Abstract This article is an attempt to reconcile the requirements of the EU General Data Protection Regulation (GDPR) and anti-money laundering and combat terrorist financing (AML/CFT) instruments used in permissionless ecosystems based on distributed ledger technology (DLT). Usually, analysis is focused only on one of these regulations. Covering by this research the interplay between both regulations reveals their incoherencies in relation to permissionless DLT. The GDPR requirements force permissionless blockchain communities to use anonymization or, at the very least, strong pseudonymization technologies to ensure compliance of data processing with the GDPR. At the same time, instruments of global AML/CFT policy that are presently being implemented in many countries following the recommendations of the Financial Action Task Force, counteract the anonymity-enhanced technologies built into blockchain protocols. Solutions suggested in this article aim to induce the shaping of permissionless DLT-based networks in ways that at the same time would secure the protection of personal data according to the GDPR rules, while also addressing the money laundering and terrorist financing risks created by transactions in anonymous blockchain spaces or those with strong pseudonyms. Searching for new policy instruments is necessary to ensure that governments do not combat the development of all privacy-blockchains so as to enable a high level of privacy protection and GDPR-compliant data processing. This article indicates two AML/CFT tools which may be helpful for shaping privacy-blockchains that can enable the feasibility of such tools. The first tool is exceptional government access to transactional data written on non-transparent ledgers, obfuscated by advanced anonymization cryptography. The tool should be optional for networks as long as another effective AML/CFT measures are accessible for the intermediaries or for the government in relation to a given network. If these other measures are not available and the network does not grant exceptional access, the regulations should allow governments to combat the development of those networks. Effective tools in that scope should target the value of privacy-cryptocurrency, not its users. Such tools could include, as a tool of last resort, state attacks which would undermine the trust of the community in a specific network.
Use of distributed ledger technologies like blockchain is becoming more common in transportation/mobility ecosystems. However, cyber-security failures may occur at places where the blockchain system connects with the real world. In this paper, we propose a novel risk assessment framework for blockchain applications in smart mobility. We aim at systematically quantifying the risk by presenting ordinal values because although vulnerabilities exist in a system, it's the probability that they can be exploited and the impact of this exploitation that determine if in fact, the vulnerability corresponds to a significant risk. As a case study, we carry out an analysis in terms of quantifying the risk associated to a multi-layered Blockchain framework for Smart Mobility Data-markets (BSMD). We first construct an actor-based analysis to determine the impact of the attacks. Then, a scenario-based analysis determines the probability of occurrence of each threat. Finally, a combined analysis is developed to determine which attack outcomes have the highest risk. In the case study of the public permissioned BSMD, the outcomes of the risk analysis highlight the highest risk factors according to their impact on the victims in terms of monetary, privacy, integrity and trust. The analysis uncovers specific blockchain technology security vulnerabilities in the transportation ecosystem by exposing new attack vectors.
With the advancing technologies become tools for illicit activities and avoid detection, digital forensics is necessary for law enforcement in modern criminal investigations. In order to maintain the integrity and authenticity of the evidence, a chain of custody is essential for the successful prosecution of criminals in court. However, it is a great challenge to manage the preservation and collection of digital evidence because of its fragile and volatile in nature. Blockchain has been proposed as a promising and reliable technology to provide immutability and traceability of digital content, however, the applications of blockchain to the law enforcement agencies (LEAs) requires special attention to the security issue. In this research, we proposed a blockchain of custody framework to facilitate the security and transparency of digital evidence in criminal investigation process. The framework is implement on Ethereum smart contract to support authenticity and integrity of digital evidence in preliminary investigation, case management and court phases. We also propose the role of investigator to leverage access control in evidence creation, transferring and modification. The corresponding actions with judicial process is simulated using private ethereum blockchain. The experimental results indicate that the proposed framework can prevent digital evidence been tampered or contaminated and assure its legal defensibility with rigorous privilege management. Moreover, by successfully synchronizing digital evidence transactions to multiple nodes ensures the accountability of evidence data for every involved law enforcement agency.
Jan 1, 2021·Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
This paper demonstrates the use of network analysis to identify core nodes associated with ransomware attacks in cryptocurrency transaction networks. The method helps trace the cyber entities involved in cryptocurrency attacks and supports intelligence efforts to identify and disrupt cryptocurrency networks. A data corpus is built by the unsupervised machine learning graph algorithm ‘DeepWalk’ [1]. DeepWalk evaluates the position of nodes within networks. It compares the relative position of different nodes (similarity) and identifies those whose removal would most affect the network (riskiness). This method helps identify on the blockchain the key nodes that are involved in the execution of a ransomware attack. When applied to the ransomware “cash out” graph, the method derived “riskiness” scores for specific nodes. Analysing the derived “riskiness” at a community level (groups of nodes in the network) provides an enhanced granularity for identifying and targeting influential nodes. Such insight could potentially support both intelligence and forensics investigations.
The aim of the article is to show cryptocurrencies as facilitators of crime, especially cybercrime. While investing in cryptocurrencies is publicly discussed, their role in the criminal world is more hidden from the public, however, causes a lot of problems for law enforcement and regulatory authorities around the world. Criminals are interested in cryptocurrencies as targets for of their attacks, means of payment and as a method of money laundering. In this article, we discuss why cryptocurrencies have become a means of payment for the criminal underworld, what benefits they have from them, and what major challenges law enforcement is facing in that regard. The survey was conducted based on reports from authorities involved in the prosecution of cybercrime. We can conclude that the organized criminal underworld has a strong interest in cryptocurrencies and their popularity among ordinary, honest citizens, as in this way it is much easier for the criminals to conceal their activities and the origin of their assets.
Elizaveta Zinovyeva, Raphael Constantin Georg Reule, Wolfgang Karl Härdle
Smart Contracts are commonly considered to be an important component or even a key to many business solutions in an immense variety of sectors and promises to securely increase their individual efficiency in an ever more digitized environment. Introduced in the early 1990's, the technology has gained a lot of attention with its application to blockchain technology to an extent, that can be considered a veritable hype. Reflecting the growing institutional interest, this intertwined exploratory study between statistics, information technology, and law contrasts these idealistic stories with the data reality and provides a mandatory step of understanding the matter, before any further relevant applications are discussed as being "factually" able to replace traditional constructions. Besides fundamental flaws and application difficulties of currently employed Smart Contracts, the technological drive and enthusiasm backing it may however serve as a jump-off board for future developments thrusting well in the presently unshakeable traditional structures.
The emergence of smart contracts has increased the attention of industry and academia to blockchain technology, which is tamper-proofing, decentralized, autonomous, and enables decentralized applications to operate in untrustworthy environments. However, these features of this technology are also easily exploited by unscrupulous individuals, a typical example of which is the Ponzi scheme in Ethereum. The negative effect of unscrupulous individuals writing Ponzi scheme-type smart contracts in Ethereum and then using these contracts to scam large amounts of money has been significant. To solve this problem, we propose a detection model for detecting Ponzi schemes in smart contracts using bytecode. In this model, our innovation is shown in two aspects: We first propose to use two bytes as one characteristic, which can quickly transform the bytecode into a high-dimensional matrix, and this matrix contains all the implied characteristics in the bytecode. Then, We innovatively transformed the Ponzi schemes detection into an anomaly detection problem. Finally, an anomaly detection algorithm is used to identify Ponzi schemes in smart contracts. Experimental results show that the proposed detection model can greatly improve the accuracy of the detection of the Ponzi scheme contracts. Moreover, the F1-score of this model can reach 0.88, which is far better than those of other traditional detection models.
Non-Fungible Token (NFT) is regarded as one of the important applications of blockchain technology. In this article, we propose an asset-backed securities (ABS) scheme that splits the complete NFT into a certain number of units, which are shared by multiple participants. On the one hand, ABS plans to promise high-value and long-term investment returns by enhancing the market liquidity of NFTs. On the other hand, securitized NFTs can participate in De-Fi as an automated market maker (AMM), just like AMM in alternative tokens. However, when a participant with a portion of the NFT tries to obtain full ownership of the NFT, the acquisition process may face some obstacles, including strategic bidding. Therefore, we proposed a game theory model and de- signed a novel NFT repurchase mechanism to overcome these obstacles. Our solution helps to successfully carry out the repurchase process at a reasonable price when issuing single-chip NFT asset-backed securities.
Валерія Аркадіївна Динту, В. А. Динту, Валерия Аркадиевна Дынту, Валерия Аркадиевна Дынту · 9 authors
The purpose of the article is to explain the use of cryptocurrency as a terrorist financing tool. This article has emphasized the ways, which terrorists appraise for being a reliable fundraising means and their adoption. Methodology. For the purposes of the study, the methods of scientific abstraction, synthesis, observation, generalization, as well as the method of induction of literature and legal documents were used to determine the features of bitcoin, promoting and preventing its use for terrorism financing. Results. The development of the Internet and electronic devices has radically changed all spheres of human life, including criminal activity. Digitalization has led to the improvement of ordinary crimes and the emergence of new types of crime, which, in principle, cannot exist without special digital electronic devices. Among the first implementers of new technologies were terrorists, who took advantage of digitalization to increase profitability. Thus, terrorists have now significantly increased their attention to cryptocurrency as a digital means of payment, namely Bitcoin. Bitcoin has a number of features that have attracted the attention of criminals as a way to evade responsibility for a crime. In particular, decentralization avoids the need for confirmation by a central authority, and pseudo-anonymity provides a certain level of anonymity. In addition, terrorists are aware that Bitcoin's confidentiality is extremely fragile and needs to be enhanced. The paper analyzes several ways to enhance anonymity, such as software that anonymizes traffic and prevents IP identification, peer-to-peer mixers, centralized mixing services (tumbler), and other approaches. It is worth emphasizing that for the fight against crime, the main issue is the de-anonymization of the Bitcoin owner/user, which allows the identification of the criminal. Currently, law enforcement agencies use direct and indirect de-anonymization, proliferation analysis, quantitative analysis, time analysis, and transactional network analysis, among others, to achieve the above goal, which are discussed in detail in this article. In addition, agencies around the world investigated and uncovered terrorist groups and their financial facilitators. Specifically, on August 13, 2020, the U.S. Department of Justice's Office of Public Affairs announced "the largest cryptocurrency seizure in the context of terrorism in history." To investigate the agenda, the legal documents of those investigations that contain information about the terrorist fund-raising mechanism were examined and analyzed. The legal documents revealed that these investigations used the aforementioned de-anonymization approaches.
We survey large-scale cryptocurrency thefts over the past decade, estimating the total value of stolen cryptocurrencies. We find that depending on when the cryptocurrency was converted to fiat, the stolen amounts are valued between $7 to $88 billion, with longer cryptocurrency holding periods corresponding to the larger estimate due to the market’s rising value. We also document that as the cryptocurrency market develops, the incidence of large-scale cryptocurrency thefts has also increased in both number and stolen values in recent years. Large-scale thefts can be attributed to security breaches accounting for 20 of the 30 thefts, human errors accounting for five, and agency problems arising from insider thefts accounting for the remaining five.
Diego Cagigas, Judith Clifton, Daniel Díaz‐Fuentes, Marcos Fernández Gutiérrez
Blockchain is heralded as being “the next big thing” that promises to radically transform society and the economy in near-future applications. While scholarly literature on blockchain has largely focused on bitcoin and cryptofinance, in recent years, a body of scholarship has started to emerge on blockchain in the public sector. The characteristics of blockchain have made it a promising technology to transform many activities related to public policy and public service provision, such as administrative processes, welfare provision and regulation practices. This article provides, to the best of our knowledge, the first systematic literature review of the use of blockchain across all the main public services. This systematic review identifies the public services most likely to be impacted by the introduction of blockchain. It also highlights the main potential benefits, costs and risks of blockchain for government, civil servants and citizens. Governments are found to benefit mainly from improvements in efficiency and traceability, while regulatory uncertainty and questions around scalability represent major costs and risks for them. Civil servants, the least studied actor in the literature, could benefit from blockchain through the reduction of red tape and improvements in coordination between agencies. Their lack of blockchain knowledge and skills represent a major cost as regards adoption. Regarding citizens, security and transparency are identified as being the most important benefits, while risks are mainly associated with data security concerns. The article concludes by noting several limitations in the literature and providing suggestions towards fruitful lines of research.
Since the inception of Bitcoin in 2009, the market of cryptocurrencies has grown beyond the initial expectations, as witnessed by the thousands of tokenised assets available on the market, whose daily trades amount to dozens of USD billions. The pseudonymity features of these cryptocurrencies have attracted the attention of cybercriminals, who exploit them to carry out potentially untraceable scams. The wide range of cryptocurrency-based scams observed over the last ten years has fostered the research on the analysis of their effects, and the development of techniques to counter them. However, doing research in this field requires addressing several challenges: for instance, although a few data sources about cryptocurrency scams are publicly available, they often contain incomplete or misclassified data. Further, there is no standard taxonomy of scams, which leads to ambiguous and incoherent interpretations of their nature. Indeed, the unavailability of reliable datasets makes it difficult to train effective automatic classifiers that can detect and analyse cryptocurrency scams. In this paper, we perform an extensive review of the scientific literature on cryptocurrency scams, which we systematise according to a novel taxonomy. By collecting and homogenising data from different public sources, we build a uniform dataset of thousands of cryptocurrency scams.We devise an automatic tool that recognises scams and classifies them according to our taxonomy.We assess the effectiveness of our tool through standard performance metrics.We also give an in-depth analysis of the classification results, offering several insights into threat types, from their features to their connection with other types. Finally, we provide a set of guidelines that policymakers could follow to improve user protection against cryptocurrency scams.
Shanqing Yu, Jie Jin, Yunyi Xie, Jie Shen · 5 authors
With the rapid growth of blockchain, an increasing number of users have been attracted and many implementations have been refreshed in different fields. Especially in the cryptocurrency investment field, blockchain technology has shown vigorous vitality. However, along with the rise of online business, numerous fraudulent activities, e.g., money laundering, bribery, phishing, and others, emerge as the main threat to trading security. Due to the openness of Ethereum, researchers can easily access Ethereum transaction records and smart contracts, which brings unprecedented opportunities for Ethereum scams detection and analysis. This paper mainly focuses on the Ponzi scheme, a typical fraud, which has caused large property damage to the users in Ethereum. By verifying Ponzi contracts to maintain Ethereum's sustainable development, we model Ponzi scheme identification and detection as a node classification task. In this paper, we first collect target contracts' transactions to establish transaction networks and propose a detecting model based on graph convolutional network (GCN) to precisely distinguishPonzi contracts. Experiments on different real-world Ethereum datasets demonstrate that our proposed model has promising results compared with general machine learning methods to detect Ponzi schemes.
A growing stream of research finds several relations between the economic growth and corruption. Government implements various strategies to diminish the corruption and also technology often plays a dominant role to face it. Among various technologies alterations, Block-chain technology that becomes an effective and efficient way to resolve these issues related to corruption. This paper represents a brief knowledge related to the Block-Chain technology as it is a kind of distributed database or public ledger of all transactions that have been executed and shared among participating parties. Each and every transaction in the public ledger is verified by a majority of the participants registered in the system. The possible risk related to current scenario and give brief idea for resolving that problem with the help of blockchain technology. Non-Governmental Organization (NGOs) which aim is to tackle some of the issues faced by the society. NGO faces difficulty in terms of maintaining and gaining the support from donors in terms of funds. In recent decades there have been multiple examples of corruption misconduct scandals impacting the public image and reputation of NGOs. It is clear that trust of the people in NGOs is affected adversely by such events. Doubts arise in terms of where does the donation ends up? Who is leading the organization? Is donated money are used in a proper direction? So as a part of it, a need is raised to solve such issues for the betterment of the society. For the above stated problem regarding management of funds in NGOs, we propose a solution by using the Blockchain technology among various technologies alteration available. Blockchain offers the way to eliminate the doubts by providing data security, immutability and transparency. So, Blockchain Technology can offer the NGO industry to regain the trust of public.
The paper is devoted to the actual problem of ensuring privacy during performing transactions in distributed ledgers. We discuss various aspects of transaction privacy, as well as the specifics of setting the problem for distributed ledgers with two main models for representing participants' balances: the UTXO-model and the account model. Based on these results, we outline definitions and consider security properties of the main cryptographic primitives used for preserving the privacy of transactions: mixers, ring signatures, homomorphic encryption, and zero-knowledge proofs. We analyze well-known solutions for distributed ledgers based on the UTXO-model, such as Zcash, Monero, Zcoin, Dash, CoinShuffle, Verge, Grin, and others, as well as for systems based on the account model: DSC, Zether, Zeth, BlockMaze. Based on the comparison of advantages, disadvantages, and limitations for existing solutions, conclusions are drawn about the future development of distributed ledgers that ensure the privacy of transactions, and new research tasks are outlined.
This comprehensive overview of analysis techniques for illicit Bitcoin transactions addresses both technical, machine learning approaches as well as a non-technical, legal, and governance considerations. We focus on the field of ransomware countermeasures to illustrate our points.
The production of counterfeit money has a long history. It refers to the creation of imitation currency that is produced without the legal sanction of government. With the growth of the cryptocurrency ecosystem, there is expanding evidence that counterfeit cryptocurrency has also appeared. In this paper, we empirically explore the presence of counterfeit cryptocurrencies on Ethereum and measure their impact. By analyzing over 190K ERC-20 tokens (or cryptocurrencies) on Ethereum, we have identified $2,117$ counterfeit tokens that target 94 of the 100 most popular cryptocurrencies. We perform an end-to-end characterization of the counterfeit token ecosystem, including their popularity, creators and holders, fraudulent behaviors and advertising channels. Through this, we have identified two types of scams related to counterfeit tokens and devised techniques to identify such scams. We observe that over 7,104 victims were deceived in these scams, and the overall financial loss sums to a minimum of \$ 17 million (74,271.7 ETH). Our findings demonstrate the urgency to identify counterfeit cryptocurrencies and mitigate this threat.
Inspired from the iForest algorithmic scheme, we propose an iForest-based blockchain social media anomaly behavior detection method via the improved tree algorithm, for the purpose of isolating the anomalous behaviors as an outlier. The model is integrated with the smart contract structure of blockchain. In the overall system, the user data is sent to the intelligent contract for a period of time. After the identification of the abnormal behavior of social media users, the abnormal behavior in blockchain is marked and stored in the abnormal chain. To a certain extent, the scheme protects users' privacy, improves the efficiency and accuracy of iForest anomaly detection, and is more suitable for multi-dimensional heterogenous data-centric social media user behavior detection.
In agreements among anonymous users, smart contracts eliminate the need for a trusted intermediary and enforce its terms when the conditions set by the parties are met. Although smart contracts are mostly used for positive purposes, they have also been used for illegal activities due to their appealing characteristics in the criminal context. More specifically, a smart contract stimulates new forms of trustless collaboration among cybercriminals and the trend toward criminal use of smart contract can be more dangerous in collaborative attacks in terms of attacks' destructive power and sophistication.
In this paper, we present an architecture for real-world collaborative attacks based on criminal smart contracts (CSCs). We propose a CSC for the case of a collaborative distributed denial of service attack. In order to explore the feasibility and capture the characteristic of the attack-result, we formulate the attackers' interaction as an incomplete information game and prove that it has a unique dominant strategy equilibrium. We also model the proposed CSC as an incentive mechanism and prove that it is a strategy-proof and budget-balanced mechanism. Our numerical simulations show that the proposed incentive mechanism provides individual rationality and fairness to the collaborating attackers in its equilibrium.
Cryptocurrencies have revolutionized the process of trading in the digital world. Roughly one decade since the induction of the first bitcoin block, thousands of cryptocurrencies have been introduced. The anonymity offered by the cryptocurrencies also attracted the perpetuators of cybercrime. This paper attempts to examine the different machine learning approaches for efficiently identifying ransomware payments made to the operators using bitcoin transactions. Machine learning models may be developed based on patterns differentiating such cybercrime operations from normal bitcoin transactions in order to identify and report attacks. The machine learning approaches are evaluated on bitcoin ransomware dataset. Experimental results show that Gradient Boosting and XGBoost algorithms achieved better detection rate with respect to precision, recall and F-measure rates when compared with k-Nearest Neighbor, Random Forest, Naïve Bayes and Multilayer Perceptron approaches
The insider threats have always been one of the most severe challenges to cybersecurity. It can lead to the destruction of the organisation's internal network system and information leakage, which seriously threaten the confidentiality, integrity and availability of data. To make matters worse, since the attacker has authorized access to the internal network, they can launch the attack from the inside and erase their attack trace, which makes it challenging to track and forensics. A blockchain traceability system for insider threats is proposed in this paper to mitigate the issue. First, this paper constructs an insider threat model of the internal network from a different perspective: insider attack forensics and prevent insider attacker from escaping. Then, we analyze why it is difficult to track attackers and obtain evidence when an insider threat has occurred. After that, the blockchain traceability system is designed in terms of data structure, transaction structure, block structure, consensus algorithm, data storage algorithm, and query algorithm, while using differential privacy to protect user privacy. We deployed this blockchain traceability system and conducted experiments, and the results show that it can achieve the goal of mitigating insider threats.
Amsterdam Law Forum (ALF) is the student-run 'International Law Journal' of VU University. Every year ALF publishes a winter, spring, and summer issue. The journal consists of three sections; scientific articles, opinion articles, and commentaries. As of this year, ALF also creates a section for inaugural speeches. In addition, ALF hosts a conference in spring with a relevant legal theme, where renowned speakers are invited to share their perspectives. Overall, ALF is a topical journal that provides a platform for established scholars and young academics to share knowledge, opinions and experiences and to make contributions to the international law discourse. Staff, PhD students and master students who have written a very good thesis are invited to submit an article to ALF. What is learned in the cradle is carried to the tomb: we are looking forward to sharing your articles on our website!
Kripto paralar ekonomi ve finans dünyasında etkili bir rol oynamaya başladığından beri bu varlıklarla işlem yapan kişi ve kuruluşların ekonomik değeri haiz bu varlıkları koruması zorlaşmıştır. Merkezi bir otoriteye bağlı olmayan kripto paraların avantajları olduğu gibi dezavantajları da vardır. Kripto para birimleri normal bir banka kartı veya kredi kartı ile aynı yasal korumaya sahip olmadığından bu sistemin güvenlik zafiyetlerini bilen kötü niyetli kişiler teknolojinin yardımıyla kripto para dolandırıcılığı yapabilmektedir. Kripto para birimlerinin kendine özgü bir varlık değerinin bulunması ve uçtan uca şifreleme yöntemi ile aracı olmaksızın transfer edilebilme imkanı, kişilere önemli avantajlar sağlarken sanal para arzlarıyla, fidye yazılımlarla, phishing tuzakları ile ponzi şeması yöntemi veya internet ve sosyal medya reklamları ile kripto para dolandırıcılığı işlenebilmektedir. Çalışmamızda kripto para dolandırıcılığı suçuyla nasıl mücadele edileceği ve mevcut düzenlemelerle mukayese edildiğinde sanal ortamda icra edilen fiillerin hangi suçlara sebebiyet vereceği tartışılarak kripto para dolandırıcılığının ne şekilde işlendiği ve bu konuda nelere dikkat edilmesi gerektiği üzerinde durulacaktır.