Marco Di Gennaro, Lorenzo Italiano, Giovanni Meroni, Giovanni Quattrocchi
Thanks to built-in immutability and persistence, the blockchain is often seen as a promising technology to certify information. However, when the information does not originate from the blockchain itself, its correctness cannot be taken for granted. To address this limitation, blockchain oracles -- services that validate external information before storing it in a blockchain -- were introduced. In particular, when the validation cannot be automated, oracles rely on humans that collaboratively cross-check external information. In this paper, we present DeepThought, a distributed human-based oracle that combines voting and reputation schemes. An empirical evaluation compares DeepThought with a state-of-the-art solution and shows that our approach achieves greater resistance to voters corruptions in different configurations.
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.
Open access
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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.