The objective of this research is to determine the impact of geopolitical developments on Bitcoin's value. It focuses on the events that occurred from October 7, 2023 including the attack on Israel by the militant group Hamas, the tension between Iran and Israel, and the conflict between Palestine and the US. Through a comprehensive event study, we can analyze the returns generated by these events. The results of the study Srevealed that Bitcoin performed well during the adjustment and anticipation periods, which showed that it could be a safe-haven asset. On the other hand, the negative AAR during the event day reflected the market's first reaction. The study also highlighted Bitcoin's dual nature as a speculative asset and a safe-haven asset providing investors with a deeper understanding of the risks that affect the cryptocurrency market.
Zhibo Wang, Liu Guoming, Hongzhen Xu, Shengyu You · 6 authors
Smart contracts play an essential role in the handling and management of digital assets, where vulnerabilities can lead to severe security issues and financial losses. Current detection techniques are largely limited to identifying single vulnerabilities and lack comprehensive identification capabilities for multiple vulnerabilities that may coexist in smart contracts. To address this challenge, we propose a novel multi-label vulnerability detection model that integrates extractive summarization methods with deep learning, referred to as Ext-ttg. The model begins by preprocessing the data using an extractive summarization approach, followed by the deployment of a custom-built deep learning model to detect vulnerabilities in smart contracts. Experimental results demonstrate that our method achieves commendable performance across various metrics, establishing the effectiveness of the proposed approach in the multi-vulnerability detection tasks within smart contracts.
Kriptovalute su digitalne ili virtualne valute koje koriste kriptografiju za sigurnost i funkcioniraju na decentraliziranim mrežama baziranim na blockchain tehnologiji. Osnovna ideja iza kriptovaluta je omogućiti sigurno, transparentno i decentralizirano vođenje financijskih transakcija bez potrebe za posrednicima poput banaka ili financijskih institucija. Bitcoin, najpoznatija kriptovaluta koja je predstavljena 2009. godine, kreirao je anonimni pojedinac ili grupa pod pseudonimom Satoshi Nakamoto. Bitcoin novčanici čuvaju privatne ključeve koji omogućavaju pristup i upravljanje Bitcoinima. Ako je datoteka novčanika izgubljena ili obrisana, a sigurnosna kopija nije napravljena, Bitcoini postaju nedostupni. Privatni ključevi su ključni za pristup sredstvima, a njihovo otkrivanje može predstavljati veliki rizik. Rudarenje Bitcoina je proces koji služi za potvrđivanje transakcija i dodavanje novih blokova u blockchain. Svakih deset minuta kreira se novi blok, a rudari rješavaju složene matematičke probleme kako bi potvrdili transakcije. Ovaj sistem osigurava kronološki redoslijed transakcija i štiti mrežu od manipulacija. Sistem rudarenja također funkcionira kao neka vrsta konkurentne lutrije, gdje nijedna osoba ne može lako manipulirati blok-lancem ili povući svoje transakcije. Proces kreiranja bloka zahtijeva značajnu kompjutersku snagu i vrijeme, čime se osigurava transparentnost i integritet mreže. Predmet ovog rada je istraživanje kriptovaluta i tržišta kriptovaluta, s fokusom na njihovu tehnologiju, volatilnost, regulaciju i primjenu u globalnom financijskom sustavu. Cilj rada je pružiti sveobuhvatan pregled kako kriptovalute funkcioniraju, analizirati faktore koji utječu na njihovu nestabilnost i rizik, te ispitati trenutne regulatorne pristupe širom svijeta. Pored toga, rad će istražiti potencijalne koristi i izazove primjene blockchain tehnologije u različitim sektorima. Metode rada u ovom istraživanju uključuju teorijsku analizu i sintezu, deskriptivnu metodu i metodu komparacije.
The use of smart contracts in areas such as finance, supply chain management, and the Internet of Things has significantly advanced blockchain technology. However, once deployed on the blockchain, smart contracts cannot be modified or revoked. Any vulnerabilities can lead to severe economic losses and data breaches, making pre-deployment vulnerability detection critically important. Traditional smart contract vulnerability detection methods suffer from low accuracy and limited reusability across different scenarios. To enhance detection capabilities, this paper proposes a smart contract vulnerability detection method based on heterogeneous contract semantic graphs and pre-training techniques. Compared to the conventional graph structures used in existing methods, heterogeneous contract semantic graphs contain richer contract information. By integrating these with pre-trained models, our method exhibits stronger vulnerability capture and generalization capabilities. Experimental results show that this method has improved the accuracy, recall, precision, and F1 value in the detection of four widely existing and harmful smart contract vulnerabilities compared with existing methods, which greatly improves the detection ability of smart contract vulnerabilities.
Ljudi danas sve češće traže načine kako izaći iz svojih rutina i unijeti promjene. Svijet se dugo vremena fokusirao na dionice i obveznice, no one više nisu vrlo inovativne i postale su opterećujuće. Prije nekoliko godina pojavilo se nešto što je fasciniralo ljude diljem svijeta: kriptovalute. One su radikalno promijenile financijski krajolik. Međutim, također su pokrenule niz pitanja, poput toga kako im financijski pristupiti. Mnoge zemlje još uvijek nisu sigurne kako najbolje oporezivati i kategorizirati kriptovalute. U ovom radu se analizira trenutačno stanje i uloga kriptovaluta u financijskom sustavu Hrvatske. U radu se pruža pregled tehnoloških napredaka koji su doprinijeli razvoju i korištenju kriptovaluta poput Bitcoina i Ethereuma, kao i njihovoj sve većoj prisutnosti u privatnim i poslovnim transakcijama. Unatoč nedostatku sveobuhvatne zakonske regulacije, interes pojedinaca i poduzeća za kriptovalute raste. U istraživanju se također analizira pravno okruženje, prihvaćenost kriptovaluta među korisnicima te njihov utjecaj na financijsku industriju i cjelokupno gospodarstvo Hrvatske. Studija ispituje potencijalne propise za budući rast ovog sektora te pruža uvid u prednosti i izazove koje kriptovalute donose u hrvatski financijski sustav. Zaključno, provedeno je istraživanje koje će dati opširniji uvid u stvarno poznavanje kriptovaluta i na koji način bi se one mogle više koristit.
Cedrick Agorbia-Atta, Imande Atalor, Rita Korkor Agyei, Richard Nachinaba
This study addresses the critical issue of terrorist financing through cryptocurrency platforms, a growing concern due to digital currencies' pseudonymous nature and global reach. The research explores the strategic role of Artificial Intelligence (AI) and Machine Learning (ML) in identifying, preventing, and disrupting the flow of illicit funds used to finance terrorism. Employing a mixed-methods approach, the study integrates qualitative case studies of documented instances of cryptocurrency-based terrorist financing with quantitative data analysis from significant cryptocurrency exchanges. Advanced AI and ML algorithms, including supervised learning models such as decision trees and neural networks, were applied to detect suspicious transactions indicative of terrorist activities. The findings reveal that AI and ML technologies significantly enhance the ability to identify patterns of terrorist financing within large and complex datasets, with models achieving precision and recall rates exceeding 90%. However, challenges remain, particularly regarding the quality and standardization of data across platforms, algorithmic biases, and the need for continuous updates to counter evolving tactics used by terrorist organizations. The study concludes that AI and ML present powerful tools for enhancing financial security. However, their successful implementation requires overcoming these challenges through collaborative efforts among stakeholders, including financial institutions, regulators, and technology providers. This research contributes to the growing field of economic crime prevention by offering a robust framework for integrating AI-driven solutions into the fight against terrorist financing on cryptocurrency platforms.
We examine cryptocurrency fraud cases prosecuted by Nigeria's Economic and Financial Crimes Commission (EFCC). We considered the lens of the Space Transition Theory (STT) in exploring the dynamics of these digital crimes. Our data analysis reveals common types of fraud, including cryptocurrency investment schemes. The results show an exclusive male demographic (100%), with the majority under 30 years old and only a quarter possessing a degree, providing insights into the socio-demographic characteristics of cryptocurrency fraudsters. Additionally, while most fraudsters (55%) targeted victims in the United States, Bitcoin, leveraging blockchain technology, was the most commonly used method (46%) for cryptocurrency fraud. Our examination of the methods and mediums used for cryptocurrency fraud supports some aspects of STT, while others do not. We advocate for a multifaceted strategy that prioritises stringent regulation, implementation, and heightened scrutiny of digital currency ecosystems in Nigeria and beyond. This study contributes to the broader discourse on cybercrime prevention and enforcement by emphasising the novel methodological approach utilised.
Abstract To address the challenges of internal security policy compliance and dynamic threat response in organizations, we present a novel framework that integrates artificial intelligence (AI), blockchain, and smart contracts. We propose a system that automates the enforcement of security policies, reducing manual effort and potential human error. Utilizing AI, we can analyse cyber threat intelligence rapidly, identify non-compliances and automatically adjust cyber defence mechanisms. Blockchain technology provides an immutable ledger for transparent logging of compliance actions, while smart contracts ensure uniform application of security measures. The framework’s effectiveness is demonstrated through simulations, showing improvements in compliance enforcement rates and response times compared to traditional methods. Ultimately, our approach provides for a scalable solution for managing complex security policies, reducing costs and enhancing the efficiency while achieving compliance. Finally, we discuss practical implications and propose future research directions to further refine the system and address implementation challenges.
Move, a programming language for smart contracts, stands out for its focus on security. However, the practical security efficacy of Move contracts remains an open question. This work conducts the first comprehensive empirical study on the security of Move contracts. Our initial step involves collaborating with a security company to manually audit 652 contracts from 92 Move projects. This process reveals eight types of defects, with half previously unreported. These defects present potential security risks, cause functional flaws, mislead users, or waste computational resources. To further evaluate the prevalence of these defects in real-world Move contracts, we present MoveScan, an automated analysis framework that translates bytecode into an intermediate representation (IR), extracts essential meta-information, and detects all eight defect types. By leveraging MoveScan, we uncover 97,028 defects across all 37,302 deployed contracts in the Aptos and Sui blockchains, indicating a high prevalence of defects. Experimental results demonstrate that the precision of MoveScan reaches 98.85%, with an average project analysis time of merely 5.45 milliseconds. This surpasses previous state-of-the-art tools MoveLint, which exhibits an accuracy of 87.50% with an average project analysis time of 71.72 milliseconds, and Move Prover, which has a recall rate of 6.02% and requires manual intervention. Our research also yields new observations and insights that aid in developing more secure Move contracts.
In the context of boosting smart contract applications, prioritizing their security becomes paramount. Smart contract exploits often result in notable financial losses. Ensuring their security is by no means trivial. Rather than resulting in program crashes, most attacks in on-chain smart contracts aim to induce financial loss, referred to as profitable exploits. By constructing seemingly innocuous inputs, profitable exploits try to extract extra profit or compromise the interests of others. However, due to the complexity of call chains in on-chain smart contracts and the need for effective oracles for profitable exploits, smart contract fuzzing suffers from low efficiency and low effectiveness in finding profitable exploits. In this paper, we present Midas, a novel feedback-driven fuzzing framework to mine profitable exploits in on-chain smart contracts effectively. Midas consists of two modules: diverse validity fuzzing and profitable transaction identification. The diverse validity fuzzing module applies two waypoints to efficiently generate valid transactions, addressing the complexity of on-chain smart contract call chains. The profitable transaction identification module applies differential analysis to effectively identify profitable exploits, addressing the limitation of ad-hoc oracles. Evaluation of Midas over on-chain smart contracts showed it effectively identified 40 real-world exploits with a precision of 80%, outperforming state-of-the-art tools (i.e., ItyFuzz and Slither) in both efficiency and effectiveness. Particularly, Midas effectively mines five unknown exploits in valuable smart contracts, and two of them have already been confirmed by their DApp developers.
Ethereum faces growing fraud threats. Current fraud detection methods, whether employing graph neural networks or sequence models, fail to consider the semantic information and similarity patterns within transactions. Moreover, these approaches do not leverage the potential synergistic benefits of combining both types of models. To address these challenges, we propose TLMG4Eth that combines a transaction language model with graph-based methods to capture semantic, similarity, and structural features of transaction data in Ethereum. We first propose a transaction language model that converts numerical transaction data into meaningful transaction sentences, enabling the model to learn explicit transaction semantics. Then, we propose a transaction attribute similarity graph to learn transaction similarity information, enabling us to capture intuitive insights into transaction anomalies. Additionally, we construct an account interaction graph to capture the structural information of the account transaction network. We employ a deep multi-head attention network to fuse transaction semantic and similarity embeddings, and ultimately propose a joint training approach for the multi-head attention network and the account interaction graph to obtain the synergistic benefits of both.
Hany F. Atlam, Ndifon Ekuri, Muhammad Ajmal Azad, Harjinder Singh Lallie
Blockchain technology has gained significant attention in recent years for its potential to revolutionize various sectors, including finance, supply chain management, and digital forensics. While blockchain’s decentralization enhances security, it complicates the identification and tracking of illegal activities, making it challenging to link blockchain addresses to real-world identities. Also, although immutability protects against tampering, it introduces challenges for forensic investigations as it prevents the modification or deletion of evidence, even if it is fraudulent. Hence, this paper provides a systematic literature review and examination of state-of-the-art studies in blockchain forensics to offer a comprehensive understanding of the topic. This paper provides a comprehensive investigation of the fundamental principles of blockchain forensics, exploring various techniques and applications for conducting digital forensic investigations in blockchain. Based on the selected search strategy, 46 articles (out of 672) were chosen for closer examination. The contributions of these articles were discussed and summarized, highlighting their strengths and limitations. This paper examines the selected papers to identify diverse digital forensic frameworks and methodologies used in blockchain forensics, as well as how blockchain-based forensic solutions have enhanced forensic investigations. In addition, this paper discusses the common applications of blockchain-based forensic frameworks and examines the associated legal and regulatory challenges encountered in conducting a forensic investigation within blockchain systems. Open issues and future research directions of blockchain forensics were also discussed. This paper provides significant value for researchers, digital forensic practitioners, and investigators by providing a comprehensive and up-to-date review of existing research and identifying key challenges and opportunities related to blockchain forensics.
This study explores the application of deep learning and machine learning technologies in the field of Anti-Money Laundering (AML) for cryptocurrencies.With the rapid growth of cryptocurrency markets, the associated money laundering activities have increasingly become a focal point for governments and financial institutions worldwide.Traditional AML measures face challenges in the digital realm, particularly in identifying and preventing illicit transactions involving cryptocurrencies.To address this, the study designs various algorithms including Deep Neural Networks (DNN), Random Forest (RF), K-Nearest Neighbors (KNN), and Naive Bayes (NB) to enhance the detection capabilities of suspicious transactions within the Bitcoin Elliptic dataset.Cryptocurrencies involve using cryptographic security measures for financial transactions, yet their anonymity and transnational nature make them susceptible to money laundering activities.By evaluating the performance of different machine learning models on the Bitcoin Elliptic dataset, this research analyzes their effectiveness in identifying illicit transactions.The results indicate that the Random Forest model performs best, achieving an overall accuracy of 95%, effectively distinguishing between most illegal and legal transactions while mitigating overfitting risks.Through these technological approaches, the study aims to enhance AML monitoring capabilities in cryptocurrency markets, providing reliable decision support for financial institutions and regulatory bodies.Future research directions may include exploring more complex deep learning models or ensemble learning methods to further improve classification accuracy across diverse datasets and enable real-time monitoring of emerging money laundering patterns.The integration of these technologies holds promise for strengthening the global AML framework, addressing the increasingly complex challenges posed by digital finance and illicit financial activities (
In the field of blockchain technology, smart contracts play a core role, but programming oversights may cause serious security risks. This study provides a comprehensive review of the types of smart contract security vulnerabilities and the development of detection techniques. This article conducts a comprehensive review of the current various detection methods, including static and dynamic analysis, and proposes a combined dynamic and static detection method for integer overflow vulnerabilities at the solidity code level and timestamp vulnerabilities at the blockchain system layer. It also analyzes a The performance of a series of mainstream detection tools in terms of detection accuracy and efficiency is compared in depth, and their advantages and limitations are analyzed.
Purpose The trend among the financial investors to integrate cryptocurrencies, the very first completely digital assets, in their investment portfolio, has increased during the last decade. Even though cryptocurrencies share certain common characteristics with other investment products, they have their own distinct characteristic features, and the behavior of this asset class is currently being studied by the research scholars interested in this domain. Design/methodology/approach Using the text mining approach, this article examines research trends in the field of cryptocurrencies to identify prospective research needs. To narrow down to ten topics, the abstracts and the indexed keywords of 1,387 research publications on cryptocurrency, blockchain and Bitcoins published between 2013 and 2022 were analyzed using the topic modeling technique and Latent Dirichlet allocation (LDA). Findings The findings show a wide range of study trends on various aspects of cryptocurrencies. In the recent years, there have been lots of research and publications on the topics such as cryptocurrency markets, cryptocurrency transactions and use of blockchain in transactions and security of Bitcoin. In comparison, topics such as use of blockchain in fintech, cryptocurrency regulations, blockchain smart contract protocols and legal issues in cryptocurrency have remained relatively underexplored. After using the LDA, this paper further analyzes the significance of each topic, future directions of individual topics and its popularity among researchers in the discussion section. Originality/value While similar studies exist, no other work has used topic modeling to comprehensively analyze the cryptocurrencies literature by considering diverse fields and domains.
Aug 19, 2024·2024 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics
With the continuous advancement of blockchain technology, smart contracts, as one of its core applications, have increasingly become a focal point for security concerns. To address this, this paper proposes a novel method that integrates keyword filter technology, the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model, and the Bidirectional Long Short-Term Memory-Conditional Random Field (BiLSTM-CRF) architecture, aimed at enhancing the efficiency and accuracy of vulnerability detection in Solidity smart contracts. Initially, keyword filter technology is employed to select and preprocess code snippets, extracting features closely associated with security vulnerabilities. Subsequently, the BERT model conducts deep semantic analysis and feature extraction, after which the BiLSTM-CRF architecture further learns from the features and predicts vulnerability types. Extensive experiments conducted on a dataset comprising eight major types of vulnerabilities demonstrate that the method proposed in this study significantly outperforms existing vulnerability detection methods in key metrics such as accuracy, recall, and F1 scores. This research not only provides an effective technical solution for detecting security vulnerabilities in smart contracts but also holds significant theoretical and practical implications for promoting the safe and reliable development of blockchain technology.
How does a decentralised brand like Bitcoin become resilient? Drawing on ecological resilience framework, we examine how Bitcoin’s hive mind has evolved, thanks to a combination of stabilising and destabilising forces. We draw on a longitudinal ethnographic and netnographic study of Bitcoin/Blockchain from 2014 to 2022. We make three assertions. First, Bitcoin is stronger because it is a decentralised brand. Second, the social memory of the brand’s hive mind is accumulated, consolidated, and crystallised by responses to internal and external crises. Third, the network of the hive mind uses crises as metamorphic moments that become enshrined in social memory and help the brand become resilient over time.
Decentralized cryptocurrencies like Bitcoin have surged in popularity, aiding numerous growing sectors. These cryptocurrencies are underpinned by blockchain technology, which facilitates distributed consensus on transactions. Cryp-tocurrency systems like Ethereum integrate Smart Contracts, a key component that empowers the execution of user-defined programs on transactions. It allows mutually distrustful parties to transact securely, removing the need for third-party intermediaries and leading to the rise of promising applications in domains such as gaming, finance, and governance. Unfortunately, it also enables illicit activities, giving rise to Criminal Smart Contracts (CSCs). Common examples of CSCs include distributed denial of service (DDoS) attacks, calling card crimes, and data leaks. In this work, we explore an application of CSCs in ransomware attack, a prevalent cyber threat that encrypts victims' files and demands a ransom for data recovery. With the growing preference for cryptocurrencies in ransom payments, ransomware attackers are likely to adopt Smart Contracts due to the anonymity and automated ransom collection advantages. Our paper introduces a novel protocol exploring the potential shift towards ransomware CSCs from centralized systems. We evaluate our protocol and show a high success rate for such attacks, underscoring them as an imminent threat. This high success rate highlights the potential effectiveness of CSCs, contradicting previous literature. We conclude by emphasizing the need for robust regulatory measures and technical solutions to effectively mitigate the risks associated with ransomware CSCs.
Ruichao Liang, Jing Chen, Cong Wu, Kun He · 9 authors
Smart contracts, the cornerstone of decentralized applications, have become increasingly prominent in revolutionizing the digital landscape. However, vulnerabilities in smart contracts pose great risks to user assets and undermine overall trust in decentralized systems. Fuzzing, a prominent security testing technique, is extensively explored to detect vulnerabilities. But current smart contract fuzzers fall short of expectations in testing efficiency for two primary reasons. Firstly, smart contracts are stateful programs, and existing approaches, primarily coverage-guided, lack effective feedback from the contract state. Consequently, they struggle to effectively explore the contract state space. Secondly, coverage-guided fuzzers, aiming for comprehensive program coverage, may lead to a wastage of testing resources on benign code areas. This wastage worsens in smart contract testing, as the mix of code and state spaces further complicates comprehensive testing. To address these challenges, we propose Vulseye, a stateful directed graybox fuzzer for smart contracts guided by vulnerabilities. Different from prior works, Vulseyeachieves stateful directed fuzzing by prioritizing testing resources to code areas and contract states that are more prone to vulnerabilities. We introduceCode TargetsandState Targetsinto fuzzing loops as the testing targets of Vulseye. We use static analysis and pattern matching to pinpointCode Targets, and propose a scalable backward analysis algorithm to specifyState Targets. We design a novel fitness metric that leverages feedback from both the contract code space and state space, directing fuzzing toward these targets. With the guidance of code and state targets, Vulseyealleviates the wastage of testing resources on benign code areas and achieves effective stateful fuzzing. In comparison with state-of-the-art fuzzers, Vulseyedemonstrated superior effectiveness and efficiency. Notably, it uncovered 4,845 vulnerabilities in 42,738 real-world smart contracts, outperforming existing approaches by up to$9.7\times $, and identified 11 previously unknown vulnerabilities within the top 50 Ethereum DApps, involving approximately 2,500,000 USD.
Due to its anonymity and decentralization, Bitcoin has long been a haven for various illegal activities. Cyber-criminals generally legalize illicit funds by Bitcoin mixing services. Therefore, it is critical to investigate the mixing services in cryptocurrency anti-money laundering. Existing studies treat different mixing services as a class of suspicious Bitcoin entities. Furthermore, they are limited by relying on expert experience or needing to deal with large-scale networks. So far, multi-class mixing service identification has not been explored yet. It is challenging since mixing services share a similar procedure, presenting no sharp distinctions. However, mixing service identification facilitates the healthy development of Bitcoin, supports financial forensics for cryptocurrency regulation and legislation, and provides technical means for fine-grained blockchain supervision. This paper aims to achieve multi-class Bitcoin Mixing Service Identification with a Graph Classification (BMSI-GC) model. First, BMSI-GC constructs 2-hop ego networks (2-egonets) of mixing services based on their historical transactions. Second, it applies graph2vec, a graph classification model mainly used to calculate the similarity between graphs, to automatically extract address features from the constructed 2-egonets. Finally, it trains a multilayer perceptron classifier to perform classification based on the extracted features. BMSI-GC is flexible without handling the full-size network and handcrafting address features. Moreover, the differences in transaction patterns of mixing services reflected in the 2-egonets provide adequate information for identification. Our experimental study demonstrates that BMSI-GC performs excellently in multi-class Bitcoin mixing service identification, achieving an average identification F1-score of 95.08%.
El-hacen Diallo, Rouwaida Abdallah, Mohammad Dib, Omar Dib
This paper introduces an innovative response to the pressing challenge of rapid and effective incident detection and management in urban settings. The proposed solution is a decentralized incident reporting system (IRS) harnessing blockchain technology and decentralized data storage systems. By empowering residents to report incidents, the proposed IRS enables seamless real-time monitoring and intervention by relevant departments. Built on a blockchain foundation, the proposed solution ensures immutability, transparency, security, and auditability, enhancing data resilience and comprehensive applicability. The proposed system leverages the InterPlanetary File System (IPFS) for the storage of incident proofs to manage the blockchain size effectively. Through the proposed IRS, transparency is upheld, enabling complete auditability of incident details and required interventions by citizens, societal bodies, and governmental bodies. Moreover, an incentive model is introduced to encourage active participation in incident reporting, thereby enhancing the system’s overall effectiveness and long-term sustainability. The proposed IRS integrates mobile technology to facilitate user engagement and data submission, essential for urban emergency management. Empirical validation using the Quorum–Raft blockchain demonstrates the feasibility of the proposed approach in terms of system throughput, incident reporting delay, blockchain size, and deployment cost. Specifically, the system maintains a latency of under 15 s even at high transaction rates, can handle up to 200 incidents per second, and is cost-effective, with deployment estimates for 16 organizations over five years being under 1.99 million USD. The method involves extensive testing with simulated incidents and user interactions to ensure robustness and scalability, showcasing the system’s potential for effective emergency management in urban environments.