The emergence of the Internet of Things (IoT) technology has caused a powerful transition in the cyber threat landscape. As a result, organisations have had to find new ways to better manage the risks associated with their infrastructure. In response, a significant amount of research has focused on developing efficient Cyber Threat Intelligence (CTI) sharing platforms. However, most existing solutions are highly centralised and do not provide a way to exchange information in a distributed way. In this chapter, we subsequently seek to evaluate how blockchain technology can be used to address a number of limitations present in existing CTI sharing platforms. To determine the role of blockchain-based sharing moving forward, we present a number of general CTI sharing challenges, and discuss how blockchain can bring opportunities to address these challenges in a secure and efficient manner. Finally, we discuss a list of relevant works and note some unique future research questions.
In this research, we discussed a rising issue for Muslims in today world that involves a financial and technical innovation, namely: cryptocurrencies. We found out through a questionnaire that many Muslims are having a hard time finding the jurisprudence rulings on certain cryptocurrencies. Therefore, the objective of this research is to investigate and identify features that play a part in determining the jurisprudence rulings on cryptocurrencies. We have collected a dataset containing 106 cryptocurrencies classified into 56 Halal and 50 Haram cryptocurrencies, and used 20 handcrafted features. Moreover, based on these identified features, we designed an intelligent system that contains a Machine Learning model for classifying cryptocurrencies into Halal and Haram.
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cs.CY
cs.HC
Terrorism, Counterterrorism, and Political Violence
With the continuous innovative development and popularization of mobile smart devices , the application of Mobile Crowd Sensing (MCS) continues to be studied extensively. However, existing centralized MCS applications that use servers for task publishing and data collection exhibit common problems, such as single points of failure and security vulnerabilities . Accordingly, we proposed a hybrid blockchain-based identity authentication scheme for MCS called HBIA, which uses blockchain technology to resolve the single-point failure problem. HBIA builds a cluster structure based on factors such as geographical location and balance, and uses it to construct a hybrid blockchain , with the cluster head node and internal cluster node authenticating on the public and private chains, respectively. We also implemented zero-knowledge proof (ZKP) to ensure the privacy of participants’ identities, thus balancing the contradiction between blockchain transparency and security. In addition, HBIA uses the zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) technology to enable off-chain computing and on-chain verification, further reducing the blockchain’s workload. Finally, HBIA was evaluated based on the pavement crack detection task and tested on the Ethereum public test network known as Ropsten. The test results indicate that the identity authentication scheme proposed in this paper is superior to existing schemes in terms of authentication time.
Alina Khayretdinova, Michael Kubach, Rachelle Sellung, Heiko Roßnagel
Abstract New approaches to identity management based on technologies such as blockchain and distributed ledgers are promoted as a chance to give users full control over their own identity data. Despite being often called the future of digital identity management, Decentralized Identity Management (DIdM) and Self-sovereign Identities (SSI) are still facing a number of challenges, usability being a major one: their concepts are too sophisticated for users and do not fit their mental models. We address this by conducting a study that analyses and evaluates the usability and practical applicability of some of the most advanced DIdM solutions. The results of the user tests reveal existing usability issues and outline the way they deprive end users of experiencing the entire range of claimed privacy and security benefits of these identity solutions.
Bitcoin is the most widely used cryptocurrency for illegal trade in current darknet markets. Owing to the anonymity of its addresses, even though transaction flows are globally visible, Bitcoin clustering remains one of the most challenging and open problems in illegal Bitcoin transaction analysis. In this article, to resolve this problem, we propose a novelmulti-layer heuristicalgorithm for Bitcoin clustering, which leverages on-chain transactions as well as off-chain application data in the real world. For this purpose, we first explored the unique characteristics of darknet market ecosystems including their trading systems. By conducting an in-depth analysis of the data manually collected for 11 months, we found that some darknet market review data disclosed transactions containing Bitcoin value and item delivery information. We then identified unique Bitcoin addresses associated with the disclosed information, owned by the same darknet providers. Based on address ownership, more accurate market clusters could be created, which have not previously been identified by other clustering algorithms. According to our experimental results, approximately 31.68% of the darknet market review data matched real Bitcoin transactions, and 122 hidden clusters associated with Silk Road 4 were found. This indicates that the proposed algorithm can complement existing clustering methods and significantly reduce the false negative rate by up to 91.7%.
Purpose: Bitcoin, the most prominent among cryptocurrencies, is a peer-to-peer (“P2P”) electronic currency and payment system used based on mutual trust. The study aims to measure awareness of Bitcoin, which has recently become popular among investors with its rapidly increasing use. To this end, a questionnaire was applied to the participants to evaluate their opinions and preferences regarding Bitcoin. With this purpose in mind, a questionnaire was applied to the academics of Erzincan Binali Yıldırım University in Turkey. Methodology: In the process of analysing the obtained data, frequency analysis and the chi-square test method were used by using the SPSS 26.0 software package. Results: According to the results of the study, it was observed that 51.6% of the academics (159 persons) had knowledge of Bitcoin, and while 31.5% of the academics participating in the study (97 persons) considered they would buy Bitcoin within five years, 57% (176 persons) conceived the use of Bitcoin would increase within ten years. Despite all these positive attitudes, it was also observed that 45.5% (140 people) considered Bitcoin as unreliable and 51.6% (159 persons) would prefer gold instead of using Bitcoin. As for the opinion of academics, it was concluded that gold and other different investment tools would be preferred instead of Bitcoin despite its increasing use. Conclusion: This research is important as it is the first research study in the field, which means that no similar study has been conducted in Turkey before, and it is thus expected to greatly contribute to the literature within the context of the importance and originality of the study.
Muhammad Shoaib Farooq, Usman Iftikhar, Adel Khelifi
A widespread mistrust towards the traditional voting system has made democratic voting in any country very critical. People have seen their fundamental rights being violated. Other digital voting systems have been challenged due to a lack of transparency. Most voting systems are not transparent enough; this makes it very difficult for the government to gain voters’ trust. The reason behind the failure of the traditional and current digital voting system is that it can be easily exploited. The primary objective is to resolve problems of the traditional and digital voting system, which include any kind of mishap or injustice during the process of voting. Blockchain technology can be used in the voting system to have a fair election and reduce injustice. The physical voting systems have many flaws in it as well as the digital voting systems are not perfect enough to be implemented on large scale. This appraises the need for a solution to secure the democratic rights of the people. This article presents a platform based on modern technology blockchain that provides maximum transparency and reliability of the system to build a trustful relationship between voters and election authorities. The proposed platform provides a framework that can be implemented to conduct voting activity digitally through blockchain without involving any physical polling stations. Our proposed framework supports a scalable blockchain, by using flexible consensus algorithms. The Chain Security Algorithm applied in the voting system makes the voting transaction more secure. Smart contracts provide a secure connection between the user and the network while executing a transaction in the chain. The security of the blockchain based voting system has also been discussed. Additionally, encryption of transactions using cryptographic hash and prevention of attack 51% on the blockchain has also been elaborated. Furthermore, the methodology for carrying out blockchain transactions during the process of voting has been elaborated using Blockchain Finally, the performance evaluation of the proposed system shows that the system can be implemented in a large-scale population.
With the growing interest in blockchain in both academic research and industry, the security and privacy of blockchains have attracted huge interest, even though only a small part of the blockchain platforms can achieve the set of abovementioned security goals in practice. Leveraging from the state-of-the-art security paradigms, we propose 5irechain protective covering which has the ability to continuously identify, map, scan, assess, and grade the risk portfolio of all the assets, vendors, and acquisitions of a company giving a hackers perspective to the company. The dashboard gives visibility of not just only the web2 infrastructure in place, but also covers the web3 space and allows 5ire to monitor all of its nodes and their activities including historical data, tracking large wallets, monitoring bot activity, detecting transaction stats and volume along with the ability to block possible large scale attacks.
Over the last decade, Bitcoin has revolutionized the global economic and technological landscape, inspiring a new generation of blockchain-based technologies. Its protocol is today among the most influential for cryptocurrencies and distributed networks. In particular, the P2P layer represents a reference point for all permissionless blockchains, which often implement its solutions in their network layer. Unfortunately, the Bitcoin network protocol lacks a strong security model, leaving it exposed to several threats. Attacks at this level can affect the reliability and trustworthiness of the consensus layer, mining the credibility of the whole system. It is therefore of utmost importance to properly understand and address the security of the Bitcoin P2P protocol. In this paper, we give a comprehensive and detailed overview of known network-level attacks in Bitcoin, as well as the countermeasures that have been implemented in the protocol. We propose a generic network adversary model, and propose an objective-based taxonomy of the attacks. Finally, we identify the core weaknesses of the protocol and study the relationship between different types of attack. We believe our contribution can help both new and experienced researchers have a broader and deeper understanding of the Bitcoin P2P network and its threats, and allow for a better modeling of its security properties.
As a typical representative of web 2.0, Ethereum has significantly boosted the development of blockchain finance. However, due to the anonymity and financial attributes of Ethereum, the number of phishing scams is increasing rapidly and causing massive losses, which poses a serious threat to blockchain financial security. Phishing scam address identification enables to detect phishing scam addresses and alerts users to reduce losses. However, there are three primary challenges in phishing scam address recognition task: 1) the lack of publicly available large datasets of phishing scam address transactions; 2) the use of multi-order transaction information requires a large number of queries and computations; and 3) the extraction of phishing scam address features relies on machine learning methods excessively, which leads to the loss of practical meaning and is harmful to the research of phishing scam addresses. This paper proposes a systematic phishing scam address recognition scheme, to simultaneously overcome the three challenges in phishing scam address recognition. In this paper, a systematic phishing scam address recognition scheme is proposed to addresses these issues. Specifically, due to the insufficient number of address tagged in the existing publicly available Ethereum phishing scam address transaction dataset, we first construct a transaction dataset involving over 10000 tagged addresses. To the best of our knowledge, this is the largest dataset of tagged addresses for Ethereum phishing scam detection. Then, we design a new heuristic rule to implement feature extraction of address nodes by analysing the traditional financial involved accounts combined with information specific to Ethernet transactions. After that, a novel adaptive feature importance filtering method is designed to adaptively adjust the filtering threshold based on the final classification results, which reduce the feature dimensionality while ensuring a certain detection performance. Finally, random forest is used to classify whether the addresses is a phishing scam address or not. Extensive experiments on real Ethereum datasets show that our approach (98.89% Precision, 98.35% Recall, 98.62% F1) achieves state-of-the-art performance.
Cryptocurrencies continue to captivate businesses and investors despite market fluctuations. The number of crypto users have risen rapidly in the last few years, and alarmingly, many appear to be unaware of the risks involved. These risks aren't confined to market hazards but include very sophisticated cybercrimes related to cryptocurrencies. As cryptocurrencies have become a breeding ground for a variety of cybercrimes, resulting in enormous financial losses, it hinders user adoption limiting the utility of the blockchain technology. It has become crucial to spot such scams and devise intelligent techniques to make this technology a safer place for investors. This study proposes a classification model to handle fake account problem over Ethereum blockchain, and its contribution is multi-faceted; firstly, available imbalanced Ethereum dataset has been balanced to enhance the accuracy of the classification model. Secondly, correlation-based feature selection technique has been applied to retain best discriminating features. Thirdly, an effective machine learning based model has been presented for the identification of fake accounts over the Ethereum system. A comparative study of ten machine learning techniques has been presented consisting of both individual and ensemble classifiers. Experimental results showed that ensemble classifiers appear to yield better performance measures over individual classifiers and among all, LightGbm-based classification model outperformed with 99.2% accuracy.
In recent years, Bitcoin and other cryptocurrencies have been increasingly considered an investment option for emerging markets. However, its erratic behavior has discouraged some potential investors. To get insights into its behavior and price fluctuation, past studies have discovered the correlation between Twitter sentiments and Bitcoin behavior. Most of them have focused exclusively on their relationships, instead of the Twitter sentiment analysis itself. Finding the most suitable classification algorithms for sentiment analysis for this kind of data is challenging. For enormous data of Twitter, unlabeled data can be time-consuming and expensive for the supervised sentiment analysis approach, which has been studied to be superior to unsupervised ones. As such, we propose HyVADRF: Hybrid VADER – Random Forest and Grey Wolf Optimizer Model. Semantic and rule-based VADER was used to calculate polarity scores and classify sentiments, which overcame the weakness of manual labeling, while Random Forest was utilized as its supervised classifier. Furthermore, considering Twitter’s massive size, we collected over 3.6 million tweets and analyzed various dataset sizes as these are related to the model’s learning process. Lastly, Grey Wolf Optimizer parameter tuning was conducted to optimize the classifier’s performance. The results show that 1) HyVADRF Model returned an accuracy of 75.29 %, precision of 70.22%, recall of 87.70%, and F1-score of 78%. 2) The most ideal percentage of dataset size is 90% of the total collected tweets (n=1,249,060). 3) With standard deviations of 0.0008 for accuracy and F1-score and 0.0011 for precision and recall. Hence, HyVADRF Model consistently delivers stable results.
Blockchain has widespread applications in the financial field but has also attracted increasing cybercrimes. Recently, phishing fraud has emerged as a major threat to blockchain security, calling for the development of effective regulatory strategies. Nowadays network science has been widely used in modeling Ethereum transaction data, further introducing the network representation learning technology to analyze the transaction patterns. In this paper, we consider phishing detection as a graph classification task and propose an end-to-end Phishing Detection Graph Neural Network framework (PDGNN). Specifically, we first construct a lightweight Ethereum transaction network and extract transaction subgraphs of collected phishing accounts. Then we propose an end-to-end detection model based on Chebyshev-GCN to precisely distinguish between normal and phishing accounts. Extensive experiments on five Ethereum datasets demonstrate that our PDGNN significantly outperforms general phishing detection methods and scales well in large transaction networks.
Arkan Hammoodi Hasan Kabla, Mohammed Anbar, Selvakumar Manickam, Taief Alaa Al-Amiedy · 7 authors
Ethereum attracts more investors, researchers, and even scammers for many reasons; this is the first platform that enables the new Decentralized Applications (DApps) to run on top of the blockchain network. However, the rich semantics and applications of DApps inevitably introduce many security issues that have grabbed significant attention from industry and academics due to their destructive impact on DApps in recent years. Therefore, there is a vital need to study the applicability of Intrusion Detection System in detecting Ethereum-based attacks. Hence, this paper is among the first comprehensive review that studies the applicability of IDS in detecting Ethereum-based attacks. In addition, this paper lists all the potential attacks on Ethereum passing through the vulnerabilities that cause those attacks and ending with the consequences of each attack. Besides, this paper analyses all the IDS-based related works of Ethereum attacks detection since the Ethereum platform was launched in 2015. Finally, this paper discusses the open issues regarding vulnerabilities and attacks, challenges, and future directions.
Arkan Hammoodi Hasan Kabla, Mohammed Anbar, Selvakumar Manickam, Shankar Karupayah
Recently, the rapid flourish of blockchain technology in the financial field has attracted many cybercriminals’ attention to launch blockchain-based attacks such as Ponzi schemes, Scam wallets, and phishing scams. Currently, Ethereum is the most prominent blockchain-based platform and the first that supports smart contracts. However, the number of phishing scam accounts are reportedly more than 50% of all cybercrimes in Ethereum. In contrast, this paper proposes a detection mechanism called Ethereum Phishing Scam Detection (Eth-PSD) that attempts to detect phishing scam-related transactions using a novel machine learning-based approach. Eth-PSD tackles some of the limitations in the existing works, such as the use of imbalanced datasets, complex feature engineering, and lower detection accuracy. We also investigated the aspects of constructing a new updated and balanced dataset that can be used for evaluating Eth-PSD effectively. Our experimental results indicate that Eth-PSD could efficiently detect the phishing scam on Ethereum with a detection accuracy of 98.11%, with a very low False Positive Rate of 0.01. Taken together, Eth-PSD showed a superior advantage compared to the existing works in reducing the dimensionality of the dataset by feature engineering and achieved an overall detection accuracy with an improvement of at least 6% compared to other existing solutions from the related work.
Blockchain kripto paraların arkasındaki teknoloji olarak sistemi değiştirmeyi, hacklemeyi, hile yapmayı zorlaştıracak ve hatta imkânsız hale getirecek şekilde bilgi kaydetme sistemidir. İş yönetimi için çok önemli olan yeniliklerin örneklerinden biridir ve işletmelerin işleyişi üzerinde önemli bir etkiye sahip, gelişmekte olan ve faydacı bir teknolojidir. Bu çalışmanın amacı blockchain teknolojisinin mevcut ve muhtemel kullanım alanlarını analiz etmektir. Bu amaçla literatür taraması yapılarak elde edilen bulgular değerlendirilmiştir. Araştırma bulguları blockchain teknolojisinin işletmelerde, üretim, insan kaynakları, tedarik zinciri, pazarlama, turizm, kamu, sağlık, tarım, finans, muhasebe denetim, enerji, eğlence sektörlerinde kullanım imkanı olduğunu göstermektedir. Elde edilen sonuçlara göre blockchain özellikle muhasebe, finans, denetim ve bankacılık alanlarında önemli değişimlere ve gelişmelere yol açacak bir teknoloji olarak kabul edilmektedir.
Kripto para birimleri 2009 yılında ilk Bitcoin'in ortaya çıkışından bu yana finansal sistemin önemli bir parçası haline gelmiştir. Özellikle de son zamanlarda finansal sistem içerisinde potansiyel değişiklikler meydana getirerek, toplumsal karşılığı ve gelecekteki beklentileri hakkında daha çok gündemi meşgul etmeye başlamıştır. Bu gündem sosyal medya sitelerinde daha çok görülmektedir. Bu çalışmada da Twitter’da #Bitcoin olarak atılan Tweetlerin duygu analizi incelenmiştir. Bunun için Orange Data Mining programı kullanılmıştır. Sonuç olarak; Bitcoin konusunda baskın bir sevinç duygusunun olduğu ve yatırımcıların Bitcoin aldıklarında kendilerini mutlu hissettikleri görülmüştür.
Kai Wang, Jun Pang, Ding-Jie Chen, Yu Zhao · 7 authors
Exploiting the anonymous mechanism of Bitcoin, ransomware activities demanding ransom in bitcoins have become rampant in recent years. Several existing studies quantify the impact of ransomware activities, mostly focusing on the amount of ransom. However, victims’ reactions in Bitcoin that can well reflect the impact of ransomware activities are somehow largely neglected. Besides, existing studies track ransom transfers at the Bitcoin address level, making it difficult for them to uncover the patterns of ransom transfers from a macro perspective beyond Bitcoin addresses. In this article, we conduct a large-scale analysis of ransom payments, ransom transfers, and victim migrations in Bitcoin from 2012 to 2021. First, we develop a fine-grained address clustering method to cluster Bitcoin addresses into users, which enables us to identify more addresses controlled by ransomware criminals. Second, motivated by the fact that Bitcoin activities and their participants already formed stable industries, such as Darknet and Miner , we train a multi-label classification model to identify the industry identifiers of users. Third, we identify ransom payment transactions and then quantify the amount of ransom and the number of victims in 63 ransomware activities. Finally, after we analyze the trajectories of ransom transferred across different industries and track victims’ migrations across industries, we find out that to obscure the purposes of their transfer trajectories, most ransomware criminals (e.g., operators of Locky and Wannacry) prefer to spread ransom into multiple industries instead of utilizing the services of Bitcoin mixers. Compared with other industries, Investment is highly resilient to ransomware activities in the sense that the number of users in Investment remains relatively stable. Moreover, we also observe that a few victims become active in the Darknet after paying ransom. Our findings in this work can help authorities deeply understand ransomware activities in Bitcoin. While our study focuses on ransomware, our methods are potentially applicable to other cybercriminal activities that have similarly adopted bitcoins as their payments.
This paper studies a fundamental problem regarding the security of blockchain PoW consensus on how the existence of multiple misbehaving miners influences the profitability of selfish mining. Each selfish miner (or attacker interchangeably) maintains a private chain and makes it public opportunistically for acquiring more rewards incommensurate to his Hash power. We first establish a general Markov chain model to characterize the state transition of public and private chains for Basic Selfish Mining (BSM), and derive the stationary profitable threshold of Hash power in closed-form. It reduces from 25% for a single attacker to below 21.48% for two symmetric attackers theoretically, and further reduces to around 10% with eight symmetric attackers experimentally. We next explore the profitable threshold when one of the attackers performs strategic mining based on Partially Observable Markov Decision Process (POMDP) that only half of the attributes pertinent to a mining state are observable to him. An online algorithm is presented to compute the nearly optimal policy efficiently despite the large state space and high dimensional belief space. The strategic attacker mines selfishly and more agilely than BSM attacker when his Hash power is relatively high, and mines honestly otherwise, thus leading to a much lower profitable threshold. Last, we formulate a simple model of absolute mining revenue that yields an interesting observation: selfish mining is never profitable at the first difficulty adjustment period, but replying on the reimbursement of stationary selfish mining gains in the future periods. The delay till being profitable of an attacker increases with the decrease of his Hash power, making blockchain miners more cautious on performing selfish mining.
Abdul Razaque, Bandar Alotaibi, Munif Alotaibi, Fathi Amsaad · 8 authors
When people use social networks, they often fall prey to a clickbait scam. The scammer attempts to create a striking headline that attracts the majority of users and attaches a link. The user follows the link and can be redirected to a fraudulent resource where the user easily loses personal data. To solve this problem, a Blockchain-enabled deep recurrent neural network (BDRNN) is proposed to detect the nature safe and malicious clickbait from the contents. The proposed BDRNN consists of three phases: analysis of clickbait and source rating, clickbait search process and multi-layered clickbait detection. The analysis of clickbait and source rating phase helps to analyze different sources to detect the clickbait and also rating the content-sources. To achieve the clickbait analysis and source rating, the detection of blocklisted/allowlisted source and source rating check algorithms are introduced. The clickbait search process is accomplished by incorporating the binary search features for a faster and more efficient search process for malicious content-detection. The multi-layered clickbait detection is main phase of the proposed BDRNN that consists of three models: content-to-vector model (layer-1), deep neural network model(layer-2), and Blockchain-enabled malicious content detection model (layer-3). These models collectively detect the malicious and safe clickbait from the contents. The extensive experiments are conducted to determine the effectiveness of the proposed BDRNN model and compared with the existing state-of-the-art neural network models designed for clickbait detection, and the result demonstrates that the proposed BDRNN model outperforms the counterparts from the, accuracy, link detection, memory usage, analogous perspectives, and attacker’s successful content capturing rate.
Yourong Chen, Hao Chen, Yang Zhang, Meng Han · 6 authors
Owing to the incremental and diverse applications of cryptocurrencies and the continuous development of distributed system technology, blockchain has been broadly used in fintech, smart homes, public health, and intelligent transportation due to its properties of decentralization, collective maintenance, and immutability. Although the dynamism of blockchain abounds in various fields, concerns in terms of network communication interference and privacy leakage are gradually increasing. Because of the lack of reliable attack analysis systems, fully understanding some attacks on the blockchain, such as mining, network communication, smart contract, and privacy theft attacks, has remained challenging. Therefore, in this study, we examine the security and privacy of the blockchain and analyze possible solutions. We systematical classify the blockchain attack techniques into three categories, then discuss the corresponding attack and defense methods based on these categories. We focus on (1) the attack and defense methods of mining pool attacks for blockchain security issues, such as block withholding, 51%, pool hopping, selfish mining, and fork after withholding attacks, in the attack type of consensus excitation; (2) the attack and defense methods of network communication and smart contracts for blockchain security issues, such as distributed denial-of-service, Sybil, eclipse, and reentrancy attacks, in the attack type of middle protocol; and (3) the attack and defense methods of privacy thefts for blockchain privacy issues, such as identity privacy and transaction information attacks, in the attack type of application service. Finally, we discuss future research directions for blockchain security.
Razieh Nokhbeh Zaeem, Kai Chih Chang, Teng-Chieh Huang, David Liau · 10 authors
Identity is at the heart of digital transformation. Successful digital transformation requires confidence in and protection of digital identities. On the Internet, however, there is no unique and standard identity layer. Consequently, a variety of digital identities have emerged over years, leading to privacy risks, security vulnerabilities, risks for identity owners, and liability for identity issuers and those relying on digital identities to grant access to goods and services. Self-Sovereign Identity (SSI) and similar forms of identity management on the blockchain distributed ledger are novel technologies that recognize the need to keep user identity privately stored in user-owned devices, securely verified by identity issuers, and only revealed to verifiers as needed. There is limited academic literature defining the prerequisite SSI functional and non-functional requirements and comparing SSI technologies. Often those SSI technologies reviewed in the literature lack behind current advances. We present the first work that compiles a comprehensive list of functional and non-functional requirements of SSI and compares an extensive number of existing SSI/blockchain-based identity management solutions with respect to these requirements. Our work sheds light on the state-of-the-art SSI development and paves the way for future, more informed analysis and development of novel identity management and SSI solutions.
In this article, we present a Social Network Analysis–based approach to investigate user behaviour during a cryptocurrency speculative bubble in order to extract knowledge patterns about it. Our approach is general and can be applied to any past, present and future cryptocurrency speculative bubble. To verify its potential, we apply it to investigate the Ethereum speculative bubble happened in the years 2017 and 2018. We also describe several interesting knowledge patterns about the behaviour of specific categories of users that we obtained from this investigation. Furthermore, we describe how our approach can support the construction of an identikit of the speculators who maneuvered behind the Ethereum bubble analysed. Finally, we show that this capability of supporting the hunting for speculators is intrinsic of our approach and can cover past, present and future bubbles.