ABSTRACT The burgeoning demand for blockchain technology in diverse sectors requires advanced optimization methods to improve the performance, security and privacy. However, today common blockchain mechanisms are effected by problems like suboptimal miner selection processes, susceptibility to abnormal transactions and types of attacks affecting nonânegligible parts of the ecosystem, performance bottlenecks and so forth, rendering them far from scalability and realâworld usage. This paper addresses the problem, by introducing a set of sophisticated methods that solve recent issues and enhances the robustness, scalability, confidentiality in blockchain networks. Firstly, we present âDeepMinerâ, a deep learningâbased solution that leverages historical blockchain data samples to infer optimal miner nodes. This method improves the block generation efficiency by optimizing miner node selection in realâtime, which is an essential addition to traditional random or otherwise static methods for selecting miners. Secondly, âAnoBlockâ which uses anomaly detection model to detect fraud in blockchain transactions using the statistical methods like Gaussian mixture models and isolation forests. Thirdly, âOptiChainâ uses data analytics to dynamically optimize blockchain performance by continuously evaluating live network metrics and the transaction throughout. Lastly, âPrivyChainâ which uses privacy preservation techniques such as zeroâknowledge proofs and homomorphic encryption to achieve transaction confidentiality while retaining blockchain transparency. Their solution addresses these issues with a dual approach to protect any sensitive transaction details from being leaked and make it feasible for computations over encrypted data, the result of which aligns blockchain technology with stringent privacy standards.
With the rapid development of blockchain technology and the popularity of cryptocurrency, phishing scams pose an increasingly severe threat to the security of cryptocurrency transactions. Existing fraud detection methods have not accurately identified phishing behaviors, especially failing to capture key neighbor information and its impact effectively. To address this problem, we proposed a phishing detection framework based on FAAN-GBM (Feature and Attention Augmented Network with Gradient Boosting Machine), which aims to improve phishing fraud detection effectiveness on the Ethereum platform by further refining the extraction of phishing account features. This framework integrates basic features, transaction features, and interaction features of nodes, optimizes feature aggregation through importance analysis and attention mechanism of neighbor node, and uses autoencoders to deepen the nonlinear expression of node features. Through extensive testing on real Ethereum datasets, FAAN-GBM has demonstrated superior performance over existing methods, effectively improving the identification accuracy of phishing fraud nodes.
M Lakshmanan, G. S. Anandha Mala, R. Poorni, G. Ilamurugan ¡ 6 authors
Crowdfunding, a method of raising capital through collective contributions, often faces challenges related to transaction efficiency, security, and transparency. Blockchain addresses these issues by providing a decentralized, transparent, and secure environment for transactions, while smart contracts automate the fund release process based on predefined conditions. PSO, a nature-inspired optimization algorithm, is applied to optimize various crowdfunding parameters such as funding strategies, donor behavior, and campaign success. By combining PSO with blockchain, the proposed model improves both the speed and security of crowdfunding transactions. This research presents an innovative approach to enhancing crowdfunding platforms by integrating Particle Swarm Optimization (PSO) with blockchain technology. Proposed Model achieves a 30% reduction in transaction time and a 40% improvement in security. This is achieved through the use of a Merkle tree for secure data storage and smart contracts to ensure efficient and automated fund management. The integration of PSO further optimizes key parameters, such as fund distribution and reward allocation, leading to a more efficient and transparent platform. The combination of these technologies results in a crowdfunding experience that is not only faster but also more secure and reliable, enabling global, borderless funding while minimizing fraud and enhancing trust among participants. This research highlights the potential of OPSA-enabled blockchain solutions to transform the crowdfunding landscape, offering a more efficient, secure, and transparent funding platform.
Roozbeh Sarenche, Ren Zhang, Svetla Nikovaâ, Bart Preneel
A Bitcoin miner who owns a sufficient amount of mining power can perform selfish mining to increase its relative revenue. Studies have demonstrated that the time-averaged profit of a selfish miner starts to rise once the mining difficulty level gets adjusted in favor of the attacker. Selfish mining profitability lies in the fact that orphan blocks are not incorporated into the current version of Bitcoinâs difficulty adjustment mechanism (DAM). Therefore, it is believed that considering the count of orphan blocks in the DAM can result in complete unprofitability for selfish mining. In this paper, we disprove this belief by providing a formal analysis of the selfish mining time-averaged profit. We present a precise definition of the orphan blocks that can be incorporated into calculating the next epochâs target and then introduce two modified versions of DAM in which both main-chain blocks and orphan blocks are incorporated. We propose two versions of smart intermittent selfish mining, where the first one dominates the normal intermittent selfish mining, and the second one results in selfish mining profitability under the modified DAMs. Moreover, we present the orphan exclusion attack with the help of which the attacker can stop honest miners from reporting the orphan blocks. Using combinatorial tools, we analyze the profitability of selfish mining accompanied by the orphan exclusion attack under the modified DAMs. Our results show that even when considering orphan blocks in the DAM, selfish mining can still be profitable. However, the level of profitability under the modified DAMs is significantly lower than that observed under the current version of Bitcoin DAM, suggesting that orphan reporting can be an effective countermeasure against a payoff-maximizing selfish miner.
As more and more attacks have been detected on Ethereum smart contracts, it has seriously affected finance and credibility. Current anti-fraud detection techniques, including code parsing or manual feature extraction, still have some shortcomings, although some generalization or adaptability can be obtained. In the face of this situation, this paper proposes to use graphical representation learning technology to find transaction patterns and distinguish malicious transaction contracts, that is, to represent Ethereum transaction data as graphs, and then use advanced ML technology to obtain reliable and accurate results. Taking into account the sample imbalance, we treated with SMOTE-ENN and tested several models, in which MLP performed better than GCN, but the exact effect depends on its field trials. Our research opens up more possibilities for trust and security in the Ethereum ecosystem.
Aman Luthra, James Cavanaugh, Hugo Renzzo Olcese, Michael W. Raymond ¡ 7 authors
Auditing the trading history of an investment fund is an effective guard against financial frauds. But how can it be performed publicly, in real time, and without disclosing any commercial secret of a fund? In 2020, Luthra et al. developed ZeroAUDIT, a customized zero-knowledge protocol based on Merkle tree, which can assert that the accrued profit of an investment fund is as claimed given commitment/encryption of its transaction records. It was believed that a customized protocol has much better performance than general purpose zk-proof systems. In this work, we show that it is not true. We present ZeroAUDITGEN, a polymorphic zk-proof system for the same zk-audit problem, over a variety of general purpose zk-proof systems. We show that the prover and verifier cost can be greatly reduced by appropriate choice of security assumptions and design of concrete cryptographic constructions.
An eclipse attack is a strategy where attackers control communication between nodes in peer-to-peer networks, such as Ethereum, using compromised nodes to escalate further attacks. Given the vast and complex nature of big data in Ethereum networks, detecting these attacks is challenging. This paper aims to identify effective features for eclipse attack detection by analyzing large volumes of network traffic data. We simulate an Ethereum network, conducting eclipse attacks to generate datasets where 28% of the traffic consists of malicious packets. We apply five feature extraction methodsâcommon network traffic, Entropy, Ď-Divergence, packet communication statistics, and packet characteristics statisticsâleveraging big data analysis techniques to process and refine extensive traffic data. To address the challenges posed by imbalanced and overlapping data, SMOTE and Tomek link algorithms are used, and Mutual Information selects the most significant features to enhance classifier performance. We evaluate five machine learning models, including XGBoost, kNN, and Random Forest, finding that XGBoost achieves the highest performance, with 99.25% accuracy and a computational time of 184 ms when processing the top 25 features, which indicates real-time detection could be possible.
Phuong Duy Huynh, Son Hoang Dau, Nicholas Huppert, Joshua Cervenjak ¡ 8 authors
We explored the ubiquitous phenomenon of serial scammers, each of whom deployed dozens to thousands of addresses to conduct a series of similar Rug Pulls on popular decentralized exchanges. We first constructed two datasets of around 384,000 scammer addresses behind all one-day Simple Rug Pulls on Uniswap (Ethereum) and Pancakeswap (BSC), and identified distinctive scam patterns including star, chain, and major (scam-funding) flow. These patterns, which collectively cover about $40\%$ of all scammer addresses in our datasets, reveal typical ways scammers run multiple Rug Pulls and organize the money flow among different addresses. We then studied the more general concept of scam cluster, which comprises scammer addresses linked together via direct ETH/BNB transfers or behind the same scam pools. We found that scam token contracts are highly similar within each cluster (average similarities $>70\%$) and dissimilar across different clusters (average similarities $<30\%$), corroborating our view that each cluster belongs to the same scammer/scam organization. Lastly, we analyze the scam profit of individual scam pools and clusters, employing a novel cluster-aware profit formula that takes into account the important role of wash traders. The analysis shows that the existing formula inflates the profit by at least $32\%$ on Uniswap and $24\%$ on Pancakeswap.
Oqeili Saleh, Abu-alzanat Thamer, Alkaraimah Qutaibah, al smadi Takialddin
CAPTCHA, which stands for Completely Automated Public Turing Test to Tell Computers and Humans Apart, is a commonly employed security measure to distinguish between humans and computers. The Turing Test, designed to guarantee network security, is the foundation of this security technique. Usability is a crucial concern that can prevent human users from engaging in laborious and time-consuming tasks. When designing CAPTCHA, security and usability must be addressed simultaneously. When designing CAPTCHA, it is crucial to address security and usability simultaneously. A concerted effort is required to protect online data and guarantee privacy and security. The personal information of Internet users remains susceptible to theft. This study uses an information extraction technique called CAPTCHA to investigate the hazards associated with violating user privacy. It is a highly harmful process due to hacking, theft, unauthorized reuse, and the breach of user information. This study proposes a privacy preservation system employing concurrent encryption techniques, multilateral security computing, and zero-knowledge proof. The objective is to create a system that allows for uncomplicated and secure puzzle-solving using dice gas. CAPTCHA limits access to users' information. In the overview and application of evidentiary measurable methods, we can draw significant conclusions about the more extensive client group's discernments and encounters with CAPTCHA as a privacy-preserving component.
Phishing scams represent a significant criminal activity on Ethereum, driving the need for effective detection methods. The methods based on graph neural networks(GNNs) make significant breakthroughs due to their ability to model complex transaction networks. However, existing approaches often overlook the heterogeneity of Ethereumâs transaction graph during neighbor nodes aggregation. These methods typically focus on low-order neighbors, disregarding high-order ones, which limits their overall performance. To this end, we propose the High- and Low-order Transaction Aggregation Graph Network(HLTAG), which separately aggregates high- and low-order features for more effective feature representation. Specifically, we utilize biased random walk to aggregate low-order neighbors. We employ path aggregation to handle high-order neighbors. To mitigate the influence of noise and redundant information from high-order neighbors, we introduce a combination of attention decay, node similarity, and path attention mechanism, which dynamically adjust the aggregation weights. Extensive experiments demonstrate that HLTAG (94.4% Recall and 89.3% AUC) outperforms the state-of-the-art approaches in detecting Ethereum phishing scams, and exhibits significant advantages in large-scale scenarios.
Xiaolin Wen, Tai D. Nguyen, Shaolun Ruan, Qiaomu Shen ¡ 7 authors
With the prevalence of smart contracts, smart Ponzi schemes have become a common fraud on blockchain and have caused significant financial loss to cryptocurrency investors in the past few years. Despite the critical importance of detecting smart Ponzi schemes, a reliable and transparent identification approach adaptive to various smart Ponzi schemes is still missing. To fill the research gap, we first extract semantic-meaningful actions to represent the execution behaviors specified in smart contract bytecodes, which are derived from a literature review and in-depth interviews with domain experts. We then propose PonziLens+, a novel visual analytic approach that provides an intuitive and reliable analysis of Ponzi-scheme-related features within these execution behaviors. PonziLens+ has three visualization modules that intuitively reveal all potential behaviors of a smart contract, highlighting fraudulent features across three levels of detail. It can help smart contract investors and auditors achieve confident identification of any smart Ponzi schemes. We conducted two case studies and in-depth user interviews with 12 domain experts and common investors to evaluate PonziLens+. The results demonstrate the effectiveness and usability of PonziLens+ in achieving an effective identification of smart Ponzi schemes.
In this paper, we examine a novel category of services in the blockchain ecosystem termed Instant Cryptocurrency Exchange (ICE) services. Originally conceived to facilitate cross-chain asset transfers, ICE services have, unfortunately, been abused for money laundering activities due to two key features: the absence of a strict Know Your Customer (KYC) policy and incomplete on-chain data of user requests. As centralized and non-transparent services, ICE services pose considerable challenges in the tracing of illicit fund flows laundered through them. Our comprehensive study of ICE services begins with an analysis of their features and workflow. We classify ICE services into two distinct types: Standalone and Delegated. We then perform a measurement analysis of ICE services, paying particular attention to their usage in illicit activities. Our findings indicate that a total of 12,473,290 illegal funds have been laundered through ICE services, and 432 malicious addresses were initially funded by ICE services. Based on the insights from measurement analysis, we propose a matching algorithm designed to evaluate the effectiveness of ICE services in terms of efficiency and prevention of traceability. Our evaluation reveals that 92% of the user requests analyzed were completed in less than three minutes, underscoring the efficiency of ICE services. In addition, we demonstrate that the algorithm is effective in tracing illicit funds in situations where ICE services are used in malicious activities. To engage the community, the entire dataset used in this study is open-source.
The Web3 ecosystem is increasingly evolving to multi-chain, with decentralized applications (dApps) distributing across different blockchains, which drives the need for cross-chain bridges for blockchain interoperability. However, it further opens new attack surfaces, and media outlets have reported serious attacks related to cross-chain bridges. Nevertheless, few prior research studies have studied cross-chain bridges and their related transactions, especially from a security perspective. To fill the void, this paper presents the first comprehensive analysis of cross-chain transactions. We first make efforts to create by far the largest cross-chain transaction dataset based on semantic analysis of popular cross-chain bridges, covering 13 decentralized bridges and 7 representative blockchains, with over 80 million transactions in total. Based on this comprehensive dataset, we present the landscape of cross-chain transactions from angles including token usage, user profile and the purposes of transactions, etc. We further observe that cross-chain bridges can be abused for malicious/aggressive purposes, thus we design an automated detector and deploy it in the wild to flag misbehaviors from millions of cross-chain transactions. We have identified hundreds of abnormal transactions related to exploits and arbitrages, etc. Our research underscores the prevalence of cross-chain ecosystems, unveils their characteristics, and proposes an effective detector for pinpointing security threats.
The Domain Name System (DNS) has been providing a decentralized global namespace to support all Internet applications and usages over the last few decades. In the recent years, a number of blockchain-based name systems have emerged with the claim of providing better namespace decentralization than DNS. The community at large seems uncertain with regard to which of these systems is the best in providing decentralized Internet namespace control. In this paper, we first deconstruct the design of DNS, identify its three essential components and explain who controls each of them. We then examine the Ethereum Name Service (ENS) as a representative example of blockchain-based naming systems, gauge the degree of its decentralization. Finally, we conduct a comparative analysis between DNS and ENS to assess the validity and affordability of each design and the (de)centralization in their namespace control and name system operations.
This paper presents the first comprehensive analysis of the address poisoning attack surged on the Ethereum blockchain. This phishing attack typically exploits the address shortening feature of Ethereum explorers and digital wallets (e.g., Etherscan and MetaMask) by crafting token transfer events with a seemingly correct address to poison victims' transfer history, waiting for them to mistakenly transfer assets to the attacker's address.
Cong Wu, Jing Chen, Ziming Zhao, Kun He ¡ 10 authors
Decentralized finance has experienced phenomenal growth, revolutionizing the landscape of financial transactions and asset management via blockchain. Yet, this swift growth brings with it substantial challenges, notably the surge in scam tokens, imposing significant security threats on cryptocurrency investments and trading. Existing detection methods of scam token, primarily relying on analyzing contract codes or transaction patterns, struggle to catch increasingly sophisticated tactics employed by scammers. For example, contract-based analysis are unable to identify scams lacking overt malicious code, e.g., most rugpulls, while transaction-based methods generally lack the foresight to early-detect potential risks.
This paper presents a comprehensive analysis of adaptive behavioral authentication systems designed for fraud detection in financial services. These systems leverage real-time user behavior, such as typing patterns, mouse movements, and geolocation data, to continuously monitor and assess authentication risks. A layered approach integrates behavioral analysis with traditional credentials, providing enhanced security against evolving fraud techniques. The proposed system illustrates the interaction between users, the authentication system, a behavioral engine, and fraud detection models, enabling dynamic decision-making processes. The proposed framework enhances fraud detection by ensuring robust monitoring without compromising user experience. Future work aims to address challenges in data privacy, ethical considerations, and system adaptability to emerging financial technologies like decentralized finance (DeFi).
Blockchain technology is significant because it makes data sharing between several parties safe, transparent, and effective. Multi-step transactions that require verification and traceability can benefit from Blockchain technology. However, the Blockchain technology too comes with its own vulnerabilities and often Blockchain networks are attacked by attacks like 51% Attack, Eclipse Attack, Sybil Attack, Time jacking Attack, Selfish Mining Attack, Finney Attack, Race Attack and so on. One among those attacks is the Sybil attacks, which are a big threat to the integrity of Blockchain networks since they assist malicious actors to create several identities, potentially overwhelming the system and defeating the very principle of consensus mechanisms. In this paper, we have captured an approach on designing a multi-layered mechanism for identifying Sybil attacks with the integration of behavioral analysis, Blockchain analysis techniques, Zero-Knowledge Proofs (ZKPs), and a robust security architecture for governance and validator selection. The broad idea is to cancel pseudo-anonymity in the Blockchain systems by detecting behavioral patterns, identifying exchange wallets, and mapping inter-wallet relationships. Integration of these approaches with ZKPs assists in improving identity verification while simultaneously maintaining user anonymity. The proposed architecture for security uses community-based governance and adaptive validator selection processes to strengthen the defense against Sybil attacks. Token concentration analysis traces down the distribution of stakes within the network in order to find potential risks due to centralization. Our findings thus conclude that integration of the security framework along with behavior analysis and ZKPs effectively reduces the proliferation of fraudulent identities in the Blockchain networks.
Mikhail Dymkov, Vladimir Gorgadze, Alexey Karanyuk, Artem Barger
This paper establishes a groundbreaking framework for pinpointing and scrutinizing web3 protocols that display attributes akin to Ponzi schemes. We meticulously define the defining features of these protocols and introduce sophisticated methodologies to assess their stability, fine-tuning their parameters, and crafting economic mechanisms to boost their sustainability. The robustness of our framework is vividly showcased through comprehensive case studies of two prominent web3 protocols: Safemoon and Ethena. In the Ethena case study, we take a step further by devising an advanced economic mechanism for automatic interest rate regulation, employing an innovative feedback loop system.
Samer Muthana Sarsam, Ahmed Ibrahim Alzahrani, Hosam AlâSamarraie, Fahad Alblehai
This study explored the role of gender preferences in cryptocurrency investments using sentiment analysis. X (Twitter) usersâ gender (male/female) together with relevant sentiments (positive/negative) were extracted and investigated in this study. The Latent Dirichlet Allocation technique was utilised to model gender-related topics in an attempt to understand male and female usersâ preferences to invest in cryptocurrency. The Apriori algorithm was employed to predict the highly associated investment terminologies with each gender. A predictive model was built to predict the type of digital currency preferred by X users. Using sentiment-based gender data, the results showed a high prediction accuracy (98.64%) of digital currency preferences. The study demonstrated that male users would most likely use Bitcoin, compared to female users who preferred Ethereum. This study further offers a novel mechanism to predict usersâ preferences for cryptocurrency platforms using their sentiment features. It extends the knowledge of cryptocurrencies in the financial business profile by revealing how investorsâ gender contributes to investment-related decisions.