Smart contracts running on public blockchains are permissionless and decentralized, attracting both developers and malicious participants. Ethereum, the world’s largest decentralized application platform on which more than 40 million smart contracts are running, is frequently challenged by smart contract vulnerabilities. What’s worse, since the homogeneity of a wide range of smart contracts and the increase in inter-contract dependencies, a vulnerability in a certain smart contract could affect a large number of other contracts in Ethereum. However, little is known about how vulnerable contracts affect other on-chain contracts and which contracts can be affected. Thus, we first present the contract dependency graph (CDG) to perform a vulnerability analysis for Ethereum smart contracts, where CDG characterizes inter-contract dependencies formed by DELEGATECALL-type internal transaction in Ethereum. Then, three generic definitions of security violations against CDG are given for finding respective potential victim contracts affected by different types of vulnerable contracts. Further, we construct the CDG with 195,247 smart contracts active in the latest blocks of the Ethereum and verify the above security violations against CDG by detecting three representative known vulnerabilities. Compared to previous large-scale vulnerability analysis, our analysis scheme marks potential victim contracts that can be affected by different types of vulnerable contracts, and identify their possible risks based on the type of security violation actually occurring. The analysis results show that the proportion of potential victim contracts reaches 14.7%, far more than that of corresponding vulnerable contracts (less than 0.02%) in CDG.
Bitcoin is the most common cryptocurrency involved in cyber scams. Cybercriminals often utilize pseudonymity and privacy protection mechanism associated with Bitcoin transactions to make their scams virtually untraceable. The Ponzi scheme has attracted particularly significant attention among the Bitcoin fraudulent activities. This paper considers a multi-class classification problem to determine whether a transaction is involved in Ponzi schemes or other cyber scams, or is a non-scam transaction. We design a specifically designed crawler to collect data and propose a novel Attention-based Long Short-Term Memory (A-LSTM) method for the classification problem. The experimental results show that the proposed model has better efficiency and accuracy than existing approaches, including Random Forest, Extra Trees, Gradient Boosting, and classical LSTM. With correctly identified scam features, our proposed A-LSTM achieves an F1-score over 82% for the original data and outperforms the existing approaches.
Rickard Elsen, Muhammad Rikza Nashrulloh, Ade Sutedi
Since the widespread use of cryptocurrency, blockchain technology start to be adapted in various applications. Some businesses are already adopting blockchain technology because of its advantages such as data integrity and privacy. One of them is Web 3.0. Web 3.0 puts forward data decentralization so that users can choose what data will be sent to the server. User data is provided locally with the help of a crypto wallet and the server just receives wallet info. With this mechanism, user privacy can be maintained directly by the user himself. All data will be processed at the users' end first before being sent to the server. With the new mechanism of web 3.0 and the advantages of blockchain, we build an application to authenticate students' login activities and grant roles to them based on their wallets. In this paper, we use the prototyping model as the method to build the application. We managed to utilize students’ wallet addresses as credentials. And with the help of Web3 module, we managed to decentralize the authentication process. And as a result of the successful authentication process, students can access their data based on their roles.
DNS has often been criticized for inherent design flaws, which make the system vulnerable to attack. Further, domain names are not fully controlled by users, meaning that they can easily be taken down by authorities and registrars. Due to this, there have been efforts to build a decentralized name service that gives greater control to domain owners. The Ethereum Name Service (ENS) is a major example. Yet, no existing work has systematically studied this emerging system, particularly regarding security and misbehavior. To address this gap, we present the first large-scale measurement study of ENS. Our findings suggest that ENS has shown growth during its four years' evolution. We identify several security issues, including traditional name system problems, as well as new issues introduced by the unique properties of ENS. We find that attackers are abusing the system with thousands of squatting ENS names, a number of scam blockchain addresses and indexing of malicious websites. We further develop a new record persistence attack, to find that 22,716 .eth names (3.7% of all names) are vulnerable to name hijacking. Our exploration suggests that our community should invest more effort into the detection and mitigation of issues in decentralized name services.
Ethereum is one of the popular Blockchain platform. The key component in the Ethereum Blockchain is the smart contract. Smart contracts (SC) are like normal computer programs which are written mostly in solidity high-level object-oriented programming language. Smart contracts allow completing transactions directly between two parties in the network without any middle man or mediator. Modification of the smart contracts are not possible once deployed into the Blockchain. Thus smart contract has to be vulnerable free before deploying into the Blockchain. In this paper, Bayesian Network Model was designed and constructed based on Bayesian learning concept to detect smart contract security vulnerabilities which are Reentrancy, Tx.origin and DOS. The results showed that the proposed BNMC (Bayesian Network Model Construction) design is able to detect the severity of each vulnerability and also suggest the reasons for the vulnerability. The accuracy of the proposed BNMC results are improved (accuracy 8% increased for both Reentracy and Tx.origin, 6% increased for DOS), compared with traditional method LSTM. This proposed BNMS design and implementation is the first attempt to detect smart contract vulnerabilities using Bayesian Networks.
With the continuous appreciation of cryptocurrency, cryptojacking, the act by which computing resources are stolen to mine cryptocurrencies, is becoming more rampant. In this paper, we conduct a measurement study on cryptojacking network traffic and propose CryptoJacking-Sniffer (CJ-Sniffer), an easily deployable, privacy-aware approach to protecting all devices within a network against cryptojacking. Compared with existing approaches that suffer from privacy concerns or high overhead, CJ-Sniffer only needs to access anonymized, content-agnostic metadata of network traffic from the gateway of the network to efficiently detect cryptojacking traffic. In particular, while cryptojacking traffic is also cryptocurrency mining traffic, CJ-Sniffer is the first approach to distinguishing cryptojacking traffic from user-initiated cryptocurrency mining traffic, making it possible to only filter cryptojacking traffic, rather than blindly filtering all cryptocurrency mining traffic as commonly practiced. After constructing a statistical model to identify all the cryptocurrency mining traffic, CJ-Sniffer extracts variation vectors from packet intervals and utilizes a long short-term memory (LSTM) network to further identify cryptojacking traffic. We evaluated CJ-Sniffer with a packet-level cryptomining dataset. Our evaluation results demonstrate that CJ-Sniffer achieves an accuracy of over 99% with reasonable delays.
Vikas Goel, Mukul Aggarwal, Amit Gupta, Narendra Kumar
An Aadhaar is a unique number issued to every citizen in India. Aadhar’s current identity authentication relies on the central identities data repository (CDIR) of the unique identification authority of India (UIDAI), which is at risk of a single-point fault attack. Perhaps worse, internal attacks can tamper with the sensitive data of authenticated devices without being detected. In this paper, the proposed system utilizes emerging technology: blockchain for solving the issue of centralized authentication. The proposed system provides a distributed, secure, and tamper-proof ledger platform for Aadhar in that Aadhar is implemented using blockchain ethereum technology. The proposed system considers the unique Aadhar identification (ID) for each citizen of India and registered it on the smart contract of ethereum so that this unique ID may be authenticated by each other in a peer-to-peer network without a central authority. For securing the data, the proposed framework uses hashing technique for significant data (i.e. firmware). Blockchain stores hashed data and instantly any change in the state of the data may be possible to detect.
Smart contracts are decentralized applications running on blockchain platforms and have been widely used in a variety of scenarios in recent years. However, frequent smart contract security incidents have focused more and more attention on their security and reliability, and smart contract vulnerability detection has become an urgent problem in blockchain security. Most of the existing methods rely on fixed rules defined by experts, which have the disadvantages of single detection type, poor scalability, and high false alarm rate. To solve the above problems, this paper proposes a method that combines Bi-LSTM and an attention mechanism for multiple vulnerability detection of smart contract opcodes. First, we preprocessed the data to convert the opcodes into a feature matrix suitable as the input of the neural network and then used the Bi-LSTM model based on the attention mechanism to classify smart contracts with multiple labels. The experimental results show that the model can detect multiple vulnerabilities at the same time, and all evaluation indicators exceeded 85%, which proves the effectiveness of the method proposed in this paper for multiple vulnerability detection tasks in smart contracts.
The rapid growth of modern technologies has encouraged non-profit organizations (NPOs) to harness such technologies to better serve the charity sector, especially in relation to charity donation processes. Non-profit organizations primarily rely on fundraising that may involve opaque operations, which leads to the exacerbation of fears that donations may be used for illegal purposes or not reach deserving people. The necessity of charity donations traceability system is inevitable to overcome such concerns, which have an adverse impact on doners’ trust in the donation process. In this study, we propose a blockchain-based donation traceability framework intended to enable all involved parties to trace the progress of charity donations from the moment they are given by donors to the moment they reach the intended recipients. The system is built on a public-permissioned blockchain on the Ethereum platform, with every transaction being recorded as a block in the chain. These blocks of information are immutable and visible to all parties, and they also enable timely and traceable transactions. The proposed framework’s effectiveness is evaluated using a hybrid qualitative approach and proves to improve the traceability of charity donations overcoming the uncertainty associated with current systems.
Coinhive released its browser-based cryptocurrency mining code in September 2017, and vicious web page writers, called vicious miners hereafter, began to embed mining JavaScript code into their web pages, called mining pages hereafter. As a result, browser users surfing these web pages will benefit mine cryptocurrencies unwittingly for the vicious miners using the CPU resources of their devices. The above activity, called Cryptojacking, has become one of the most common threats to web browser users. As mining pages influence the execution efficiency of regular programs and increase the electricity bills of victims, security specialists start to provide methods to block mining pages. Nowadays, using a blocklist to filter out mining scripts is the most common solution to this problem. However, when the number of new mining pages increases quickly, and vicious miners apply obfuscation and encryption to bypass detection, the detection accuracy of blacklist-based or feature-based solutions decreases significantly. This paper proposes a solution, called MinerGuard, to detect mining pages. MinerGuard was designed based on the observation that mining JavaScript code consumes a lot of CPU resources because it needs to execute plenty of computation. MinerGuard does not need to update data used for detection frequently. On the contrary, blacklist-based or feature-based solutions must update their blocklists frequently. Experimental results show that MinerGuard is more accurate than blacklist-based or feature-based solutions in mining page detection. MinerGuard’s detection rate for mining pages is 96%, but MinerBlock, a blacklist-based solution, is 42.85%. Moreover, MinerGuard can detect 0-day mining pages and scripts, but the blacklist-based and feature-based solutions cannot.
Misleading content, fake news and false media spreading across social media platforms is a threat to society. It negatively effects people and its misuse in political propaganda, cyber crimes and other areas is undeniable. This paper presents how to build a secure, trustful and efficient platform to combat against malicious content and fake news by implementing NLP techniques including stop words removal, topic modelling and by applying machine learning models of KNN, Mulitnomial Naive Bayes and deep learning model of LSTM with Word2Vec and GloVe. These models are fed training and testing data by concatenating two kaggle datasets and selecting sample from them. Their accuracy is also compared at the end. To make the system decentralized Etheruem Blockchain is combined and as an offchain storage for blockchain IPFS is used.
We reveal six zero-day social engineering attacks in Ethereum, and subdivide them into two classes: Address Manipulation and Homograph. We demonstrate the attacks by embedding them in source codes of five popular smart contracts with combined market capitalization of over \$29 billion, and show that the attacks have the ability to remain dormant during the testing phase and activate only after production deployment. We analyze 85,656 open source smart contracts and find 1,027 contracts that can be directly used for performing social engineering attacks. For responsible disclosure, we contact seven smart contract security firms. In the spirit of open research, we make the source codes of the attack benchmark, tools, and datasets available to the public.
Non-Fungible Token (NFT) is evolving with the rise of the cryptocurrency market and the development of blockchain techniques, which leads to an emerging NFT market that has become prosperous rapidly then followed by a cooldown. Nevertheless, the overall rise procedure of the NFT market has not been well understood. To this end, we consider that social media communities evolving alongside the market growth, are worth exploring and reasoning about, as the mineable information might unveil the market behaviors. We explore the procedure from the perspective of NFT Twitter communities and its impact on the NFT price moves with two experiments. We perform a Granger causality test on the number of tweets and the NFT price time series and find that the number of tweets has a positive impact on (Granger-causes) the price or reversely for larger part of the 19 top authentic projects but seldom copycat projects. Besides, to investigate the price moves predictability, we experiment on predicting Markov normalized NFT price (representing the direction and magnitude of price moves) given tweets-extracted word features and interpret the feature importance to find insights. Our results show that social media words as the predictors result in all 19 top projects having a testing accuracy evidently above the baseline. Based on the feature importance analysis, we find that both general market-related words and NFT event-related words have a markedly positive contribution in predicting price moves. We summarize the characteristics including categorization and sentiment for the words with the top and least feature importance.
Ali Aljofey, Abdur Rasool, Qingshan Jiang, Qiang Qu
Blockchain technology has allowed many abnormal schemes to hide behind smart contracts. This causes serious financial losses, which adversely affects the blockchain. Machine learning technology has mainly been utilized to enable automatic detection of abnormal contract accounts in recent years. In spite of this, previous machine learning methods have suffered from a number of disadvantages: first, it is extremely difficult to identify features that enable accurate detection of abnormal contracts, and based on these features, statistical analysis is also ineffective. Second, they ignore the imbalances and repeatability of smart contract accounts, which often results in overfitting of the model. In this paper, we propose a data-driven robust method for detecting abnormal contract accounts over the Ethereum Blockchain. This method comprises hybrid features set by integrating opcode n-grams, transaction features, and term frequency-inverse document frequency source code features to train an ensemble classifier. The extra-trees and gradient boosting algorithms based on weighted soft voting are used to create an ensemble classifier that balances the weaknesses of individual classifiers in a given dataset. The abnormal and normal contract data are collected by analyzing the open source etherscan.io, and the problem of the imbalanced dataset is solved by performing the adaptive synthetic sampling. The empirical results demonstrate that the proposed individual feature sets are useful for detecting abnormal contract accounts. Meanwhile, combining all the features enhances the detection of abnormal contracts with significant accuracy. The experimental and comparative results show that the proposed method can distinguish abnormal contract accounts for the data-driven security of blockchain Ethereum with satisfactory performance metrics.
Home delivery (B2C) experienced rapid growth during the COVID-19 pandemic, especially food delivery. Nonrepudiation is a problem in security and affects delivery. Blockchain technology is a new technology that addresses this issue. This paper proposes preventing nonrepudiation in home delivery through blockchain technology. We also design a data structure and smart contract for distributed application. In addition, we evaluate the performance of the proposed framework in terms of transaction fees and response times. We experimented on the blockchain emulator, stored data in RDBMS, and implemented a client with a mobile application. The data in the smart contract were directly impacted by the gas used and the response time. Primarily, the function processes the large data size and involves high transaction fees and long response times. The gas used accounts for 9061 times of data length, while the response time accounts for 2.84 times of data length. Finally, we propose a security policy for the proposed framework.
With the wide application and development of blockchain technology, the past years have witnessed the emergence of various cybercrimes, which have caused a huge amount of economic loss. Among them, phishing scams on the blockchain are regarded as a serious threat to the trading security of the blockchain ecosystem. By modeling the transaction data of blockchain as a network, a series of graph-based phishing detection frameworks have been proposed. Enlightened by adversarial attacks of graph data, we propose to verify the robustness of current phishing detection frameworks under intentional attackers aiming to hide phishing behaviors. In this study, we first propose a general phishing detection framework based on feature engineering and then propose a phishing hiding framework combing the greedy selection mechanism with four phishing hiding strategies to measure the robustness of the proposed general detection models. Extensive experiments evaluate the detective performance of the phishing detection model and its robustness against the hiding framework. The experimental results indicate that the detective model based on feature engineering is rather fragile under adversarial attacks.
K. Vidya, Hussain Imthiaz Hussain, Vishal Celestine, V. Siva Kumar · 5 authors
Abstract Crowdfunding is a method of raising funds from a large number of individuals or businesses. Investors can contribute to any project they are interested in and earn if the initiative is successful. Many crowdfunding sites now exist, and they accept large sums of money from investors and contributors and then leave them with bogus promises. Blockchain-based crowdfunding alters the usual approach to company finance. Generally, when people need to acquire funds to start a firm, they must first develop a strategy, statistical surveys, and models, and then offer their ideas to attract people or organisations. Banks, individual investors, and venture capital firms were among the sources of funding. The modern crowdfunding concept is based on three types of on-screen characters: the task initiator who presents the idea or venture to be financed, individuals or investors who invest in the idea, and a platform that connects these two characters to make the venture successful. It can be used to fund a wide range of start-ups and new concepts, such as inventive activities, medical improvements, travel, and social commercial enterprise projects. This work presents a practical implementation of a crowdfunding application that is secured by a lattice-based cryptosystem for encryption of user data and zero-knowledge proof for the identification of application users. Additionally, machine learning has been used for prediction of campaign success for the benefit of fund contributors.
Cryptocurrencies are highly speculative assets with large price volatility. If one could forecast their behavior, this would make them more attractive to investors. In this work we study the problem of predicting the future performance of cryptocurrencies using social media data. We propose a new model to measure the engagement of users with topics discussed on social media based on interactions with social media posts. This model overcomes the limitations of previous volume and sentiment based approaches. We use this model to estimate engagement coefficients for 48 cryptocurrencies created between 2019 and 2021 using data from Twitter from the first month of the cryptocurrencies' existence. We find that the future returns of the cryptocurrencies are dependent on the engagement coefficients. Cryptocurrencies whose engagement coefficients have extreme values have lower returns. Low engagement coefficients signal a lack of interest, while high engagement coefficients signal artificial activity which is likely from automated accounts known as bots. We measure the amount of bot posts for the cryptocurrencies and find that generally, cryptocurrencies with more bot posts have lower future returns. While future returns are dependent on both the bot activity and engagement coefficient, the dependence is strongest for the engagement coefficient, especially for short-term returns. We show that simple investment strategies which select cryptocurrencies with engagement coefficients exceeding a fixed threshold perform well for holding times of a few months.
N. Gopikarani, B. Gayathri, S.S. Praja, Sneha Sridharan
Counterfeit drugs are without a doubt becoming a greater hazard to consumers and the pharmaceutical sector. As a result, real-time visibility of drug manufacturing and management is required. The proposed system uses Ethereum blockchain as the main technology. The primary advantage of blockchain technology is that the transactions are maintained in immutable digital ledger format and it may be read easily without jeopardizing the users’ security and privacy. In our proposed system, the admin validates and adds the manufacturers. The manufacturer after registering and logging in can perform tasks like adding the drug and seller list. The seller can place order to the manufacturer which the manufacturer can accept or reject. The seller can update status of order of accepted orders to delivered. The customer can view the order details by entering the serial number on the drug package. Any transaction or exchange that occurs in the network is recorded in the chain. It functions similarly to other networks, but blockchain technology is distinguished by the fact that no data can be removed or altered by anyone in the network. No changes to the network can be made unless it has been validated by all of the network’s authorized users. All the information stored can be read by anybody so to incorporate more security, AES has been used to store data in the blockchain. The use of AES encryption technique distinguishes this system from all the existing implementations. Thus, this makes it easy to trace to the exact point in the supply chain and detect any counterfeit drugs in movement. As an extension to the drug counterfeit prevention system a Drug Recommendation System is also performed using the ensemble model with a combination of Random Forest and Logistic Regression for sentiment analysis training. Furthermore, when compared to the existing Linear SVM model, which has an accuracy of 90.39%, the suggested model has the best accuracy of 93.31%. Using the obtained sentiment for each drug, the drug is predicted accurately for the specified medical condition.
Ethereum smart contract and the agreements contained therein exist across a distributed, decentralized blockchain network, which controls the execution of Dapps, and transactions are trackable and irreversible. Since the amount of smart contracts on Ethereum has a rapid growth, which also lead to a large number of security vulnerabilities. However, the traditional method such as static symbolic analysis requires manual analysis in advance with high false detection rate. In this paper, we propose a GVD-net model for smart contract vulnerability detection. GVD-net consists of preprocessing section,backbone-net and detection section.We prepossess the source code and gain a weight matrix in the first section. In backbone-net, we generate a CFG graph according to the variables and function calling relationships of the solidity code, and use the CFG graph to build the corresponding relationship of nodes and generate a non-Euclidean graph. Then we use the adjacent search algorithm to generate a non-complete random walk sequence, and use the graph embedding algorithm Node2Vec to generate a 256-dimensional vector and gain another weight matrix. We input the test code into GVD-net to generate the threshold ϑ in the final section and will classify the smart contract as danger code if the ϑ is bigger than 0.75. Moreover, GVD-net will confirm the type of vulnerability according to weight matrix. We conduct comprehensive experiments on GVD-net with the SBcurated data-set, and the experiment results show that (1) GVD-net can classify a smart contract as safe or danger code, with the accuracy of 90.2% (2) GVD-net can detect three types of vulnerability (arithmetic issues, access control and asset frozen) and attain good performance within 1s.
Decentralized cryptocurrency systems have become primary targets for attackers due to substantial profit gain and economic rewards. A number of attack models have been proposed during last few years. However, the evaluation and comparison of those attack models remain problematic due to the lack of systematic framework to analyze them. In this work, we propose a general quantitative analysis framework for attack models in the network and consensus layer of blockchain. We identify the problem statement and evolution process. And we show how to apply our general framework in previous attacks such as selfish mining and bribery attack. We also explained that the framework is suitable for other attacks in blockchain. For further exploration, we simulate the success rate and benefits of different attacks through experiments. We provide several defensive strategies, and study how these strategies against previous attack models.
Zheyuan He, Shuwei Song, Yang Bai, Xiapu Luo · 10 authors
Tokens have become an essential part of blockchain ecosystem, so recognizing token transfer behaviors is crucial for applications depending on blockchain. Unfortunately, existing solutions cannot recognize token transfer behaviors accurately and efficiently because of their incomplete patterns and inefficient designs. This work proposes TokenAware , a novel online system for recognizing token transfer behaviors. To improve accuracy, TokenAware infers token transfer behaviors from modifications of internal bookkeeping of a token smart contract for recording the information of token holders (e.g., their addresses and shares). However, recognizing bookkeeping is challenging, because smart contract bytecode does not contain type information. TokenAware overcomes the challenge by first learning the instruction sequences for locating basic types and then deriving the instruction sequences for locating sophisticated types that are composed of basic types. To improve efficiency, TokenAware introduces four optimizations. We conduct extensive experiments to evaluate TokenAware with real blockchain data. Results show that TokenAware can automatically identify new types of bookkeeping and recognize 107,202 tokens with 98.7% precision. TokenAware with optimizations merely incurs 4% overhead, which is 1/345 of the overhead led by the counterpart with no optimization. Moreover, we develop an application based on TokenAware to demonstrate how it facilitates malicious behavior detection.
Due to the decentralized and public nature of the blockchain ecosystem, malicious activities on the Ethereum platform impose immeasurable losses on users. At the same time, the transparency of cryptocurrency transactions provides a unique opportunity to analyze illegal activities, such as phishing scams, from a network perspective. Most existing phishing scam detection methods focus primarily on analyzing account interaction networks, which limits their ability to uncover transaction behavior patterns embedded within transaction interactions. To address this, we construct theTransactionSubGraphNetwork (TSGN) by using transaction subgraphs as basic elements and further propose a novel framework for Ethereum phishing account detection. Specifically, we rebuild the graph structures via three well-designed mapping mechanisms, yielding TSGN and its two variants, i.e., Directed-TSGN and Temporal-TSGN, to obtain direction-aware and time-aware transfer flow features. By further incorporating the mapping strategy into transaction multidigraphs, we develop the Multiple-TSGN, which could preserve more transaction flow features while concurrently reducing the time consumption of modeling large-scale networks. TSGN models based on transaction subgraph interactions can capture complex higher-order dependencies, which lay beyond the reach of models that exclusively capture pairwise account interactions. As a general framework, our model can incorporate various feature extraction methods to improve the performance of phishing detection. Extensive experimental results on Ethereum datasets show that our method achieves superior performance in phishing detection, yielding 3.27%$\sim$6.71% relative improvement over previous state-of-the-art.