As the field of Web3 continues its rapid expansion, the security of Web3 authentication, often the gateway to various Web3 applications, becomes increasingly crucial. Despite its widespread use as a login method by numerous Web3 applications, the security risks of Web3 authentication have not received much attention. This paper investigates the vulnerabilities in the Web3 authentication process and proposes a new type of attack, dubbed blind message attacks. In blind message attacks, attackers trick users into blindly signing messages from target applications by exploiting users' inability to verify the source of messages, thereby achieving unauthorized access to the target application. We have developed Web3AuthChecker, a dynamic detection tool that interacts with Web3 authentication-related APIs to identify vulnerabilities. Our evaluation of real-world Web3 applications shows that a staggering 75.8% (22/29) of Web3 authentication deployments are at risk of blind message attacks. In response to this alarming situation, we implemented Web3AuthGuard on the open-source wallet MetaMask to alert users of potential attacks. Our evaluation results show that Web3AuthGuard can successfully raise alerts in 80% of the tested Web3 authentications. We have responsibly reported our findings to vulnerable websites and have been assigned two CVE IDs.
The rise of cryptocurrencies has created new avenues for criminal money exchanges. Among various techniques, Bitcoin address clustering plays a crucial role in detecting and grouping addresses owned by the same entity. This fundamental step is essential for deanonymizing addresses and analyzing the flow of funds in the blockchain. This advancement contributes to the battle against illicit commerce, money laundering, fraud, scams, and similar activities. In this paper, we introduce two new heuristics, NSS and PEKET. NSS leverages Bitcoin non-standard scripts, while PEKET exploits the re-use of public keys to establish connections controlled by the same entity. Our contributions encompass (i) the detailed explanation of these two novel methods; (ii) the open-source publication of the tools we developed; and, (iii) the assessment of these heuristics using a proprietary extensive dataset of labeled addresses, which achieve precision levels of 1.0 and 0.979 respectively.
The role played by email communication in our lives nowadays has been such a tremendous one especially when it comes to fast exchange of information. Nevertheless, this convenience is marred by the omnipresent threat of email spam that not only disrupts channels of communication but also present serious security and privacy concerns. Traditional models of spam detection which are based on rules or heuristics tend to fail because they do not adapt quickly enough to the new techniques employed by spammers. In response to these challenges, this paper proposes an inventive solution to the problem—integration of blockchain technology into the process of detecting email spams.Email spam is often defined as an unwanted and usually malicious form of correspondence, thus it has continued being a notable cyber security worry. The conventional mechanisms for discovering them are prone to false positives and negatives at times. Additionally, such systems have centralized data which can be interfered with and accessed without permission. Weighing up the limitations inherent in existing methods, this research examines how blockchain may change email spam detection. Keywords— Blockchain technology, ethereum, Spam, email
Madhu B K, D Sowmya, N Spoorthi, Umme Kulsum · 5 authors
In this technology counterfeiting is very common and dangerous, also another consequence of counterfeiting is that a company’s reputation suffers. There are several methods such as RFID tags artificial intelligence blockchain and QR based systems etc. In our survey paper we are focusing mainly on blockchain techonlogy. Blockchain typically managed by peer-to-peer computer network for use as a public distributed ledger. The blockchain technology ensures identification and traceability of original product through the supply chain
Public and permissionless blockchain systems are challenged by Sybil attacks, in which attackers use multiple identities to gain control.Traditionally, such attacks are prevented by consensus mechanisms relying on resource expenditure.However, such mechanisms (e.g.proof of work) face criticism for being wasteful.To address this and other concerns, novel blockchain systems backed by new consensus mechanisms have recently emerged.We formalise three key characteristics pursued by these systems: permissionlessness, Sybil attack resistance, and freeness.We demonstrate that no blockchain protocol can simultaneously achieve all three characteristics within the paradigm established by our formalisation.Thus, a trilemma emerges for distributed ledger technology designers, who must balance these characteristics thoughtfully.
The closed architecture of prevailing blockchain systems renders the usage of this technology mostly infeasible for a wide range of real-world problems. Most blockchains trap users and applications in their isolated space without the possibility of cooperating or switching to other blockchains. Therefore, blockchains need additional mechanisms for seamless communication and arbitrary data exchange between each other and external systems. Unfortunately, current approaches for cross-blockchain communication are resource-intensive or require additional blockchains or tailored solutions depending on the applied consensus mechanisms of the connected blockchains. Therefore, we propose an oracle with an off-chain aggregation mechanism based on Zero-Knowledge Succinct Non-interactive Arguments of Knowledge (zk-SNARKs) to facilitate cross-blockchain communication. The oracle queries data from another blockchain and applies a rollup-like mechanism to move state and computation off-chain. The zkOracle contract only expects the transferred data, an updated state root, and proof of the correct execution of the aggregation mechanism. The proposed solution only requires constant 378 kgas to submit data on the Ethereum blockchain and is primarily independent of the underlying technology of the queried blockchains.
Cryptocurrency wallets come in various forms, each with unique usability and security features. Through interviews with 24 users, we explore reasons for selecting wallets in different contexts. Participants opt for smart contract wallets to simplify key management, leveraging social interactions. However, they prefer personal devices over individuals as guardians to avoid social cybersecurity concerns in managing guardian relationships. When engaging in high-stakes or complex transactions, they often choose browser-based wallets, leveraging third-party security extensions. For simpler transactions, they prefer the convenience of mobile wallets. Many participants avoid hardware wallets due to usability issues and security concerns with respect to key recovery service provided by manufacturer and phishing attacks. Social networks play a dual role: participants seek security advice from friends, but also express security concerns in soliciting this help. We offer novel insights into how and why users adopt specific wallets. We also discuss design recommendations for future wallet technologies based on our findings.
The advent of Web3 technologies promises unprecedented levels of user control and autonomy. However, this decentralization shifts the burden of security onto the users, making it crucial to understand their security behaviors and perceptions. To address this, our study introduces a comprehensive framework that identifies four core components of user interaction within the Web3 ecosystem: blockchain infrastructures, Web3-based Decentralized Applications (DApps), online communities, and off-chain cryptocurrency platforms. We delve into the security concerns perceived by users in each of these components and analyze the mitigation strategies they employ, ranging from risk assessment and aversion to diversification and acceptance. We further discuss the landscape of both technical and human-induced security risks in the Web3 ecosystem, identify the unique security differences between Web2 and Web3, and highlight key challenges that render users vulnerable, to provide implications for security design in Web3.
Language models (LMs) have demonstrated superior performance in detecting fraudulent activities on Blockchains. Nonetheless, the sheer volume of Blockchain data results in excessive memory and computational costs when training LMs from scratch, limiting their capabilities to large-scale applications. In this paper, we present ZipZap, a framework tailored to achieve both parameter and computational efficiency when training LMs on large-scale transaction data. First, with the frequency-aware compression, an LM can be compressed down to a mere 7.5% of its initial size with an imperceptible performance dip. This technique correlates the embedding dimension of an address with its occurrence frequency in the dataset, motivated by the observation that embeddings of low-frequency addresses are insufficiently trained and thus negating the need for a uniformly large dimension for knowledge representation. Second, ZipZap accelerates the speed through the asymmetric training paradigm: It performs transaction dropping and cross-layer parameter-sharing to expedite the pre-training process, while revert to the standard training paradigm for fine-tuning to strike a balance between efficiency and efficacy, motivated by the observation that the optimization goals of pre-training and fine-tuning are inconsistent. Evaluations on real-world, large-scale datasets demonstrate that ZipZap delivers notable parameter and computational efficiency improvements for training LMs. Our implementation is available at: https://github.com/git-disl/ZipZap.
Peer-to-Peer (P2P) cryptocurrency exchanges are two-sided marketplaces, similar to eBay, where individuals can offer to sell cryptocurrencies in exchange for payment. Due to disintermediation, these marketplaces trade off increased privacy for higher risk (e.g., scams/fraud). Although these marketplaces use feedback systems to encourage healthier transactions, anecdotal evidence suggests that feedback often fails to capture vendor-associated risks. This work documents the online safety of cryptocurrency P2P marketplaces, identifies underlying issues in feedback-based reputation systems, and proposes improved mechanisms for predicting/monitoring risky accounts. We collect data from two cryptocurrency marketplaces, Paxful and LocalCoinSwap (LCS) for 12 months (06/2022--06/2023). The data includes over 396,000 listings, 67,000 vendors, and 4.7 million feedback for Paxful; and about 52,000 listings, 14,000 users, and 146,000 feedback for LCS.First, we show that the current feedback system does not sufficiently convey enough information about risky vendors, and is susceptible to reputation manipulation through user collusion and automation. Second, combining various publicly available information, we build machine learning models to predict account suspension, and achieve a 0.86 F1-score and 0.93 AUC for Paxful. Third, while our models appear to have limited transferability across markets, we identify which features most help account suspension across platforms. Finally, we perform a month-long online evaluation to show that our models are significantly more successful than mere feedback-based reputation schemes at predicting which users will be suspended in the future.
Krishnendu Chatterjee, A. Ebrahimzadeh, Mehrdad Karrabi, Krzysztof Pietrzak · 6 authors
We study selfish mining attacks in longest-chain blockchains like Bitcoin, but where the proof of work is replaced with efficient proof systems -- like proofs of stake or proofs of space -- and consider the problem of computing an optimal selfish mining attack which maximizes expected relative revenue of the adversary, thus minimizing the chain quality. To this end, we propose a novel selfish mining attack that aims to maximize this objective and formally model the attack as a Markov decision process (MDP). We then present a formal analysis procedure which computes an $ε$-tight lower bound on the optimal expected relative revenue in the MDP and a strategy that achieves this $ε$-tight lower bound, where $ε>0$ may be any specified precision. Our analysis is fully automated and provides formal guarantees on the correctness. We evaluate our selfish mining attack and observe that it achieves superior expected relative revenue compared to two considered baselines. In concurrent work [Sarenche FC'24] does an automated analysis on selfish mining in predictable longest-chain blockchains based on efficient proof systems. Predictable means the randomness for the challenges is fixed for many blocks (as used e.g., in Ouroboros), while we consider unpredictable (Bitcoin-like) chains where the challenge is derived from the previous block.
Bruno Ramos-Cruz, Javier Andreu-Pérez, Francisco J. Quesada, Luis Martı́nez
Blockchain technology has become a trusted method for establishing secure and transparent transactions through a distributed, encrypted network. The operation of blockchain is governed by consensus algorithms, among which Proof of Stake (PoS) is popular yet has its drawbacks, notably the potential for centralising power in nodes with larger stakes or higher rewards. Our proposed novel solution, Fuzzychain, leverages fuzzy sets to define stake semantics, introducing a degree of softness in validator selection. This approach mitigates rigid threshold-based decision-making by allowing gradual transitions between stake levels, reducing sharp disparities among validators. By incorporating this enhanced stake evaluation, Fuzzychain promotes a more adaptive and distributed selection process, ensuring a fairer and more inclusive blockchain network. A thorough assessment of a real-time multi-agent blockchain system to examine validator selection and reduce inequality, promoting a more equitable distribution of stakes among validators compared to other consensus mechanisms. This fosters a more inclusive selection process and a more equitably distributed network.
Although smart contracts are immutable once they are deployed, the reality is that they need upgrades to fix bugs or add new features. Nowadays, there are a few upgrade methods in Ethereum, some of which can change the contract without changing the contract address that users interact with. This upgrade way increases potential danger and results in users' distrust, because it may secretly change the function of the contract and cause users financial loss. We examine two of these upgrade methods, i.e., proxy pattern and metamorphic contract. For the proxy pattern, we propose a bytecode-based method for detecting these upgradeable contracts, which achieves a 99.37% F1-score. We use the bytecode-based method to detect the contracts in the first 12 million blocks of Ethereum and find 126,500 upgradeable contracts. For the metamorphic contracts, we employ an Ethereum replay tool to replay the transactions and find the metamorphic contracts according to the SELFDESTRUCT and CREATE2 instructions. We find that 64.3% of the contracts upgraded using this way are malicious MEV bots. Finally, we summarize the reasons for smart contract upgrades and make development recommendations.
Currently, smart contract vulnerabilities (SCVs) have emerged as a major factor threatening the transaction security of blockchain. Existing state-of-the-art methods rely on deep learning to mitigate this threat. They treat each input contract as an independent entity and feed it into a deep learning model to learn vulnerability patterns by fitting vulnerability labels. It is a pity that they disregard the correlation between contracts, failing to consider the commonalities between contracts of the same type and the differences among contracts of different types. As a result, the performance of these methods falls short of the desired level. To tackle this problem, we propose a novel Contrastive Learning Enhanced Automated Recognition Approach for Smart Contract Vulnerabilities, named Clear. In particular, Clear employs a contrastive learning (CL) model to capture the fine-grained correlation information among contracts and generates correlation labels based on the relationships between contracts to guide the training process of the CL model. Finally, it combines the correlation and the semantic information of the contract to detect SCVs. Through an empirical evaluation of a large-scale real-world dataset of over 40K smart contracts and compare 13 state-of-the-art baseline methods. We show that Clear achieves (1) optimal performance over all baseline methods; (2) 9.73%-39.99% higher F1-score than existing deep learning methods.
Fictitious products have emerged as a substantial challenge in the manufacturing sector, inflicting adverse consequences on a company's financial health, reputation, and overall prosperity. Thankfully, blockchain technology provides an effective remedy for discerning fake items and verifying the legitimacy of authentic ones, all within a decentralized and widely distributed digital ledger. Quick Response (QR) codes serve as pivotal tools in the fight against fictitious goods, as each product is now furnished with a QR code that acts as a direct bridge to the blockchain system, essentially bestowing each item with a digital identification card. QR code scanners communicate with the blockchain to promptly validate whether a product is genuine or fictitious. Moreover, customers can harness this technology to track a product's journey through the supply chain and authenticate ownership details, akin to a digital breadcrumb trail that securely preserves product information and unique codes as database blocks. Considering the globalized business landscape and the perpetual advancements in technology, industrial manufacturers and distributors are wholeheartedly committed to optimizing their supply chain processes, ensuring they remain one step ahead of fictitious product proliferation and fortifying their operations against the dissemination of spurious items. In this paper we compare Different Fake product identification Methods like Barcodes, QR Codes, RFID Tags, Serial numbers with methods using Blockchain technology which is having high security, transferability & traceability throughout Supply Chain.
Web services that use a blockchain and crypto-assets (Web3 services) improve user privacy by anonymous logins using wallet addresses. However, since many users list their account identities (IDs) on social networking service (SNS) profile pages and reuse their account IDs for self-branding and curation purposes, which increases the risk of de-anonymization on Web3 services by linking these accounts. If such high-risk SNS accounts hold large amounts of crypto-assets, they are subject to account hijacking and spoofing attacks for financial gain. In this study, we proposed a method to discover highly relevant SNS accounts from a seed account on Web2 and Web3 SNSs and estimate their account ownership. We applied our method to 480 seed accounts of 9 different SNSs and discovered 1,233 new accounts. We found that SNSs with multiple URL input forms on their profile setting pages linked more accounts and revealed that 207 out of 253 (81.8%) users reused their IDs across different SNSs. We identified 26 accounts linked to personal and crypto-asset information that are at risk of de-anonymization. Our user study using crowdsourcing services showed that as many as 232 (40.8%) out of 568 respondents do not understand the traceability of blockchain transaction histories. We examined the security and privacy risks caused by account listing and ID reuse, and made recommendations for service providers and users based on our findings.
Cryptocurrencies have experienced a surge in popularity, paralleled by an increase in phishing scams exploiting their transactional networks. Therefore, detecting anomalous transactions in the complex structure of cryptocurrency transaction data and the imbalance between legitimate and fraudulent data is considered a very important task. To this end, we introduce a model specifically designed for scam detection within the Ethereum network, focusing on its capability to process long and complex transaction graphs. Our method, Deep Graph traversal based on Transformer for Scam Detection (DGTSD), employs the DeepWalk algorithm to traverse extensive graph structures and a Transformer-based classifier to analyze intricate node relationships within these graphs. The necessity for such an approach arises from the inherent complexity and vastness of Ethereum transaction data, which traditional techniques struggle to process effectively. DGTSD applies subgraph sampling to manage this complexity, targeting significant portions of the network for detailed analysis. Then, it leverages the multi-head attention mechanism of the Transformer model to effectively learn and analyze complex patterns and relationships within the Ethereum transaction graph to identify fraudulent activity more accurately. Our experiments with other models demonstrate the superiority of this model over traditional methods in performance, with an F1 score of 0.9354. By focusing on the challenging aspects of Ethereum’s transaction network, such as its size and intricate connections, DGTSD presents a robust solution for identifying fraudulent activities, significantly contributing to the enhancement of blockchain security.
Reentrancy, a notorious vulnerability in smart contracts, has led to millions of dollars in financial loss. However, current smart contract vulnerability detection tools suffer from a high false positive rate in identifying contracts with reentrancy vulnerabilities. Moreover, only a small portion of the detected reentrant contracts can actually be exploited by hackers, making these tools less effective in securing the Ethereum ecosystem in practice. In this paper, we propose BlockWatchdog, a tool that focuses on detecting reentrancy vulnerabilities by identifying attacker contracts. These attacker contracts are deployed by hackers to exploit vulnerable contracts automatically. By focusing on attacker contracts, BlockWatchdog effectively detects truly exploitable reentrancy vulnerabilities by identifying reentrant call flow. Additionally, BlockWatchdog is capable of detecting new types of reentrancy vulnerabilities caused by poor designs when using ERC tokens or user-defined interfaces, which cannot be detected by current rule-based tools. We implement BlockWatchdog using cross-contract static dataflow techniques based on attack logic obtained from an empirical study that analyzes attacker contracts from 281 attack incidents. BlockWatchdog is evaluated on 421,889 Ethereum contract bytecodes and identifies 113 attacker contracts that target 159 victim contracts, leading to the theft of Ether and tokens valued at approximately 908.6 million USD. Notably, only 18 of the identified 159 victim contracts can be reported by current reentrancy detection tools.
Sulemana Awal, Diyawu Mumin, Arnold Mashud Abukari, Abukari Aziz Danaa · 5 authors
Online services currently rely heavily on centralized authentication methods to manage user identification and authentication. However, these methods are vulnerable to account hacking, which can compromise user data and lead to attacks. A potential solution to this issue is the use of distributed ledger technology, such as blockchain, to decentralize credential ownership and provide a secure, immutable platform for verifying user identity. This paper aims to analyze the drawbacks of centralized authentication systems and propose an alternative that uses blockchain technology for authentication, ensuring robustness, transparency, and security. The proposed system is tested on web applications using the Ethereum testnet and an authentication provider (backend server).
The core of many cryptocurrencies is the decentralised validation network operating on proof-of-work technology. In these systems, validation is done by so-called miners who can digitally sign blocks once they solve a computationally-hard problem. Conventional wisdom generally considers this protocol as secure and stable as miners are incentivised to follow the behaviour of the majority. However, whether some strategic mining behaviours occur in practice is still a major concern. In this paper we target this question by focusing on a security threat: a selfish mining attack in which malicious miners deviate from protocol by not immediately revealing their newly mined blocks. We propose a statistical test to analyse each miner's behaviour in five popular cryptocurrencies: Bitcoin, Litecoin, Monacoin, Ethereum and Bitcoin Cash. Our method is based on the realisation that selfish mining behaviour will cause identifiable anomalies in the statistics of miner's successive blocks discovery. Secondly, we apply heuristics-based address clustering to improve the detectability of this kind of behaviour. We find a marked presence of abnormal miners in Monacoin and Bitcoin Cash, and, to a lesser extent, in Ethereum. Finally, we extend our method to detect coordinated selfish mining attacks, finding mining cartels in Monacoin where miners might secretly share information about newly mined blocks in advance. Our analysis contributes to the research on security in cryptocurrency systems by providing the first empirical evidence that the aforementioned strategic mining behaviours do take place in practice.
Open access
Blockchain Technology Applications and Security
Spam and Phishing Detection
Advanced Steganography and Watermarking Techniques
With the emergence of blockchain technology, the cryptocurrency market has experienced significant growth in recent years, simultaneously fostering environments conducive to cybercrimes such as phishing scams. Phishing scams on blockchain platforms like Ethereum have become a grave economic threat. Consequently, there is a pressing demand for effective detection mechanisms for these phishing activities to establish a secure financial transaction environment. However, existing methods typically utilize only the most recent transaction record when constructing features, resulting in the loss of vast amounts of transaction data and failing to adequately reflect the characteristics of nodes. Addressing this need, this study introduces a multiscale feature fusion approach integrated with a graph convolutional network model to detect phishing scams on Ethereum. A node basic feature set comprising 12 features is initially designed based on the Ethereum transaction dataset in the basic feature module. Subsequently, in the edge embedding representation module, all transaction times and amounts between two nodes are sorted, and a gate recurrent unit (GRU) neural network is employed to capture the temporal features within this transaction sequence, generating a fixed-length edge embedding representation from variable-length input. In the time trading feature module, attention weights are allocated to all embedding representations surrounding a node, aggregating the edge embedding representations and structural relationships into the node. Finally, combining basic and time trading features of the node, graph convolutional networks (GCNs), SAGEConv, and graph attention networks (GATs) are utilized to classify phishing nodes. The performance of these three graph convolution-based deep learning models is validated on a real Ethereum phishing scam dataset, demonstrating commendable efficiency. Among these, SAGEConv achieves an F1-score of 0.958, an AUC-ROC value of 0.956, and an AUC-PR value of 0.949, outperforming existing methods and baseline models.
Crowdfunding is an online fundraising method whereby individuals contribute small amounts to support the startup projects or businesses of young entrepreneurs. The emergence of crowdfunding sites has improved the financing process. Individuals, business owners, and innovators can now reach a worldwide audience to raise money for their ideas. In addition to examining the emergence, important models, and societal impact of crowdfunding platforms, this abstract offers a succinct summary of their many facets. Building confidence between donors and recipients is important, especially in light of the increasing number of scams. Donating to unknown parties generally scares people since they don't know if their money will go towards the appropriate causes or not. Thus, the development of a safe crowdfunding platform is required. Blockchain technology has revolutionized a number of industries by bringing previously unheard-of levels of security, transparency, and decentralization. The aim of this research paper is to examine how blockchain technology integration might improve transparency, build confidence between project creators and backers, and solve current issues with crowdfunding platforms. The decentralized ledger technology of blockchain guarantees the security, accountability, and transparency of project creators and backers. This presentation concludes by outlining the revolutionary potential of crowdfunding platforms to change financial dynamics and promote inclusive financing process involvement.
In this work, we review existing cryptocurrency wallet solutions with regard to authentication methods and factors from the user's point of view. In particular, we distinguish between authentication factors that are verified against the blockchain and the ones verified locally (or against a centralized party). With this in mind, we define notions for $k-factor$ authentication against the blockchain and $k-factor$ authentication against the authentication factors. Based on these notions, we propose a classification of authentication schemes. We extend our classification to accommodate the threshold signatures and signing transactions by centralized parties (such as exchanges or co-signing services). Finally, we apply our classification to existing wallet solutions, which we compare based on various security and key-management features.
Open access
2 source records
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Cyber Threat Intelligence (CTI) has become increasingly important in safeguarding organizations against cyber threats. However, managing, storing, analyzing, and sharing vast and sensitive threat intelligence data is a challenge. Blockchain technology, with its robust and tamper-resistant properties, offers a promising solution to address these challenges. This systematic literature review explores the recent advancements and emerging trends at the intersection of CTI and blockchain technology. We reviewed research papers published during the last 5 years to investigate the various proposals, methodologies, models, and implementations related to the distributed ledger technology and how this technology can be used to collect, store, analyze, and share CTI in a secured and controlled manner, as well as how this combination can further support additional dimensions such as quality assurance, reputation, and trust. Our findings highlight the focus of the CTI and blockchain convergence on the dissemination phase in the CTI lifecycle, reflecting a substantial emphasis on optimizing the efficacy of communication and sharing mechanisms, based on an equitable emphasis on both permissioned, private blockchains and permissionless, public blockchains, addressing the diverse requirements and preferences within the CTI community. The analysis reveals a focus towards the tactical and technical dimensions of CTI, compared to the operational and strategic CTI levels, indicating an emphasis on more technical-oriented utilization within the domain of blockchain technology. The technological landscape supporting CTI and blockchain integration emerges as multifaceted, featuring pivotal roles played by smart contracts, machine learning, federated learning, consensus algorithms, IPFS, deep learning, and encryption. This integration of diverse technologies contributes to the robustness and adaptability of the proposed frameworks. Moreover, our exploration unveils the overarching significance of trust and privacy as predominant themes, underscoring their pivotal roles in shaping the landscape within our research realm. Additionally, our study addresses the maturity assessment of these integrated systems. The approach taken in evaluating maturity levels, distributed across the Technology Readiness Level (TRL) scale, reveals an average balance, indicating that research efforts span from early to mid-stages of maturity in implementation. This study signifies the ongoing evolution and maturation of research endeavors within the dynamic intersection of CTI and blockchain technology, identifies trends, and also highlights research gaps that can potentially be addressed by future research on the field.