behavior features from transaction evolution graphs (TEGs). First, we cast the transaction series into multiple time slices, capturing the target address’s transaction behaviors in different periods. Then, we provide a fast nonparametric phishing detector (FD) to narrow down the search space of suspicious addresses. Finally, TEGDetector considers both the spatial and temporal evolutions toward a complete characterization of the evolving transaction behaviors. Moreover, TEGDetector utilizes adaptively learned time coefficient to pay distinct attention to different periods, which provides several novel insights. Extensive experiments on the large-scale Ethereum transaction dataset demonstrate that the proposed method achieves state-of-the-art (SOTA) detection performance. The code of TEGDetector is open sourced at https://github.com/Seaocn/TEGDetector.
Yinxing Xue, Jiaming Ye, Wei Zhang, Jun Sun · 7 authors
Smart contract transactions are increasingly interleaved by cross-contract calls. While many tools have been developed to identify a common set of vulnerabilities, the cross-contract vulnerability is overlooked by existing tools. Cross-contract vulnerabilities are exploitable bugs that manifest in the presence of more than two interacting contracts. Existing methods are however limited to analyze a maximum of two contracts at the same time. Detecting cross-contract vulnerabilities is highly non-trivial. With multiple interacting contracts, the search space is much larger than that of a single contract. To address this problem, we present xFuzz, a machine learning guided smart contract fuzzing framework. The machine learning models are trained with novel features (e.g., word vectors and instructions) and are used to filter likely benign program paths. Comparing with existing static tools, machine learning model is proven to be more robust, avoiding directly adopting manually-defined rules in specific tools. We compare xFuzz with three state-of-the-art tools on 7,391 contracts. xFuzz detects 18 exploitable cross-contract vulnerabilities, of which 15 vulnerabilities are exposed for the first time. Furthermore, our approach is shown to be efficient in detecting non-cross-contract vulnerabilities as well -- using less than 20% time as that of other fuzzing tools, xFuzz detects twice as many vulnerabilities.
Jochen Schäfer, Christian Müller, Frederik Armknecht
Abstract Bitcoin and similar cryptocurrencies are becoming increasingly popular as a payment method in both legitimate and illegitimate online markets. Such markets usually deploy a review system that allows users to rate their purchases and help others to determine reliable vendors. Consequently, vendors are interested into accumulating as many positive reviews (likes) as possible and to make these public. However, we present an attack that exploits these publicly available information to identify cryptocurrency addresses potentially belonging to vendors. In its basic variant, it focuses on vendors that reuse their addresses. We also show an extended variant that copes with the case that addresses are used only once. We demonstrate the applicability of the attack by modeling Bitcoin transactions based on vendor reviews of two separate darknet markets and retrieve matching transactions from the blockchain. By doing so, we can identify Bitcoin addresses likely belonging to darknet market vendors.
Surfing Online Social Media (OSM) websites have become a daily activity for a large number of people worldwide. People use OSMs to satisfy their innate need to socialise, but also as a source of information or to share personal facts. Thanks to the massive success of cryptocurrencies, the blockchain technology gained popularity among researchers, giving birth to a new generation of social media. Steemit is the most well-known blockchain-based social media, and it is based on the public blockchain Steem. Steemit employs Steem as data storage, and to implement a rewarding mechanism that grants cryptocurrency to pieces of content that are considered relevant by the users. Steem represents the first experiment that integrates OSMs and an economic rewarding system on the same platform, and in this paper, we inspect the interactions among the users from a community perspective. We apply two community detection algorithms on five graphs that model just as many facets of the Steem blockchain and test the detected structure against three measures for community structure evaluation. Findings show that communities tend to be very large, index of how much users are encouraged to interact as much as possible, and in particular, in the monetary graph, we detect a large number of the block producers of Steem.
JT Hamrick, Farhang Rouhi, Arghya Mukherjee, Marie Vasek · 6 authors
As the number of cryptocurrencies has exploded in recent years, so too has the fraud. One popular strategy is when actors promote coordinated purchases of coins in hopes of temporarily driving up prices. Prior work investigating such pump and dump schemes has focused on the immediate impact to prices following pump signals, which were largely interpreted as following the same strategy. The reality, as with most cybercrimes, is that the operators of the schemes try out a much more heterogeneous mix of tactics. From a population of 12,252 pump signals observed between July 2017 and January 2019, we identify and examine 3,683 so-called target-based pump signals that announce promoted coins alongside buy and sell targets, but without a coordinated purchase time. We develop a strategy to measure the success of target pumps over longer time horizons. We find that around half of these pumps reach at least one of their sell targets, and that reaching their peak price often takes days, as opposed to the seconds or minutes required in pumps studied previously. We also examine the various groups promoting coins and present evidence that groups try a variety of distinct strategies and experience varying success. We find that the most successful groups promote many coins and issue many pumps, but not for the same coins. As decentralized finance becomes more popular, a deeper understanding of price manipulation techniques like target pumps is needed to combat fraud.
On an Ethereum node, txpool (a.k.a. mempool) is a buffer storing unconfirmed transactions and controls what downstream services can see, such as mining and transaction propagation. This work presents the first security study on Ethereum txpool designs.
Sakshi Dhall, Ashutosh Dhar Dwivedi, Saibal K. Pal, Gautam Srivastava
With social media becoming the most frequently used mode of modern-day communications, the propagation of fake or vicious news through such modes of communication has emerged as a serious problem. The scope of the problem of fake or vicious news may range from rumour-mongering, with intent to defame someone, to manufacturing false opinions/trends impacting elections and stock exchanges to much more alarming and mala fide repercussions of inciting violence by bad actors, especially in sensitive law-and-order situations. Therefore, curbing fake or vicious news and identifying the source of such news to ensure strict accountability is the need of the hour. Researchers have been working in the area of using text analysis, labelling, artificial intelligence, and machine learning techniques for detecting fake news, but identifying the source or originator of such news for accountability is still a big challenge for which no concrete approach exists as of today. Also, there is another common problematic trend on social media whereby targeted vicious content goes viral to mobilize or instigate people with malicious intent to destabilize normalcy in society. In the proposed solution, we treat both problems of fake news and vicious news together. We propose a blockchain and keyed watermarking-based framework for social media/messaging platforms that will allow the integrity of the posted content as well as ensure accountability on the owner/user of the post. Intrinsic properties of blockchain-like transparency and immutability are advantageous for curbing fake or vicious news. After identification of fake or vicious news, its spread will be immediately curbed through backtracking as well as forward tracking. Also, observing transactions on the blockchain, the density and rate of forwarding of a particular original message going beyond a threshold can easily be checked, which could be identified as a possible malicious attempt to spread objectionable content. If the content is deemed dangerous or inappropriate, its spread will be curbed immediately. The use of the Raft consensus algorithm and bloXroute servers is proposed to enhance throughput and network scalability, respectively. Thus, the framework offers a proactive as well as reactive, practically feasible, and effective solution for curtailment of fake or vicious news on social media/messaging platforms. The proposed work is a framework for solving fake or vicious news spread problems on social media; the complete design specifications are beyond scope of the current work and will be addressed in the future.
Lin Jin, Shuai Hao, Yan Huang, Haining Wang · 5 authors
Domain Name System (DNS) is known to present privacy concerns. To this end, decentralized blockchains have been used to host DNS records, so that users can synchronize with the blockchain to maintain a local DNS database and resolve domain names locally. However, existing blockchain-based solutions either do not guarantee a domain name is controlled by its "true" owner; or have to resort to DNSSEC, a not yet widely adopted protocol, for verifying ownership. In this paper, we present DNSonChain, a new blockchain-based naming service compatible with DNS. It allows domain owners to claim their domain ownership on the blockchain where DNS records are hosted. The core function of DNSonChain is to validate the domain ownership in a decentralized manner. We propose a majority vote mechanism that randomly selects multiple participants (i.e., voters) in the system to vote for the authority of domain ownership. To provide resistance to attacks from fraudulent voters, DNSonChain requires two rounds of voting processes. Our security analysis shows that DNSonChain is robust against several types of security failures, able to recover from various attacks. We implemented a prototype of DNSonChain as an Ethereum decentralized application and evaluate it on an Ethereum Testnet.