The internet plays host to many different drug supply interfaces, from social media to the darknet. It is the location for communities of drug users, and is also intertwined with surveillance systems. Digitally enabled drug supply creates new kinds of user-dealer relationships, such as where dealer and user do not need to ever meet or share the same social network. The human geography and labour ecology of drug supply can be reshaped and redistributed as a consequence. The chapter outlines digital dealing in two modes: first, the reworking of existing technology platforms such as mobile phones and social media apps, and second, the design and hosting of platforms specifically designed for drug distribution, such as darknet cryptomarkets. Various innovations are involved in the use of technology and in the development of hybrid roles where tech-savvy participants mediate the drug market for others. Innovation can create new harms and new opportunities for harm reduction. We are challenged to rethink existing understandings of risk and harm in this light.
India is experiencing a sharp rise in criminal activity. This is a serious problem, as many of these crimes go unreported. Although there is an online platform for the police to store First Information Reports (FIR) and Non-Cognizable Reports (NCR), most FIRs are still written by hand. This is inefficient and can lead to errors. Additionally, the complainant must typically be at the police station to report a cognizable offense. This can be inconvenient and time-consuming, especially for victims who live in rural areas. In 2009, the Crime and Criminal Tracking Network and Systems (CCTNS) were launched as an efficient e-governance system. This system has helped to improve the reporting of crimes, but it is still a centralized system. This means that it is vulnerable to cyberattacks and can be easily shut down by a single point of failure. Therefore, a fully decentralized system is required to ensure no single point of failure and that complaints are handled safely and securely to prevent unauthorized access. This paper proposes a blockchain-based solution called BlockFIR to manage complaints against cognizable and non-cognizable offenses. Using this system, complaints can be registered by users. The police stations will be able to see complaints registered in their jurisdiction, register FIRs/NCRs accordingly, and take action on them. Through a prototype implementation using Go-Ethereum (Geth), smart contracts, and Django web server, we demonstrate the practical use of BlockFIR. We show that our system can be easily used by users, police personnel, and Higher Authorities to improve the current systems in India.
With the popularity of Non-Fungible Tokens (NFTs), NFTs have become a new target of phishing attacks, posing a significant threat to the NFT trading ecosystem. There has been growing anecdotal evidence that new means of NFT phishing attacks have emerged in Ethereum ecosystem. Most of the existing research focus on detecting phishing scam accounts for native cryptocurrency on the blockchain, but there is a lack of research in the area of phishing attacks of emerging NFTs. Although a few studies have recently started to focus on the analysis and detection of NFT phishing attacks, NFT phishing attack means are diverse and little has been done to understand these various types of NFT phishing attacks. To the best of our knowledge, we are the first to conduct case retrospective analysis and measurement study of real-world historical NFT phishing attacks on Ethereum. By manually analyzing the existing scams reported by Chainabuse, we classify NFT phishing attacks into four patterns. For each pattern, we further investigate the tricks and working principles of them. Based on 469 NFT phishing accounts collected up until October 2022 from multiple channels, we perform a measurement study of on-chain transaction data crawled from Etherscan to characterizing NFT phishing scams by analyzing the modus operandi and preferences of NFT phishing scammers, as well as economic impacts and whereabouts of stolen NFTs. We classify NFT phishing transactions into one of the four patterns by log parsing and transaction record parsing. We find these phishing accounts stole 19,514 NFTs for a total profit of 8,858.431 ETH (around 18.57 million dollars). We also observe that scammers remain highly active in the last two years and favor certain categories and series of NFTs, accompanied with signs of gang theft.
Xueyan Tang, Yuying Du, Alan Lai, Ze Zhang · 5 authors
<title>Abstract</title> This paper aims to explore the application of deep learning in smart contract vulnerabilities detection. Smart contracts are an essential part of blockchain technology and are crucial for developing decentralized applications. However, smart contract vulnerabilities can cause financial losses and system crashes. Static analysis tools are frequently used to detect vulnerabilities in smart contracts, but they often result in false positives and false negatives because of their high reliance on predefined rules and lack of semantic analysis capabilities. Furthermore, these predefined rules quickly become obsolete and fail to adapt or generalize to new data. In contrast, deep learning methods do not require predefined detection rules and can learn the features of vulnerabilities during the training process.In this paper, we introduce a solution called Lighting Cat which is based on deep learning techniques. We trained three deep learning models for detecting vulnerabilities in smart contract: Optimized-CodeBERT, Optimized-LSTM, and Optimized-CNN. To precisely extract vulnerability features, we acquired segments of vulnerable code functions to retain critical vulnerability features. Using the CodeBERT pre-training model for data preprocessing, we could capture the syntax and semantics of the code more accurately, thereby enhancing the performance of vulnerabilities detection. This is particularly significant in the inspection of Solidity Code.To demonstrate the feasibility of our proposed solution, we evaluated its performance using the SolidiFI-benchmark dataset, which consists of 9369 vulnerable contracts injected with vulnerabilities from seven different types. Experimental results showed that, among the Lighting Cat we proposed, Optimized-CodeBERT model surpassed other methods, achieving an f1-score of 93.53%.
This paper introduces the Time-Aware LSTM (T-LSTM) model to identify criminal activities involving USDT on wallet addresses within the blockchain ecosystem. The model utilizes a time-aware LSTM architecture to learn the continuous variations in node address features over different transaction time intervals. Additionally, a gating mechanism filters the influence intensity of neighboring transaction node addresses on the central node. The gating mechanism accounts for the transactional correlation strength between node addresses. Finally, a self-attention mechanism is employed to integrate node address features across various transaction timestamps, producing a comprehensive feature representation for the addresses. Experimental results demonstrate that the T-LSTM model effectively captures the dynamic feature changes of node addresses over irregular transaction intervals, outperforming traditional detection models regarding precision, recall, and F1 score on the test set.
This work compares machine learning methods using supervised, semi-supervised and unsupervised learning, to classify advertisements for cryptocurrency related investment scams found in the online forum Bitcointalk, and the social media platform Reddit. We extract more than 24.2 million posts from Bitcointalk and use Reddit’s API to collect 2,108 submissions. We train and compare several multiclass text classification approaches and use the models with highest accuracy and F-measure to identify cryptocurrency investment scam advertisements found on both platforms. We discover around five percent of all posts collected on both sites are potential scams. We then use another text classifier to identify the scam actors involved in these investment scam advertisements. We also discover the lures used within these fraudulent adverts and find the main differences in luring techniques used between Bitcointalk and Reddit. We identify that the most prevalent lure type uses the financial principle, followed by the distraction principle in Bitcointalk, and by the authority principle in Reddit. Finally, we use subreddits as communities’ proxies and compare scam advertisements within them to identify whether pensioners are being specifically targeted by cryptocurrency scam adverts. Our results show that retirement subreddits do not contain a higher number of cryptocurrency investment scam adverts compared to other investment focused subreddits.
Decentralized Finance (DeFi) ecosystem has grown rapidly in the past few years. In the DeFi ecosystem, flash loan is a novel type of uncollateralized loan with nearly negligible lending costs. Malicious attackers can easily borrow a large number of crypto assets, and utilize them to disrupt the price of crypto assets to make a profit. Many flash loan based price manipulation attacks have been reported recently, and caused immense economic losses, e.g., 30 million USD in a single attack. In this paper, we conduct an empirical study on real-world flash loan based attacks in the past two years and present three attack patterns for price manipulation attacks. Then, we propose an approach, LeiShen, to automatically detect price manipulation attacks with asset transfers. We evaluate LeiShen on the first 14,500,000 blocks in Ethereum, and detect 180 attacks with a precision of 78.9%. Among our newly-found attacks, the severest attack has caused a total loss of more than 6.1 million USD.
Aiming at the problem of insufficient technology for detecting smart contract vulnerabilities and the lack of improvement of certain detection tools, a method for the expansion and improvement of the internal module of the Mythril method is proposed. Since the technology of detecting vulnerabilities is not yet mature and there are vulnerabilities in smart contracts, vulnerability detection is particularly important. Since the Mythril tool covers the most types of vulnerabilities, its accuracy rate is also the highest. In order to ensure the effect of smart contract vulnerability detection, this paper proposes the expansion and improvement of the internal module of Mythril, which realizes the operation of automatic vulnerability analysis when performing vulnerability detection by improving the operation efficiency and simplifying the operation of smart contract opcodes while analyzing them. The comparison of experimental results shows that the proposed method is more suitable for smart contract vulnerability detection, and the detection accuracy and efficiency are improved, with an average accuracy rate of 94.07%. It performs better in vulnerability detection and provides an effective guarantee for the security and reliability of smart contracts, which has important application value and research significance.
Adi Darmawansyah, Djunaedi Djunaedi, Kristiawanto Kristiawanto
The medium of exchange can be any object that can be accepted by everyone in society in the process of exchanging goods and services. Long before knowing money, humans had made transactions using barter practices, that is, the exchange of goods and/or services for the desired goods and/or services. In the preparation of this research, a normative juridical approach where approach is carried out based on the main legal material by examining theories, concepts, legal principles, and laws and regulations related to this research. Cryptocurrency assets don't just impact people who mine or trade crypto. It turns out that anonymous platforms that run crypto are also increasingly associated with cybercrime. A recent study from Interisle Consulting Group revealed that phishing attempts related to cryptocurrencies grew 257 percent compared to last year (compared to a 61 percent increase in phishing attacks overall), especially for attacks on wallets and exchanges. The rapid development of information and communication technology makes the journey of the development of crime in the virtual and digital world (cybercrime) sophisticated and complex.
cryptocurrency is a digital or virtual currency that uses ryptography for security and operates independently of a entral bank. Its decentralized nature allows for secure and transparent transactions, making it an appealing alternative to traditional fiat currencies. Cryptocurrencies uses block chain technology, which is a distributed ledger that records all transactions on a network of computers. Bit coin was the first cryptocurrency to gain widespread attention, but today there are thousands of different crypto currencies with varying degrees of popularity and acceptance. Despite their potential benefits, cryptocurrencies are subject to volatility, regulatory uncertainty, and security risks, which have led to debates about their future role in the global economy. In this paper we are going to discuss different fraud prospects in cryptocurrency investment faces by users. Here we are listed possible scams happened in past and possibilities in future with cryptocurrency fund. Even we tried to discuss recent available detection & prevention methods for such scams. With the help of our future research perspective we are planning to provide technique to prevent such different scams and saving our valuable money.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Firuz Kamalov, Mehdi Gheisari, Yang Liu, Mohammad Reza Feylizadeh · 5 authors
The Internet of Things (IoT) has been considered in various fields in the last decade. With the increasing number of IoT devices in the community, secure, accessible, and reliable infrastructure for processing and storing computed data has become necessary. Since traditional security protocols are unsuitable for IoT devices, IoT implementation is fraught with privacy and security challenges. Thus, blockchain technology has become an effective solution to the problems of IoT security. Blockchain is an empirical data distribution and storage model involving point-to-point transmission, consensus mechanism, asymmetric encryption, smart contract, and other computer technologies. Security and privacy are becoming increasingly important in using the IoT. Therefore, this study provides a comprehensive framework for classifying security criteria based on blockchain technology. Another goal of the present study is to identify causal relationship factors for the security issue using the Fuzzy Decision-Making Trial-and-Evaluation Laboratory (FDEMATEL) approach. In order to deal with uncertainty in human judgment, fuzzy logic is considered an effective tool. The present study’s results show the proposed approach’s efficiency. Authentication (CR6), intrusion detection (CR4), and availability (CR5) were also introduced as the most effective and essential criteria, respectively.
Abstract With the widespread use of blockchain, more and more smart contracts are being deployed, and their internal logic is getting more and more sophisticated. Due to the large false positive rate and low detection accuracy of most current detection methods, which heavily rely on already established detection criteria, certain smart contracts additionally call for human secondary detection, resulting in low detection efficiency. In this study, we propose HGAT, a hierarchical graph attention network-based detection model, in order to address the aforementioned issues as well as the shortcomings of current smart contract vulnerability detection approaches. First, using Abstract Syntax Tree (AST) and Control Flow Graph, the functions in the smart contract are abstracted into code graphs (CFG). Then abstract each node in the code subgraph, extract the node features, utilize the graph attention mechanism GAT, splice the obtained vectors to form the features of each line of statements and use these features to detect smart contracts. To create test data and assess HGAT, we leverage the open-source smart contract vulnerability sample dataset. The findings of the experiment indicate that this method can identify smart contract vulnerabilities more quickly and precisely than other detection techniques.
Abstract Cryptocurrencies continue to captivate businesses and investors despite market fluctuations. The number of crypto users has risen rapidly in the last few years, and alarmingly, many appear to be unaware of the risks involved. These risks aren't confined to market hazards but include very sophisticated cybercrimes related to cryptocurrencies. As cryptocurrencies have become a breeding ground for a variety of cybercrimes, resulting in enormous financial losses, it hinders user adoption limiting the utility of blockchain technology. It has become crucial to spot such scams and devise intelligent techniques to make this technology safer for investors. This paper proposes a classification model based on the Cross Industry Standard Process for Data Mining (CRISP‐DM) framework to identify fraudulent transactions over the Ethereum blockchain. Its contribution is multi‐faceted; first, the available imbalanced Ethereum dataset has been balanced to enhance the accuracy of the classification model. Second, the correlation‐based feature selection technique has been applied to retain the best discriminating features. Thirdly, an effective machine learning‐based ensemble classification model has been adopted for the identification of fraudulent transactions over the Ethereum network. A comparative analysis of 10 machine learning techniques has been presented consisting of both individual and ensemble classifiers. Evaluated outcomes show that ensemble classifiers appear to yield better performance measures over individual classifiers, and among all, the LightGbm classifier outperformed with 99.2% accuracy. Further, extensive experiments indicate that the proposed method outperforms the state‐of‐the‐art method when applied to a similar dataset.
Lei Yu, Fengjun Zhang, Jiajia Ma, Yang Li · 6 authors
With the development of blockchain technology, security concerns have become increasingly prominent in recent years. Money laundering through blockchain has been found to generate a significant amount of money and has become a serious threat. Towards money laundering detection in Bitcoin, conventional methods heavily rely on fixed expert rules, leading to low accuracy and poor scalability. Graph convolutional network approaches have improved this issue, but they fail to distinguish the importance of surrounding transactions and the structural information of different transactions. To solve above problems, we propose an approach to detect money laundering on blockchain by mining its transaction records, named AEtransGAT. First, we use a novel approach called transGat as an encoder to determine the significance of surrounding transactions by considering the transaction amount values of transaction flows. The original features and the features after graph embedding are combined to address the issue of feature distortion. Second, we deploy the graph autoencoder as the decoder to learn the overall structural information of different transactions, and the concatenated embedding is used to output the classification results as the detector. Finally, we propose our model based on mutual learning in this task which takes the advantages of both transactions classification loss and structure reconstruction loss. We validate the performance of our model on the Elliptic dataset which is the only large open source dataset in Bitcoin anti-money laundering. The results show that our method outperforms current state-of-the-art methods and is linearly scalable.
This paper presents a comprehensive analysis of the cryptocurrency free giveaway scam disseminated in a new distribution channel, Twitter lists. To collect and detect the scam in this channel, unlike existing scam detection systems that rely on manual effort, this paper develops a fully automated scam detection system, \textit{GiveawayScamHunter}, to continuously collect lists from Twitter and utilize a Nature-Language-Processing (NLP) model to automatically detect the free giveaway scam and extract the scam cryptocurrency address. By running \textit{GiveawayScamHunter} from June 2022 to June 2023, we detected 95,111 free giveaway scam lists on Twitter that were created by thousands of Twitter accounts. Through analyzing the list creator accounts, our work reveals that scammers have combined different strategies to spread the scam, including compromising popular accounts and creating spam accounts on Twitter. Our analysis result shows that 43.9\% of spam accounts still remain active as of this writing. Furthermore, we collected 327 free giveaway domains and 121 new scam cryptocurrency addresses. By tracking the transactions of the scam cryptocurrency addresses, this work uncovers that over 365 victims have been attacked by the scam, resulting in an estimated financial loss of 872K USD. Overall, this work sheds light on the tactics, scale, and impact of free giveaway scams disseminated on Twitter lists, emphasizing the urgent need for effective detection and prevention mechanisms to protect social media users from such fraudulent activity.
Matteo Loporchio, Anna Bernasconi, Damiano Di Francesco Maesa, Laura Ricci
Abstract In the Bitcoin protocol, dust refers to small amounts of currency that are lower than the fee required to spend them in a transaction. Although “economically irrational”, dust is commonly used for achieving unconventional side effects, rather than exchanging value. For instance, dust might be linked to on-chain services or to malicious activity, such as dust attacks attempting to break users’ pseudonymity. To study this phenomenon, this paper presents an in depth analysis of Bitcoin transactions involving dust, showing how dust is created and consumed. We identify the top dust creators and consumers and discuss how consumption has evolved over time. Finally, we use the data to identify transactions suspected of being part of dust attacks and quantify their impact on address deanonymization. Our results show that dust is mainly related to on-chain betting services. Transactions likely to be part of dust attacks are a minority of dust creating transactions but, despite their relatively low number, they have a disproportionately high effect on helping attackers to break address pseudonymity.
Purpose This paper aims to explain the implications of the impending establishment of national blockchain infrastructure by governments around the world, and how these structures can be integrated with existing legislation and assist in the prevention of financial crime. Design/methodology/approach The methodology used is a literature review and analysis of progress being made to establish national blockchain infrastructure. It provides a discussion of the connection between blockchain and financial crime, and how this infrastructure will interact with existing regulatory frameworks, and particularly, financial crime legislation. Findings This paper documents financial crime risks posed by digital currencies and smart contracts and the role that national blockchain infrastructure can potentially play in mitigating these risks. It highlights the need for governments to devote resources to developing this infrastructure and associated regulatory frameworks. Originality/value There are few, if any, academic papers in the financial crime, or wider literature, that have examined the potential for national blockchain infrastructures prevent financial crime, including the implications for existing regulation in the field.
Her geçen gün önemi daha belirgin hale gelen blockchain, açık anahtarlı şifreleme ile güvence altına alınan ve blok dizisinden oluşan sayı dizisidir. Günümüzde blockchain tabanlı oluşturulan yüzlerce kripto para birimi bulunmaktadır. Yakın zamanda kripto para piyasanın gelişmesi ile birlikte Non-Fungible Token olarak ifade edilen dijital bir varlık dikkatleri üzerine çekmiştir. Benzersiz kimlikleri olan NFT, koleksiyon, sanat eserleri, sanal dünyadaki nesneler ve diğer oyunlardan dijitalleştirilmiş karakterlerdir. Non-Fungible Tokenler yatırımcılara bir nesneyi satın alma veya satma yolunu tanıtırlar. Ayrıca bu varlıklar çevrimiçi olarak, çoğunlukla kripto para birimiyle işlem görürler ve bir blok zincirindeki akıllı sözleşmeler içinde kodlanırlar. Bu perspektifte araştırmanın amacı, 2020-2022 yakın dönemde "Non-Fungible Token" ve kısaltması "NFT" terimlerinin Google aranma sıklıklarını ‘finans’ alt segmentinde inceleyerek, ABD ve Türkiye ülkelerinde karşılaştırmalı olarak tespit etmektedir. Araştırma sonucunda elde edilen Google Trends verileri, kullanıcı/yatırımcının "Non-Fungible Token"den ziyade baş harflerinden oluşan "NFT" kavramını 2021 yılı itibarıyla daha çok bildiğini işaret etmiştir. Bu kapsamda araştırma güçlü bir pazar haline gelen ve yatırımcının bir anda dikkatini çekmeyi başaran "Non-Fungible Token" ve kısaltması "NFT" kavramlarının finans literatüründeki bilinirlilik düzeyini belirleme aşamasında ulusal literatüre özgün değer katmaktadır.
Blockchain is the latest technology, which is now widely used in various fields due to its benefits such as higher tamper-proof security. Each transaction is stored in an immutable distributed ledger on each blockchain node. Criminal records are confidential and should be kept safe from hackers and intruders. Law enforcement agencies or investigative agencies can use these security recordings to analyze and detect criminal activity. Due to the lack of a global online system, Investigative agencies are having difficulty locating and researching the past of anonymous foreign criminal who is committing crimes abroad. This problem will only be solved if investigative agencies have a common global criminal database and applications to extract information. Investigation agencies, Copes, and other organizations can access global crime data using this technology on the distributed node of the blockchain in their nation. Authorities can instantly and efficiently access global criminal histories from local blockchain databases and identify the names and details of anonymous individuals in response to any foreigners suspected of engaging in unsocial activities in their country of origin. This will reduce the time and process of gathering information from the country where the suspect is located. The system would eliminate the possibility of falsification and tamper with criminal records. Key Words: Blockchain, light, Global Criminal, Criminal information
The immutable and trustable characteristics of blockchain enable smart contracts to be applied in various fields. Unfortunately, smart contracts are subject to various vulnerabilities, which are frequently exploited by attackers, causing financial damage to users.In this paper, we study the problem of vulnerable smart contract function locating. We construct a novel Multi-Relational Nested contract Graph (MRNG) to better characterize the rich syntactic and semantic information in the smart contract code, including the relationships between data and instructions. An MRNG represents a smart contract, where each node represents a function in the smart contract and each edge describes the calling relationship between the functions. In addition, we create a Multi-Relational Function Graph (MRFG) for each function, which characterizes the corresponding function code. That is, each function is characterized as an MRFG, which corresponds to a node in the MRNG. Each MRFG uses different types of edges to represent the different control and data relationships between nodes within a function. We also propose a Multi-Relational Nested Graph Convolutional Network (MRN-GCN) to process the MRNG. MRN-GCN first extracts and aggregates features from each MRFG, using the edge-enhanced graph convolution network and self-attention mechanism. The extracted feature vector is then assigned to the corresponding node in the MRNG to obtain a new Featured Contract Graph (FCG) for the smart contract. Graph convolution is used to further extract features from the FCG. Finally, a feed forward network with a Sigmoid function is used to locate the vulnerable functions. Experimental results on the real-world smart contract datasets show that model MRN-GCN can effectively improve the accuracy, precision, recall and F1-score performance of vulnerable smart contract function locating.
Alexander Shevtsov, Despoina Antonakaki, Ioannis Lamprou, Ioannis Kontogiorgakis · 6 authors
On 24 February 2022, Russia invaded Ukraine, starting what is now known as the Russo-Ukrainian War, initiating an online discourse on social media. Twitter as one of the most popular SNs, with an open and democratic character, enables a transparent discussion among its large user base. Unfortunately, this often leads to Twitter's policy violations, propaganda, abusive actions, civil integrity violation, and consequently to user accounts' suspension and deletion. This study focuses on the Twitter suspension mechanism and the analysis of shared content and features of the user accounts that may lead to this. Toward this goal, we have obtained a dataset containing 107.7M tweets, originating from 9.8 million users, using Twitter API. We extract the categories of shared content of the suspended accounts and explain their characteristics, through the extraction of text embeddings in junction with cosine similarity clustering. Our results reveal scam campaigns taking advantage of trending topics regarding the Russia-Ukrainian conflict for Bitcoin and Ethereum fraud, spam, and advertisement campaigns. Additionally, we apply a machine learning methodology including a SHapley Additive explainability model to understand and explain how user accounts get suspended.
Due to the rapid development of blockchain, security issues caused by smart contract vulnerabilities are receiving increasingly widespread attention. Unfortunately, traditional smart contract vulnerability detection methods rely heavily on expert knowledge and elaborate rules, while neural network-based vulnerability detection methods have not yet achieved satisfactory accuracy either. In this paper, we propose a novel vulnerability detection method for smart contracts called VULDET. We first construct a contract graph based on the structure of the smart contract source code and combine security domain knowledge to attach additional features to nodes in the graph that are closely associated with vulnerabilities to highlight key nodes, and finally use graph attention networks for contract vulnerability detection. We apply VULDET to reentrancy vulnerability as well as timestamp dependency vulnerability detection and conduct extensive experiments, and the results show that our approach has significant advantages over existing methods.
Research in Ethereum BlockChain has resulted in the growth of several tools for vulnerability detection. As a typical example, the Vandal tool detects selfdestruct vulnerability.Even though Vandal is a static analysis tool, its approach is also employed by several dynamic analysis tools. There is a need for a different approach for dynamic analysis tools to detect vulnerabilities such as selfdestruct so that dynamic analysis tools can maintain their individuality. This paper uses dynamic analysis to detect the selfdestruct (or self-destructive) vulnerability. Our work balances the developmental pace of the static and dynamic analysis approaches. The novelty of the work presented in this paper is that we use an Ether transfer-based approach and name it as "terminating transfer" to detect the selfdestruct vulnerability using the tool developed by us called "TechyTech".
Over the past few years, Algorand has emerged as one of the prominent pure proof-of-stake (PPoS) blockchains, with a rapidly growing number of accounts and transactions. Despite its increasing significance, there is limited knowledge about its smart contracts, transaction activities, and other features. This paper presents the first comprehensive study of Algorand using graph analysis to investigate the creation of entities, payment of Algos, invocation of smart contracts, and transfer of assets over time. By collecting and analyzing external transaction data, we have constructed graphs to gain insights into the behavior of the blockchain. Our research has uncovered novel observations and insights by performing relevant graph analysis, and we have also proposed a solution to one security problem based on these graphs.