As the development of Solidity contracts on Ethereum , more developers are reusing them on other compatible blockchains. However, developers may overlook the differences between the designs of the blockchain system, such as the Gas Mechanism and Consensus Protocol , leading to the same contracts on different blockchains not being able to achieve consistent execution as on Ethereum . This inconsistency reveals design flaws in reused contracts, exposing code smells that hinder code reusability, and we define this inconsistency as EVM-Inequivalent Code Smells . In this paper, we conducted the first empirical study to reveal the causes and characteristics of EVM-Inequivalent Code Smells . To ensure the identified smells reflect real developer concerns, we collected and analyzed 1,379 security audit reports and 326 Stack Overflow posts related to reused contracts on EVM-compatible blockchains, such as Binance Smart Chain (BSC) and Polygon . Using the open card sorting method, we defined six types of EVM-Inequivalent Code Smells . For automated detection, we developed a tool named EquivGuard . It employs static taint analysis to identify key paths from different patterns and uses symbolic execution to verify path reachability. Our analysis of 905,948 contracts across six major blockchains shows that EVM-Inequivalent Code Smells are widespread, with an average prevalence of 17.70%. While contracts with code smells do not necessarily lead to financial loss and attacks, their high prevalence and significant asset management underscore the potential threats of reusing these smelly Ethereum contracts. Thus, developers are advised to abandon Copy-and-Paste programming practices and detect EVM-Inequivalent Code Smells before reusing Ethereum contracts.
M. K. Ghosh, Chirag Dinesh Jain, Raju Halder, Joydeep Chandra
Phishing scams on the Ethereum network have become a serious threat, especially with the influx of new users into the cryptocurrency market. Current detection methods are mainly focused on long-term consistent transaction patterns with smooth temporal dynamics. However, these methods often struggle to differentiate between phishing and non-phishing users, whose behaviours may appear deceptively similar. Additionally, they face challenges such as network sparsity and data leakage, leading to significant performance limitations. To address these issues, we introduce TEMPER, a novel sequential learning framework designed to jointly capture the subtle distinctions between long- and short-term user behaviours and their correlations to provide more comprehensive insights. TEMPER effectively generates distinguishable user embeddings, enabling the accurate identification of phishing users. Unlike previous approaches, TEMPER mitigates data leakage through a novel sequential transaction sampling algorithm and addresses network sparsity with short-term temporal learning. Through extensive experimentation on three real-world Ethereum datasets, TEMPER demonstrates its efficacy by achieving a 3-4% improvement in the F1-Score compared to existing baseline models, representing a significant advancement in Ethereum phishing user detection.
Junhao Wu, Yixin Yang, Chengxiang Jin, Silu Mu · 8 authors
With the widespread adoption of Ethereum, financial frauds such as Ponzi schemes have become increasingly rampant in the blockchain ecosystem, posing significant threats to the security of account assets. Existing Ethereum fraud detection methods typically model account transactions as graphs, but this approach primarily focuses on binary transactional relationships between accounts, failing to adequately capture the complex multi-party interaction patterns inherent in Ethereum. To address this, we propose a hypergraph modeling method for the Ponzi scheme detection method in Ethereum, called HyperDet. Specifically, we treat transaction hashes as hyperedges that connect all the relevant accounts involved in a transaction. Additionally, we design a two-step hypergraph sampling strategy to significantly reduce computational complexity. Furthermore, we introduce a dual-channel detection module, including the hypergraph detection channel and the hyper-homo graph detection channel, to be compatible with existing detection methods. Experimental results show that, compared to traditional homogeneous graph-based methods, the hyper-homo graph detection channel achieves significant performance improvements, demonstrating the superiority of hypergraph in Ponzi scheme detection. This research offers innovations for modeling complex relationships in blockchain data.
Kyounggon Kim, Seok‐Hee Lee, Sundaresan Ramachandran, Ibrahim Alzahrani
Cybercriminals are employing sophisticated techniques to illegally obtain money from victims, with ransomware, that is the most notorious malware utilized for financial gain. This paper focuses on the Arab world, a prime target region for ransomware gangs. Due to rapid economic growth and digitalization in this region, cybercriminals are increasingly targeting it. However, there is a lack of research on ransomware crime syndication in the Arab region. Data on claimed ransomware victims from 2020 to 2023 was collected from the darknet. Analysis of ransomware gangs in this area revealed significant findings. Based on three years of data collection and analysis, 20 ransomware gangs primarily operating in the Arab region were identified in 2023. Three major ransomware gangs-LockBit, ALPHV/BlackCat, and CL0P-are predominantly targeting the Arab world, with the United Arab Emirates and Saudi Arabia being major targets, along with the manufacturing industry. In addition to identifying the ransomware gangs, the tactics, techniques, and procedures (TTP) used by them were also identified. There was 17 TTPs used by ransomware gangs. This study has also developed a platform to track ransomware gangs and cryptocurrency transactions. Bitcoin’s anonymity and popularity made it the most preferred cryptocurrency by ransomware gangs. This research lays the groundwork for further studies to understand the exact trends and data related to ransomware in the Arab world.
The decentralized, open-source architecture of blockchain technology, exemplified by the Ethereum platform, has transformed online transactions by enabling secure and transparent exchanges. However, this architecture also exposes the network to various security threats that cyber attackers can exploit. Detecting suspicious behaviors in account on the Ethereum blockchain can help mitigate attacks, including phishing, Ponzi schemes, eclipse attacks, Sybil attacks, and distributed denial of service (DDoS) incidents. The proposed system introduces an ensemble stacking model combining Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and a neural network (NN) to detect potential threats within the Ethereum platform. The ensemble model is fine-tuned using Bayesian optimization to enhance predictive accuracy, while explainable artificial intelligence (XAI) tools-SHAP, LIME, and ELI5-provide interpretable feature insights, improving transparency in model predictions. The dataset used comprises 9,841 Ethereum transactions across 52 initial fields (reduced to 17 relevant features), encompassing both legitimate and fraudulent records. The experimental findings demonstrate that the proposed model achieves a superior accuracy of 99.6%, outperforming that of other cutting-edge methods. These findings demonstrate that the XAI-enabled ensemble stacking model offers a highly effective, interpretable solution for blockchain security, strengthening trust and reliability within the Ethereum ecosystem.
As cryptocurrency transactions continue to grow, detecting scams within transaction records remains a critical challenge. These transactions can be represented as dynamic graphs, where Neural Network Convolution (NNConv) models are widely used for detection. However, NNConv models suffer from model decay due to evolving transaction patterns, the introduction of new users, and the emergence of adversarial techniques designed to evade detection. To address this issue, we propose an automated, periodic hyperparameter optimization method based on proximal policy optimization (PPO), a reinforcement learning algorithm designed for dynamic environments. By leveraging PPO’s stable policy updates and efficient exploration strategies, our approach continuously refines hyperparameters to sustain model performance without frequent retraining. We evaluate the proposed method on a large-scale cryptocurrency transaction dataset containing 2,973,489 nodes and 13,551,303 edges. The results demonstrate that our method achieves an F1 score of 0.9478, outperforming existing graph-based approaches. These findings validate the effectiveness of PPO-based optimization in mitigating model decay and ensuring robust cryptocurrency scam detection.
Detecting fraudulent activities such as Ponzi schemes within smart contract transactions is a critical challenge in decentralized finance. Existing methods often fail to capture the heterogeneous, multi-faceted nature of blockchain data, and many graph-based models overlook the contextual patterns that are vital for effective anomaly detection. In this paper, we propose MVCG-SPS, a Multi-View Contrastive Graph Neural Network designed to address these limitations. Our approach incorporates three key innovations: (1) Meta-Path-Based View Construction, which constructs multiple views of the data using meta-paths to capture different semantic relationships; (2) Reinforcement-Learning-Driven Multi-View Aggregation, which adaptively combines features from multiple views by optimizing aggregation weights through reinforcement learning; and (3) Multi-Scale Contrastive Learning, which aligns embeddings both within and across views to enhance representation robustness and improve anomaly detection performance. By leveraging a multi-view strategy, MVCG-SPS effectively integrates diverse perspectives to detect complex fraudulent behaviors in blockchain ecosystems. Extensive experiments on real-world Ethereum datasets demonstrated that MVCG-SPS consistently outperformed state-of-the-art baselines across multiple metrics, including F1 Score, AUPRC, and Rec@K. Our work provides a new direction for multi-view graph-based anomaly detection and offers valuable insights for improving security in decentralized financial systems.
B. N. Das, Babul Chandra Sarker, Amit Saha, Kanchon Kumar Bishnu · 9 authors
The exponential growth of cryptocurrency implementation in the USA has brought with it a surge in correlated risks, particularly in the form of scams that exploit the relative novelty and complexity of digital currencies. The primary objective of this study was to develop machine algorithms for identifying fraud trends in cryptocurrency transactions. By employing complex analysis, this research project attempted to identify certain trends and behaviors that fall under a variety of scams, providing a platform for effective detection and counter-strategies. This study will have a definite objective in terms of Bitcoin, Ethereum, and other high-profile cryptocurrencies in America when it comes to scam analysis. The scam-related transaction dataset comprised in-depth information regarding suspicious fraud activity in the cryptocurrency environment, such as a specific ID for a transaction, timestamps, values for transactions, and labels distinguishing between suspicious and legitimate activity. A variety of proven models were selected such as Logistic Regression, Random as well Multinomial Naive Bayes, where each model had its respective weaknesses and strengths. The Random Forest algorithm attained the highest accuracy, nearing perfection which underscores its robustness and reliability in classifying both legitimate and fraudulent reports. To effectively counter fraud in cryptocurrencies, U.S. policies must be strengthened with a merger of machine intelligence in them. Regulatory agencies have to work towards developing a system that encourages exchanges to utilize complex analysis for fraud detection, perhaps in terms of reduced compliance burden for entities with effective anti-fraud controls in position. Leveraging AI insights can go a long way in supporting investigations into scams in cryptocurrencies conducted by governments. By utilizing machine algorithms trained with datasets of past scams, governments can monitor and follow illicit fund flows through the blockchain with ease.
The integration of blockchain with Information-Centric Networking (ICN) enhances content distribution efficiency in areas such as the Internet of Things (IoT) and 5G/6G communications. This integration implies that the network state information of ICN can significantly impact consensus efficiency. However, the Ethereum Casper FFG consensus algorithm overlooks the network heterogeneity among consensus nodes, leading to a potential bottleneck in consensus efficiency, especially when nodes with inferior network quality participate. To address this issue, this paper proposes a multidimensional reputation model based on an ICN-enabled blockchain architecture. The model combines on-chain stake and network contributions to evaluate the reputation of ICN consensus nodes. Furthermore, a reputation-based hybrid consensus mechanism, RepuICN, is introduced, which enhances the network layer of the Casper FFG algorithm. This mechanism selects higher-reputation ICN consensus nodes as proposers for checkpoint blocks, mitigating the impact of network latency fluctuations on block propagation. Additionally, RepuICN improves block propagation efficiency through ICN multicast and caching techniques. Simulation results show that, under identical conditions with a network of 5000 nodes and 2% ICN nodes, RepuICN reduces broadcast latency by 17% for regular blocks and 61.4% for checkpoint blocks and achieves 3.4 times higher transaction throughput than Casper FFG.
In recent years, the proliferation of malware has reached unprecedented levels, leading to escalating cybercrime costs. Signatures extracted by static analysis of files have been widely adopted for malware detection: vendors maintain databases of known malware signatures that are shared with registered users. The recent literature has proposed the use of private and consortium (thus, permissioned) blockchains for spreading signatures among blockchain users. These approaches require controlled access to enhance trust and accountability but restrict the widespread sharing of up-to-date signatures because users must be registered. In this paper, we present a novel technique that leverages a public blockchain to enable the massive dissemination of malware signatures among any users since a public blockchain is permissionless. On the other hand, the use of a public blockchain introduces new challenges related to security and data privacy, which our solution solves. The main benefit and outcome of our solution is that any users can securely access and verify malware signatures facilitating real-time detection of malicious files. We implemented our solution in Ethereum and exploited a smart contract written in Solidity to demonstrate that our approach is highly cost-effective.
Krishna Nivash. J Krishna Nivash. J, Dr. V. Vijayakumar Dr. V. Vijayakumar
Blockchain Based Cross Network calling is an innovative decentralized peer-to-peer (P2P) communication system leveraging WebRTC and blockchain technology. This paper explores the system architecture, scalability mechanisms, and potential future enhancements. We present an in-depth analysis of its hybrid decentralized architecture, discuss the scalability challenges in P2P networks, and propose future enhancements including Layer-2 blockchain solutions, decentralized signaling, and AI integration. The findings suggest that decentralized audio communication can be significantly improved through advanced architectural designs and optimized scalability solutions. This paper provides a comprehensive exploration of Cross Network-based decentralized communication, emphasizing its implications for privacy, security, and future enhancements in Web3 applications.
Phishing is a serious threat to cryptocurrency networks; Bitcoin and Ethereum are prime targets for these attacks. This paper discusses some aspects of phishing attacks on these platforms. While the simpler architecture of Bitcoin leads to more direct phishing attempts, the more complex ecosystem in Ethereum introduces a wide range of attack vectors through dApps and smart contracts. A comparative analysis of phishing attacks in both blockchains shows that while both have their fair share of attacks, Bitcoin seems to bear the brunt of phishing attacks. Current defense strategies, like 2FA and anti-phishing tools, as well as recommendations for increasing network security against phishing are discussed in this paper. Understanding these phishing mechanisms is crucial in strengthening the security of blockchain platforms and mitigating future attacks.
The Ethereum blockchain operates as a decentralized platform, utilizing blockchain technology to distribute smart contracts across a global network. It enables currency and digital value exchange without centralized control. However, the exponential growth of online commerce has created a fertile ground for a surge in fraudulent activities such as money laundering and phishing, thereby exacerbating significant security vulnerabilities. To combat this, our article introduces an ensemble learning approach to accurately detect fraudulent Ethereum blockchain transactions. Our goal is to integrate a decision-making tool into the decentralized validation process of Ethereum, allowing blockchain miners to identify and flag fraudulent transactions. Additionally, our system can assist governmental organizations in overseeing the blockchain network and identifying fraudulent activities. Our framework incorporates various data pre-processing techniques and evaluates multiple machine learning algorithms, including logistic regression, Isolation Forest, support vector machine, Random Forest, XGBoost, and recurrent neural network. These models are fine-tuned using grid search to enhance their performance. The proposed approach utilizes an ensemble of three distinct models (Random Forest, extreme gradient boosting (XGBoost), and support vector machine) to further improve classification performance. It achieves high scores of over 98% across key classification metrics like accuracy, precision, recall, and F1-score. Moreover, the approach is suitable for real-world usage, with an inference time of 0.13 s.
This paper introduces RoSeMary, the first-of-its-kind ML/Crypto codesign watermarking framework that regulates LLM-generated code to avoid intellectual property rights violations and inappropriate misuse in software development. High-quality watermarks adhering to the detectability-fidelity-robustness tri-objective are limited due to codes' low-entropy nature. Watermark verification, however, often needs to reveal the signature and requires re-encoding new ones for code reuse, which potentially compromising the system's usability. To overcome these challenges, RoSeMary obtains high-quality watermarks by training the watermark insertion and extraction modules end-to-end to ensure (i) unaltered watermarked code functionality and (ii) enhanced detectability and robustness leveraging pre-trained CodeT5 as the insertion backbone to enlarge the code syntactic and variable rename transformation search space. In the deployment, RoSeMary uses zero-knowledge proofs for secure verification without revealing the underlying signatures. Extensive evaluations demonstrated RoSeMary achieves high detection accuracy while preserving the code functionality. RoSeMary is also robust against attacks and provides efficient secure watermark verification.
A. Bendada, Abdelaziz Amara Korba, Mouhamed Amine Bouchiha, Yacine Ghamri‐Doudane
The rapid growth of decentralized finance (DeFi) has led to the widespread use of automated agents, or bots, within blockchain ecosystems like Ethereum, Binance Smart Chain, and Solana. While these bots enhance market efficiency and liquidity, they also raise concerns due to exploitative behaviors that threaten network integrity and user trust. This paper presents a decentralized federated learning (DFL) approach for detecting financial bots within Ethereum Virtual Machine (EVM)-based blockchains. The proposed framework leverages federated learning, orchestrated through smart contracts, to detect malicious bot behavior while preserving data privacy and aligning with the decentralized nature of blockchain networks. Addressing the limitations of both centralized and rule-based approaches, our system enables each participating node to train local models on transaction history and smart contract interaction data, followed by on-chain aggregation of model updates through a permissioned consensus mechanism. This design allows the model to capture complex and evolving bot behaviors without requiring direct data sharing between nodes. Experimental results demonstrate that our DFL framework achieves high detection accuracy while maintaining scalability and robustness, providing an effective solution for bot detection across distributed blockchain networks.
The aim of this research is to raise awareness regarding cryptocurrency fraud. In this context, the study focuses on cryptocurrency investment frauds and provides an evaluation from the perspective of new media literacy. Throughout the research process, a total of 969 complaints were analyzed under the categories of "Cryptocurrency Investment Fraud" and "Cryptocurrency and Victim Complaints" on the Şikayetvar platform. Adopting an exploratory approach, the complaints were coded under various themes using content and thematic analysis methods. The analysis process was conducted using MAXQDA 24, a qualitative data analysis software. The findings reveal that the theme with the highest frequency among types of fraud is "Fake Coin/Token" (337), illustrating the strategies employed by cryptocurrency fraudsters to deceive investors through fraudulent projects and assets. Additionally, the theme "Withdrawal and Transaction Request Rejection" (159) reflects the difficulties faced by users in conducting transactions and withdrawing their funds on legitimate platforms, showcasing how fraudulent platforms delay their victims. Furthermore, scams conducted through Telegram channels (173) have garnered attention, highlighting the significant role social media platforms play in fraudulent activities. Cryptocurrency frauds underscore the deficiencies in users' new media literacy and emphasize the importance of financial literacy and new media literacy education in an environment where fraud is prevalent.
Abstract— Remote electronic voting promises increased accessibility but remains constrained by persistent challenges related to coercion in unsupervised environments, credential compromise, and the difficulty of sustaining long-term voter trust. While coercion-resistant approaches commonly rely on revoting, most existing systems treat credential loss or recovery as an administrative exception, often reintroducing identity linkage or trusted intermediaries and offering limited means for voters or observers to verify that an election unfolded as intended. This paper presents Arcaunt, a remote voting architecture that elevates anonymous credential recovery to a first-class security property and integrates it directly into the voting lifecycle. The architecture introduces an Anonymous Recovery Channel (ARC), enabling voters to revoke and replace compromised credentials without identity disclosure or reliance on administrator discretion. Recovery is logically and operationally decoupled from ballot casting. This preserves ballot secrecy and prevents temporary compromise of credentials, devices, or voter autonomy from becoming a permanent loss of voting control. Arcaunt builds on established cryptographic mechanisms, including publicly verifiable bulletin boards, commitment-based ballots, and unlinkable bearer credentials. These components provide ballot integrity and verifiability without exposing voter identities and form the foundation on which revoting, recovery, and auditability are composed. Individual assurance is provided through deferred, non-transferable verification mechanisms: voters receive a receipt at ballot submission, while verification becomes possible only after election closure, preventing real-time feedback that could enable coercion while still allowing voters to confirm that their final valid ballot was recorded and included. At the system level, integrity is enforced through an append-only, publicly auditable ledger and deterministic “last valid vote” counting rules, ensuring that administrative database access cannot alter election outcomes without detection. The architecture explicitly bounds its threat model, acknowledging limits against global traffic analysis and continuous coercion while constraining failures to be temporary and non-scalable. We analyze the security properties of the proposed system under realistic adversarial assumptions and evaluate a prototype implementation, demonstrating that anonymous recovery, coercion-resistant revoting, individual verification, and public auditability can be combined efficiently without reliance on trusted administrators or specialized hardware. Keywords—e-voting, arcaunt, anonymous recovery channel (arc), coercion resistance, sha-3, digital democracy, govtech, zero-knowledge proofs.
Zubaida Rehman, Mark Gregory, Iqbal Gondal, Hai Dong · 5 authors
This paper presents a comprehensive study on eclipse attacks in blockchain networks by describing how eclipse attacks work, their effects, detection, and prevention. In this context, understanding and controlling network-level attacks, such as eclipse attacks, is an essential task in relation to assurance and reliability for decentralized systems that utilize blockchain technology. An eclipse attack is a sequence of network-layer attacks that monopolize the connections to a target node to isolate it from the rest of the network. Eclipse attacks that focus on node discovery manipulation, can have a substantial impact on a blockchain network, by increasing transaction computation cost, transaction censorship, and consensus disruption. We studied eclipse attacks on a blockchain network. The attack vectors were associated with node discovery manipulation, network partitioning, and information flow exploitation. This paper also reviews state-of-the-art detection methods and prevention strategies, shedding light on their effectiveness and limitations. Awareness of eclipse attacks and their effect provides the motivation for further research in developing practical and resilient security measures for blockchain networks.
Iori Suzuki, Yin Minn Pa Pa, Anh Thi Van Nguyen, Katsunari Yoshioka
Decentralized Finance (DeFi) token scams have become one of the most prevalent forms of fraud in Web-3 technology, generating approximately $241.6 million in illicit revenue in 2023 [1].Detecting these scams requires analyzing both on-chain data, such as transaction records on the blockchain, and off-chain data, such as websites related to the DeFi token project and associated social media accounts.Relying solely on one type of data may fail to capture the full context of fraudulent activities.While on-chain data is publicly accessible due to the transparency inherent in blockchain technology, off-chain data often disappears alongside DeFi scam campaigns, making it difficult for the security community to study these scams.To address this challenge, we propose a dataset comprising more than 550 thousand archived web and social media data as offchain data, in addition to on-chain data related to 32,144 DeFi tokens deployed on Ethereum blockchain from September 24, 2024 to January 14, 2025.This dataset aims to support the security community in studying and detecting DeFi token scams.To illustrate its utility, our case studies demonstrated the potential of the dataset in identifying patterns and behaviors associated with scam tokens.These findings highlight the dataset's capability to provide insights into fraudulent activities and support further research in developing effective detection mechanisms.
In recent years, blockchain technology, as an innovative information technology, has received widespread attention in academia and industry. However, its limitations in mechanism design and the completeness of supporting infrastructure, combined with the immaturity of security concepts, have exposed blockchain systems to severe security threats and challenges. This study aims to address critical security issues in blockchain technology by proposing a blockchain security architecture based on Web attack principles. The architecture adopts a negotiated consensus mechanism and integrates real-time protection techniques from the field of cybersecurity, designing an innovative framework capable of identifying and restricting malicious nodes. With dynamic isolation as its core strategy, the architecture detects abnormal behaviors and temporarily isolates malicious nodes, preventing further damage to the blockchain network. The results demonstrate that this architecture successfully addresses the bottlenecks of inadequate targeted defense in existing blockchain systems and significantly improves operational efficiency and security. Experimental validation indicates that the architecture exhibits substantial practical value in scenarios such as decentralized finance (DeFi) and supply chain management, laying a solid foundation for the widespread application of blockchain technology in real-world settings.
Online voting promises greater convenience and accessibility, but moving from supervised polling places to unsupervised settings magnifies the risk of coercion and vote buying. A compelling strategy is to give voters fake credentials: credentials that look and behave like real voting credentials but whose ballots are silently excluded from the tally. Despite its conceptual appeal, practical realizations and usability evidence for fake credentials have remained limited. This dissertation presents Votegral, the first end-to-end verifiable, coercion-resistant online voting system with empirical evidence towards practical usability. Votegral has two components: TRIP and VLT. TRIP is a trust-limited, in-person registration scheme that issues voters a real credential and any number of fake credentials on paper, without trusted hardware. TRIP embeds an interactive zero-knowledge proof into the physical printing process so that real credentials carry sound proof transcripts while fake credentials carry identically formatted but unsound proof transcripts -- distinguishable only by the voter during issuance and not transferable thereafter. VLT is a tallying scheme that constrains ballots to registrar-issued credentials to enable linear-time filtering of fake ballots. VLT also introduces standing votes: a voter facing extreme coercion can, at registration, delegate their voting rights to a publicly registered political party and leave the booth with only fake credentials. Tallying then credits the party's ballot by the number of such delegations and publishes publicly auditable proofs, resulting in both transparency and coercion evidence -- evidence that an aggregate number of voters felt unsafe to leave the registrar with a real credential. Our prototype tallies 1 million ballots in about 14 hours on a 128 core, 256 GB RAM machine; this puts Votegral on par with modern end-to-end verifiable systems such as Swiss Post, while significantly outperforming prior JCJ-style systems such as Civitas. TRIP's end-to-end, voter-observable registration session completes in under 20 seconds on resource-constrained hardware. In our main user study with 150 demographically diverse participants recruited in Boston, Massachusetts, 83% successfully registered and cast a ballot in our mock election. Among the 120 participants exposed to fake credentials, 96% correctly understood the purpose of fake credentials. These promising results suggest a path for practical viability of coercion-resistant, end-to-end verifiable online voting using fake credentials.
The decentralized, transparent, and immutable ledger system of blockchain has fundamentally changed data security and digital transactions. Blockchain has built-in security safeguards, yet it is still vulnerable to flaws and attacks. In this review paper, the authors will examine the threats and vulnerabilities that blockchain technology faces and the mitigation factors that can be used to overcome these issues. The authors discuss the significant threats like the 51% attack, double spending attack and many more that compromise the integrity of blockchain technology further authors discusses the vulnerabilities that are present in consensus mechanisms, smart contracts, network level, cryptography and privacy. These vulnerabilities expose blockchain networks to potential exploits and operational risks. To overcome these threats and challenges, the paper also discusses several countermeasures that are used for strengthening the blockchain network. It includes consensus mechanism enhancement through hybrid models and enhancing network-level protection against DDoS and routing attacks. This paper also discusses about the significance of quantum resistance cryptographic algorithms, privacy-enhancing technologies like zero-knowledge proofs, and scalability solutions such as layer 2 protocols and sidechains. This review paper also includes the current research and advancements in security blocks and provides a detailed understanding of the present work and future initiatives in the blockchain system.