Abstract Directed Graph based models of a blockchain that capture accounts as nodes and transactions as edges, evolve over time. This temporal nature of a blockchain model enables us to understand the behavior (malicious or benign) of the accounts. Predictive classification of accounts as malicious or benign could help users of the permissionless blockchain platforms to operate in a secure manner. Motivated by this, we introduce temporal features such as burst and attractiveness on top of several already used graph properties such as the node degree and clustering coefficient. Using identified features, we train various Machine Learning (ML) models and identify the algorithm that performs the best in detecting malicious accounts. We then study the behavior of the accounts over different temporal granularities of the dataset before assigning them malicious tags. For the Ethereum blockchain, we identify that for the entire dataset—the ExtraTreesClassifier performs the best among supervised ML algorithms. On the other hand, using cosine similarity on top of the results provided by unsupervised ML algorithms such as K-Means on the entire dataset, we were able to detect 554 more suspicious accounts. Further, using behavior change analysis for accounts, we identify 814 unique suspicious accounts across different temporal granularities.
Ransomware is malicious software (malware) that blocks access to someone’s computer system or files on the system and subsequently demands a ransom to be paid for unlocking the computer or files. Ransomware is considered one of the main threats in cybercrime today. Cryptoware is a specific type of ransomware, which encrypts files on computer systems. The ransom is often demanded in bitcoins. Based on desk research, a series of interviews, and the investigation of several police files, this paper investigates the modi operandi in which cybercriminals use ransomware and cryptoware to make profits and how they launder these profits. Two models, based on the payment of the ransom via vouchers and via bitcoins respectively, are identified and described. These methods allow criminals to launder profits in relative anonymity and prevent the seizure of the illegally obtained money.
In recent years, vaccine incidents occurred around the world, which endangers people’s lives. In the technical respect, these incidents are partially due to the fact that existing vaccine management systems are distributively managed by different entities in the vaccine supply chain. This architecture makes it relatively easy to modify or even delete the vaccine circulation data maliciously, which makes tracing problematic vaccine hard and identifying the responsibility for a vaccine accident hard. To solve these issues, this article presents a blockchain-based solution to protect the whole process of vaccine circulation. We first propose a model to supervise the vaccine circulation process by incorporating existing regulatory practices. Then, we propose a blockchain-based tracing system to implement this model. The proposed system takes the blockchain as a global, unique, and verifiable database to store all the circulation data. Through data insertions and queries on the global and unique database, the proposed system achieves the protection of vaccine circulation. We also implement a proof-of-concept prototype of the proposed system. Experimental results confirm that the proposed system is beneficial.
Liang Chen, Jiaying Peng, Yang Liu, Jintang Li · 6 authors
Blockchain has attracted an increasing amount of researches, and there are lots of refreshing implementations in different fields. Cryptocurrency as its representative implementation, suffers the economic loss due to phishing scams. In our work, accounts and transactions are treated as nodes and edges, thus detection of phishing accounts can be modeled as a node classification problem. Correspondingly, we propose a detecting method based on Graph Convolutional Network and autoencoder to precisely distinguish phishing accounts. Experiments on different large-scale real-world datasets from Ethereum show that our proposed model consistently performs promising results compared with related methods.
Bithin Alangot, Daniël Reijsbergen, Sarad Venugopalan, Paweł Szałachowski
Clients of permissionless blockchain systems, like Bitcoin, rely on an underlying peer-to-peer network to send and receive transactions. It is critical that a client is connected to at least one honest peer, as otherwise the client can be convinced to accept a maliciously forked view of the blockchain. In such an eclipse attack, the client is unable to reliably distinguish the canonical view of the blockchain from the view provided by the attacker. The consequences of this can be catastrophic if the client makes business decisions based on a distorted view of the blockchain transactions. In this paper, we investigate the design space and propose two approaches for Bitcoin clients to detect whether an eclipse attack against them is ongoing. Each approach chooses a different trade-off between average attack detection time and network load. The first scheme is based on the detection of suspicious block timestamps. The second scheme allows blockchain clients to utilize their natural connections to the Internet (i.e., standard web activity) to gossip about their blockchain views with contacted servers and their other clients. Our proposals improve upon previously proposed eclipse attack countermeasures without introducing any dedicated infrastructure or changes to the Bitcoin protocol and network, and we discuss an implementation. We demonstrate the effectiveness of the gossip-based schemes through rigorous analysis using original Internet traffic traces and real-world deployment. The results indicate that our protocol incurs a negligible overhead and detects eclipse attacks rapidly with high probability, and is well-suited for practical deployment.
Bithin Alangot, Daniël Reijsbergen, Sarad Venugopalan, Paweł Szałachowski
Clients of permissionless blockchain systems, like Bitcoin, rely on an\nunderlying peer-to-peer network to send and receive transactions. It is\ncritical that a client is connected to at least one honest peer, as otherwise\nthe client can be convinced to accept a maliciously forked view of the\nblockchain. In such an eclipse attack, the client is unable to reliably\ndistinguish the canonical view of the blockchain from the view provided by the\nattacker. The consequences of this can be catastrophic if the client makes\nbusiness decisions based on a distorted view of the blockchain transactions. In\nthis paper, we investigate the design space and propose two approaches for\nBitcoin clients to detect whether an eclipse attack against them is ongoing.\nEach approach chooses a different trade-off between average attack detection\ntime and network load. The first scheme is based on the detection of suspicious\nblock timestamps. The second scheme allows blockchain clients to utilize their\nnatural connections to the Internet (i.e., standard web activity) to gossip\nabout their blockchain views with contacted servers and their other clients.\nOur proposals improve upon previously proposed eclipse attack countermeasures\nwithout introducing any dedicated infrastructure or changes to the Bitcoin\nprotocol and network, and we discuss an implementation. We demonstrate the\neffectiveness of the gossip-based schemes through rigorous analysis using\noriginal Internet traffic traces and real-world deployment. The results\nindicate that our protocol incurs a negligible overhead and detects eclipse\nattacks rapidly with high probability, and is well-suited for practical\ndeployment.\n
Smart Cities enclose various industries, sectors, and components. A Smart City application benefits from the data collected using a tremendous number of sensors serving to establish an accurate corporative decision. However, the communication between different components and application of a Smart City exhort security measures as it faces critical security attacks and integrity issues. To medicate this dilemma, researches have used the assistant of Blockchain to secure Smart Cities and improve its decision’s accuracy. Yet, with the recent development of Quantum Computers, Quantum-based algorithms are certainly menacing the security of classical encryption together with Blockchain itself. Thus, threaten the insurance of Blockchain-based application including critical applications such as Smart Cities. In this paper, we demonstrate the Blockchain-based Smart Cities’ framework and we present an understanding overview that encloses the menaces of a Quantum computer on Blockchain and Blockchain-based applications essentially Smart City. We explain as well some Post-Quantum solutions concerning the security of Blockchain-based Smart City.
The anonymity of blockchain has caused Ponzi schemes to be transferred to smart contract platforms by scammers. These Ponzi schemes wearing the mask of smart contracts caused huge losses to people, which makes the detection of smart Ponzi schemes attract people's attention. Recent methods mainly focus on machine learning technology to enable automatic detection for smart Poniz schemes. However, there are some problems with their methods. Firstly, the gradient boosting algorithm in machine learning they used have the problem of prediction shift due to target leakage when processing category features and calculating gradient estimates. Secondly, they ignored the imbalance and repetitiveness of Ponzi schemes on smart contract platforms. These problems can directly lead to model overfitting and affect the generalization ability of trained models. This paper proposes a novel Ponzi schemes detection method on smart contract platform for blockchain. Our method addresses the above issues with the following strategies. Firstly, we leverage ordered target statistic (TS) to process the category features of smart contract. Secondly, we solve the imbalance of dataset through a data augmentation method. Thirdly, with the idea of ordered boosting algorithm, we train a PonziTect model to fight prediction shift caused by target leakage. Based on the above ideas, the experimental results fully manifest the effectiveness and reliability of our model in detecting smart Ponzi schemes on the blockchain. Specifically, our model achieves 98% F-score on the real-world dataset, which significantly outperforms the existing methods. Using our method, we estimate that there are about 532 Ponzi schemes on Ethereum.
Zakwan Jaroucheh, Mohamad Alissa, William J. Buchanan, Xiaodong Liu
The growing trend of sharing news/contents, through social media platforms and the World Wide Web has been seen to impact our perception of the truth, altering our views about politics, economics, relationships, needs and wants. This is because of the growing spread of misinformation and disinformation intentionally or unintentionally by individuals and organizations. This trend has grave political, social, ethical, and privacy implications for society due to 1) the rapid developments in the field of Machine Learning (ML) and Deep Learning (DL) algorithms in creating realistic-looking yet fake digital content (such as text, images, and videos), 2) the ability to customize the content feeds and to create a polarized so-called "filter-bubbles" leveraging the availability of the big-data. Therefore, there is an ethical need to combat the flow of fake content. This paper attempts to resolves some of the aspects of this combat by presenting a high-level overview of TRUSTD, a blockchain and collective signature based ecosystem to help content creators in getting their content backed by the community, and to help users judge on the credibility and correctness of these contents.
Smart contracts vulnerability auditing is vitally critical to ensure transaction execution in normal on blockchain. The current data-driven approaches normally tokenize smart contracts into a series of sequences according to only one tokenization standard for vulnerability detection purpose, resulting some of the semantic contexts could not be reflected within restricted sequence length. To address this limitation, we generate sequences from smart contracts in three tokenization standards for which we utilize n-gram language model to capture semantic contexts respectively, and finally exploiting our effective combination strategy of Intersection or Union to integrate the audited results from multiple semantic contexts. In order to evaluate the proposed approach, we applied it on over 7200 Ethereum smart contract samples. Experimental result shows our proposed method is capable of detecting vulnerabilities and competitive with the baseline in test sets, with improved precision of over 44% when Intersection is applied in their results, as well as improved Recall measure up by over 300% and F-measure up by 220% when Union is applied. Our proposed method for smart contract vulnerability detection, an important tool for developing quality decentralized software applications, is able to analyze multiple semantic contexts and successfully detects more true vulnerabilities with high precision, outperforming that of the baseline approaches.
In recent years, blockchain technology has created a new cryptocurrency world and has attracted a lot of attention. It also is rampant with various scams. For example, phishing scams have grabbed a lot of money and has become an important threat to users' financial security in the blockchain ecosystem. To help deal with this issue, this paper proposes a systematic approach to detect phishing accounts based on blockchain transactions and take Ethereum as an example to verify its effectiveness. Specifically, we propose a graph-based cascade feature extraction method based on transaction records and a lightGBM-based Dual-sampling Ensemble algorithm to build the identification model. Extensive experiments show that the proposed algorithm can effectively identify phishing scams.
The current pandemic situation has made us realize how fake news can have detrimental impacts on society. Although we can't ignore the benefits of social media, we surely can't overlook the misuse of these mediums to spread rumors and fake articles. These posts/ articles/ messages can be commonly called "Fake News" and they not only create misinformation but also create panic among the public. Blockchain is a disruptive technology that is being used to make innovative solutions in the sectors of food, fashion, supply chain, and banking. The decentralized and transparent nature of blockchain can not only be used in the above sectors but it can also be used to tackle the problem of fake news. Several solutions have been proposed to combat the problem using blockchain. This paper will propose one such solution to combat the fake news problem that can be used by all the news agencies. The system is called the "News Tracking and Tracing System " and is built using Ethereum.
Product serialization aims to allocate unique serial numbers to products in a supply chain. The security challenges to product serialization are: • Valid serial numbers can be stolen and used to label fake products. Thus uniqueness of a serial number should be verifiable at any stage of its lifecycle in a supply chain. • A planned change of custody of a product in distribution can be corrupted by a few intimidatory nodes. Compliance with the planned change of custody should be verifiable. • The manufacturer and the consumer should be able to verify that perishable food products with expired shelf life are discarded. In this paper, we use blockchains to develop a product serialization method that solves the above security issues in a multi-party perishable good supply chain. Blockchains can revolutionize security and transparency in supply chains by providing a secure data-sharing platform in a multi-party environment. Although blockchains can provide a secure data storage of change of custody events of products in a supply chain, a high volume of such events poses scalability problems for blockchains. In this paper, we solve the product serialization problem using blockchain offline channels. Our solution significantly reduces the number of transactions needed to be recorded in the blockchain. We propose a secure serialization protocol to verify the authenticity of serial numbers despite not frequently engaging with the blockchain.
Ransomware is malicious software (malware) that blocks access to someone’s computer system or files on the system and subsequently demands a ransom to be paid for unlocking the computer or files. Ransomware is considered one of the main threats in cybercrime today. Cryptoware is a specific type of ransomware, which encrypts files on computer systems. The ransom is often demanded in bitcoins. Based on desk research, a series of interviews, and the investigation of several police files, this paper investigates the modi operandi in which cybercriminals use ransomware and cryptoware to make profits and how they launder these profits. Two models, based on the payment of the ransom via vouchers and via bitcoins respectively, are identified and described. These methods allow criminals to launder profits in relative anonymity and prevent the seizure of the illegally obtained money.
Cryptocurrency has seen an explosive growth in recent years, thanks to the evolvement of blockchain technology and its economic ecosystem. Besides Bitcoin, thousands of cryptocurrencies have been distributed on blockchains, while hundreds of cryptocurrency exchanges are emerging to facilitate the trading of digital assets. At the same time, it also attracts the attentions of attackers. Fake deposit, as one of the most representative attacks (vulnerabilities) related to exchanges and tokens, has been frequently observed in the blockchain ecosystem, causing large financial losses. However, besides a few security reports, our community lacks of the understanding of this vulnerability, for example its scale and the impacts. In this paper, we take the first step to demystify the fake deposit vulnerability. Based on the essential patterns we have summarized, we implement DEPOSafe, an automated tool to detect and verify (exploit) the fake deposit vulnerability in ERC-20 smart contracts. DEPOSafe incorporates several key techniques including symbolic execution based static analysis and behavior modeling based dynamic verification. By applying DEPOSafe to 176,000 ERC-20 smart contracts, we have identified over 7,000 vulnerable contracts that may suffer from two types of attacks. Our findings demonstrate the urgency to identify and prevent the fake deposit vulnerability.
Çalışmanın amacı, günümüz dijital çağında geleneksel muhasebe süreçlerinden teknolojik süreçlere geçişte blockchain (blok zinciri) teknolojisini muhasebe ve mali kontroller açısından incelemektir. Bu bağlamda çalışma, blok zinciri uygulamalarına, yol haritasına, kurumsal uygulamalarda blok zinciri etkinliğine ve blok zinciri temelli muhasebe sistemine odaklanmaktadır. Blok zinciri temelli üç taraflı muhasebe sistemi ile birlikte, Kurumsal Kaynak Planlama (ERP) sistemlerinin blok zinciri ile entegrasyonu ve teknolojinin bir diğer yönü olan kod ile yazılmış, belirli bir blok zinciri katmanına gömülü akıllı sözleşmeler detaylı olarak ele alınmıştır. Blok zinciri temelli üç taraflı muhasebe sistemi; çift girişli sistemin üzerine kurulacak, içine gömülü üçüncü bir blok zinciri katmanı olacak hem kurum içinde hem de dış taraflarla yapılan işlemleri kaydedecek bir sistem önermektedir.
EOSIO has become one of the most popular blockchain platforms since its mainnet launch in June 2018. In contrast to the traditional PoW-based systems (e.g., Bitcoin and Ethereum), which are limited by low throughput, EOSIO is the first high throughput Delegated Proof of Stake system that has been widely adopted by many decentralized applications. Although EOSIO has millions of accounts and billions of transactions, little is known about its ecosystem, especially related to security and fraud. In this paper, we perform a large-scale measurement study of the EOSIO blockchain and its associated DApps. We gather a large-scale dataset of EOSIO and characterize activities including money transfers, account creation and contract invocation. Using our insights, we then develop techniques to automatically detect bots and fraudulent activity. We discover thousands of bot accounts (over 30% of the accounts in the platform) and a number of real-world attacks (301 attack accounts). By the time of our study, 80 attack accounts we identified have been confirmed by DApp teams, causing 828,824 EOS tokens losses (roughly \$2.6 million) in total.
As the pioneer of blockchain technology, Bitcoin is the most popular cryptocurrency to date. Given its dramatic price spikes (and crashes) along with the never-ending news from SEC regulations to security breaches, there seems to be a lack of understanding about the dynamics of cryptocurrencies. These dynamics are believed to be affected by various political, security, financial, and regulatory events. In this paper, we present an efficient framework for holistic analysis of cryptocurrency fluctuations by introducing the Impact-Score metric to distinguish event-induced changes from normal variations. We have applied our framework to 16 major worldwide events and the Bitcoin blockchain network (defined as Bitcoin transaction and users, blockchain data, and memory pool data) from 2016-2018. The results show that a majority of the events are correlated with substantial network changes. We observed roughly generalizable correlations between event types (e.g. financial events) and sub-structures of the Bitcoin blockchain network. Subgroups of these events have strongly consistent temporal impacts on specific facets (e.g. activity or fees) of the Bitcoin ecosystem. Furthermore, we demonstrate the robustness of our process by correlating a majority of spikes in network/subnetwork change with major events.
Marsha Chikita Intania Putri, Parman Sukarno, Aulia Arif Wardana
Authentication is a method for securing an account by verifying the user identity by inputting email with a password. Two factor authentications is an authentication system that combines the first-factor authentication with the second factor. General two factor authentication by entering an email or username with a password are similar. However, two factor authentication requires additional information that must be inputted by the user. Additional information can be in the form of tokens or one-time passwords (OTP). Two factor authentications generally still uses third-party services to generate token or OTP still have vulnerable because can attacked from tokens steal through MITM and found that the generated tokens with the same value. Therefore, we propose a two-factor authentication framework based on ethereum blockchain with dApp as token generation system. Firstly, outcome from the analysis of the system, next succeeded in creating a two-factor authentication system without using third-parties. Second, token system generate up to 3164 different tokens in one second and has been collisions tested. Third, security method to protect token from MITM attack. The attacker unable to get access caused all the checking are done by dApp user authentication.
Jiarui Zhang, Yukun Cheng, Xiaotie Deng, Bo Wang · 7 authors
As one of the fastest-growing applications in the Peer-to-Peer (P2P) network, the development of blockchain technology is accompanied by different attacks. Those include whitewashing, free-riding, and distributed denial of service (DDoS) attacks, particularly because of features such as anonymity, distributed, permissionless in the blockchain network. One popular of them is spam transactions. Although the blockchain protocol requires each node to verify all received transactions, many nodes choose to forward transactions without verification to conserve their computational power, as there is no punishment for such a shirking. And it makes the blockchain vulnerable to the spreading of spam transactions over the network and creates extra burdens for all nodes in the network. We propose a reputation mechanism for the blockchain system to tackle this problem: Each node will locally compute reputations of its neighbors, and decide the probability to verify a received transaction based on the reputation value of the transaction sender. In turn, its neighbors will have an incentive to conduct verification to keep its reputation high. Subsequently, spam transactions can be blocked before reaching the miners. We have conducted a series of simulations, which clearly demonstrate the advantage of our reputation mechanism.
The blockchain technology firstly presented by Haber and Stornetta in the year 1990, and first time blockchain technology used in Bitcoin by Satoshi Nakamoto in 2008. The blockchain technology is truly decentralized technology. In blockchain technology, every block has consisted three main parts that is data, hash block, and the previous hash block. Hash is controls the uniqueness of each block and it is unique for each block. Each block also contains the hash of the previous block; thus, the blocks are connected to each other. A blockchain can divided into three categories public blockchain, consortium blockchain and private blockchain. The proposed paper provided the comparative and analytical review on the blockchain consensus algorithms.
Intrusion Detection System is a well-known term in the domain of Network and Information Security. It's one of the important components of the Network and Information Security infrastructure. Host Intrusion Detection System (HIDS) helps to detect unauthorized use, abnormal and malicious activities on the host, whereas Network Intrusion Detection System (NIDS) helps to detect attacks and intrusion on networks. Various researchers are actively working on different approaches to improving the IDS performance and many improvements have been achieved. However, development in many other technologies and newly emerging techniques always opens the doors of opportunity to add a sharp edge to IDS and to make it more robust and reliable. This paper proposes the development of Distributed Intrusion Detection System (DIDS) using emerging and promising technologies like Blockchain upon a stable platform like cloud infrastructure.
Blockchain is a promising technology and is becoming predominant for solving many problems in the field related to security under the control of both public and private sectors. Blockchain is gaining popularity within the domain of charity. Due to lack of transparency in the transactions involved in Donations the donor(s) are not able to know whether their donations are being utilized properly, which has made people lose trust in Charities. The paper proposes a Blockchain based Decentralized Donation tracking system built on Ethereum Blockchain which will provide full transparency, accountability and direct reach to the intended recipients.