Muhammad Saad, Jeffrey Spaulding, Laurent Njilla, Charles Kamhoua · 7 authors
In this paper, we systematically explore the attack surface of the Blockchain technology, with an emphasis on public Blockchains. Towards this goal, we attribute attack viability in the attack surface to 1) the Blockchain cryptographic constructs, 2) the distributed architecture of the systems using Blockchain, and 3) the Blockchain application context. To each of those contributing factors, we outline several attacks, including selfish mining, the 51% attack, DNS attacks, distributed denial-of-service (DDoS) attacks, consensus delay (due to selfish behavior or distributed denial-of-service attacks), Blockchain forks, orphaned and stale blocks, block ingestion, wallet thefts, smart contract attacks, and privacy attacks. We also explore the causal relationships between these attacks to demonstrate how various attack vectors are connected to one another. A secondary contribution of this work is outlining effective defense measures taken by the Blockchain technology or proposed by researchers to mitigate the effects of these attacks and patch associated vulnerabilities.
This article examines cryptocurrency cases decided in the U.S. District and Circuit Courts to determine the applicability of Gottschalk’s convenience theory of white-collar crime to cryptocurrency crime litigation and to empirically analyze whether the conditions under which cryptocurrency offenses occurred show support for the convenience theory. Analysis of U.S. federal district and circuit court case law involving cryptocurrency crimes and fraud indicates support for the convenience theory of white-collar crime. Defendants in various schemes were motivated by financial gain, either for the company or for personal use. Their roles and positions in the businesses allowed them access to resources that helped them perpetrate fraud through the following mechanisms: (1) operating front companies; (2) relationship building by defendants; (3) over representing profits that investors would obtain from purchases of virtual currencies, representing that cryptocurrencies were safe and reliable investments when they were risky, and overestimating abilities and capacities to provide services promised to investors in securities fraud; (4) breaching fiduciary duties to their clients and corporate stockholders by misappropriating profits for their own personal gain; and (5) engaging in dark web transactions that guaranteed anonymity. Defendants also employed various neutralization techniques to justify their crimes.
In recent years, the emergence of blockchain technology (BT) has become a unique, most disruptive, and trending technology. The decentralized database in BT emphasizes data security and privacy. Also, the consensus mechanism in it makes sure that data is secured and legitimate. Still, it raises new security issues such as majority attack and double-spending. To handle the aforementioned issues, data analytics is required on blockchain based secure data. Analytics on these data raises the importance of arisen technology Machine Learning (ML). ML involves the rational amount of data to make precise decisions. Data reliability and its sharing are very crucial in ML to improve the accuracy of results. The combination of these two technologies (ML and BT) can provide highly precise results. In this paper, we present a detailed study on ML adoption for making BT-based smart applications more resilient against attacks. There are various traditional ML techniques, for instance, Support Vector Machines (SVM), clustering, bagging, and Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN) and Long short-term memory (LSTM) can be used to analyse the attacks on a blockchain-based network. Further, we include how both the technologies can be applied in several smart applications such as Unmanned Aerial Vehicle (UAV), Smart Grid (SG), healthcare, and smart cities. Then, future research issues and challenges are explored. At last, a case study is presented with a conclusion.
Abstract When bitcoin was released by the mysterious Satoshi Nakamoto in 2008, few could have predicted that it would attract as much attention as it has today. It has spawned a veritable host of other cryptocurrencies, including ether on the upstart Ethereum network, which boasts smart contract functionality. The underlying blockchain technology has also attracted attention, with some within the blockchain community suggesting that it can solve such diverse problems as secured digital voting to tracking food provenance. In the legal context, blockchains have been envisaged as capable of revolutionising registries for assets ranging from land to intellectual property, modernising clearing and settlement, and even fundamentally transforming the contracting process. This article critically evaluates the popular claims surrounding the potential of blockchain technologies to disrupt the legal system by separating hype from fact.
Smart contracts and blockchain technology have the potential to change tremendously the contractual practices. Together with the development of blockchain-based technology, many well-known companies and other private or public entities such as governments take advantage of smart contracts. On the one hand, there are costs connected with the programming and coding of smart contracts, or training those administering them, on the other hand, however, it seems to be true that smart contracts will bring greater certainty and extra cost saving for those who apply them in their business dealings. During recent years, rapid technological development has resulted in plenty of changes in the way how financial services are conducted. Blockchain technology is predicted to disrupt the current environment of business dealings by enabling the unprecedented ways to cooperate and communicate between the parties of the contract.
A U Mentsiev, V S Magomadov, M Z Ashakhanova, A U Mentsiev · 5 authors
Abstract This research gives a brief insight into one of the most widely used technologies known as Blockchain. The paper lays the groundwork of how Blockchain functions and how it has quickly become a network with millions of users throughout the world. This study discusses the most important use of Blockchain technology, which is the enhancement of the cyber-security industry. Blockchain has revolutionized the cyber-security industry by introducing a system which is not bound to any four walls and is widely distributed all around the world, seeking refuge on millions of user servers. This decentralized system helps Blockchain defend cyber networks against security threats and attacks such as malware, phishing attacks, and DDoS.
Know Your Customer (KYC) is done as a mandatory entry step into any financial institution. However, even today the amount of manual intervention involved in the process is staggering. Often the data is centrally stored, and the computer programs acting are also centrally governed thus are not tamperproof making them susceptible to vulnerabilities and attacks. Different organizations do not have a unified application where the KYC information can be seamlessly shared between them without any risk of repudiation from any of the participating organizations. Our application, that is based on blockchain technology, aims to provide this platform as a service to financial institutions as an electronic-KYC solution, in the process of making the life of the end consumer easier.
Ayman Alkhalifah, Alex Ng, Mohammad Jabed Morshed Chowdhury, A. S. M. Kayes · 5 authors
The establishment of Bitcoin in 2008 has introduced Blockchain technology to become the predominant new ways in digital transactions in many fields, such as energy, healthcare, and financial services. Although blockchain technology promised many advantages, it has suffered from various attacks with significant financial loses. We analyse 65 cybersecurity incidents that have been happening on the blockchain networks between 2011 and the first half-year of 2019. We categorize those incidents against the key cybersecurity vulnerabilities in blockchain technology, which showed an upward trend of an increasing number of attacks which dues to lack of security controls in the digital currency exchange and vulnerability in smart contracts. This result of this research highlights the needs in a major overhaul of the blockchain technology.
Mrunali Chopade, Sana Khan, Uzma Shaikh, Renuka Pawar
The fundamental aim of digital forensics is to discover, investigate and protect an evidence, increasing cybercrime enforces digital forensics team to have more accurate evidence handling. This makes digital evidence as an important factor to link individual with criminal activity. In this procedure of forensics investigation, maintaining integrity of the evidence plays an important role. A chain of custody refers to a process of recording and preserving details of digital evidence from collection to presenting in court of law. It becomes a necessary objective to ensure that the evidence provided to the court remains original and authentic without tampering. Aim is to transfer these digital evidences securely using encryption techniques.
Block chain has drawn major attention recently in the area of cyber security. Till to date many detection techniques and protection mechanisms have been proposed to enhance the security. In this paper we have proposed an overview of distributed ledger framework, block chain to defense against the cyber attacks. We here present a comprehensive overview of block chain architecture and some major algorithms used in different block chains. The future trends of block chain technology in the applications such as IoT security, E-voting, Banking sector has also coined out in this paper.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Abeer ElBahrawy, Laura Alessandretti, Leonid Rusnac, Daniel Goldsmith · 6 authors
Dark markets are commercial websites that use Bitcoin to sell or broker transactions involving\ndrugs, weapons, and other illicit goods. Being illegal, they do not offer any user protection, and\nseveral police raids and scams have caused large losses to both customers and vendors over the past\nyears. However, this uncertainty has not prevented a steady growth of the dark market phenomenon\nand a proliferation of new markets. The origin of this resilience have remained unclear so far, also due\nto the difficulty of identifying relevant Bitcoin transaction data. Here, we investigate how the dark\nmarket ecosystem re-organises following the disappearance of a market, due to factors including raids\nand scams. To do so, we analyse 24 episodes of unexpected market closure through a novel datasets\nof 133 million Bitcoin transactions involving 31 dark markets and their users, totalling 4 billion USD.\nWe show that coordinated user migration from the closed market to coexisting markets guarantees\noverall systemic resilience beyond the intrinsic fragility of individual markets. The migration is\nswift, efficient and common to all market closures. We find that migrants are on average more active\nusers in comparison to non-migrants and move preferentially towards the coexisting market with\nthe highest trading volume. Our findings shed light on the resilience of the dark market ecosystem\nand we anticipate that they may inform future research on the self-organisation of emerging online\nmarkets.
Alex Groce, Josselin Feist, Gustavo Grieco, Michael Colburn
An important problem in smart contract security is understanding the likelihood and criticality of discovered, or potential, weaknesses in contracts. In this paper we provide a summary of Ethereum smart contract audits performed for 23 professional stakeholders, avoiding the common problem of reporting issues mostly prevalent in low-quality contracts. These audits were performed at a leading company in blockchain security, using both open-source and proprietary tools, as well as human code analysis performed by professional security engineers. We categorize 246 individual defects, making it possible to compare the severity and frequency of different vulnerability types, compare smart contract and non-smart contract flaws, and to estimate the efficacy of automated vulnerability detection approaches.
Alex Groce, Josselin Feist, Gustavo Grieco, Michael D. Colburn
An important problem in smart contract security is understanding the\nlikelihood and criticality of discovered, or potential, weaknesses in\ncontracts. In this paper we provide a summary of Ethereum smart contract audits\nperformed for 23 professional stakeholders, avoiding the common problem of\nreporting issues mostly prevalent in low-quality contracts. These audits were\nperformed at a leading company in blockchain security, using both open-source\nand proprietary tools, as well as human code analysis performed by professional\nsecurity engineers. We categorize 246 individual defects, making it possible to\ncompare the severity and frequency of different vulnerability types, compare\nsmart contract and non-smart contract flaws, and to estimate the efficacy of\nautomated vulnerability detection approaches.\n
Protecting sensitive medical data, including prescription and pill data, during its handling and storage is critical in the digital era. Data vaults that protect privacy provide a strong way to protect this information, guaranteeing that patient information is kept private but yet available for authorised uses. Focussing on the safe preservation of pharmaceutical data, this project investigates the creation of sophisticated algorithms for privacy-preserving data vaults. We start by contrasting the suggested innovative technique, which combines elements of Zero-Knowledge Proofs with Enhanced Homomorphic Encryption, with other known cryptographic and data masking algorithms, such as Differential Privacy, Secure Multi-Party Computation, and Homomorphic Encryption. Data integrity, computational efficiency, and resistance to different attack vectors are some of the characteristics used in the comparison.As the results show, the suggested method offers aimproved performance against confidentiality compromises, especially in real-time data retrieval scenarios, while existing techniques offer varied degrees of efficiency and security. But this comes with more implementation complexity and processing overhead. Improved security characteristics, like less data leakage and strong user authentication systems, are benefits of the suggested approach. Large-scale applications may experience latency problems and require more powerful hardware, which are drawbacks.The trade-offs between various data privacy strategies are highlighted in this study, and it also highlights the necessity for on-going innovation in privacy-preserving technology, which makes a contribution to the region.
Cybercriminals exploit cryptocurrencies to carry out illicit activities. In this paper, we focus on Ponzi schemes that operate on Bitcoin and perform an in-depth analysis of MMM, one of the oldest and most popular Ponzi schemes. Based on 423K transactions involving 16K addresses, we show that: (1) Starting Sep 2014, the scheme goes through three phases over three years. At its peak, MMM circulated more than 150M dollars a day, after which it collapsed by the end of Jun 2016. (2) There is a high income inequality between MMM members, with the daily Gini index reaching more than 0.9. The scheme also exhibits a zero-sum investment model, in which one member's loss is another member's gain. The percentage of victims who never made any profit has grown from 0% to 41% in five months, during which the top-earning scammer has made 765K dollars in profit. (3) The scheme has a global reach with 80 different member countries but a highly-asymmetrical flow of money between them. While India and Indonesia have the largest pairwise flow in MMM, members in Indonesia have received 12x more money than they have sent to their counterparts in India.
Cybercriminals exploit cryptocurrencies, such as Bitcoin, to carry out
various illicit activities. In this paper, we focus on Ponzi schemes that
operate on Bitcoin and perform an in-depth analysis of MMM, one of the oldest
and most popular Ponzi schemes. Based on 423K transactions involving 16K
addresses, we show that: (1) Starting Sep 2014, the scheme goes through three
phases over three years. At its peak, MMM circulated more than 150M dollars a
day, after which it collapsed by the end of Jun 2016. (2) There is a high
income inequality among MMM members, with the daily Gini index reaching more
than 0.9. The scheme also exhibits a zero-sum investment model, in which one
member's loss is another member's gain. The percentage of victims who never
made any profit has grown from 0% to 41% in five months, during which the
top-earning scammer has made 765K dollars in profit. (3) The scheme has a
global reach with 80 different member countries, but a highly-asymmetrical flow
of money between them. While India and Indonesia have the largest pairwise flow
in MMM, members in Indonesia have received 12x more money than they have sent
to their counterparts in India.
Bitcoin is the most successful cryptocurrency with more than half of the market capitalization of all more than 2,000 currently existing cryptocurrencies. In recent years, there have been several high-profile hacks and scams that resulted in billions of stolen funds. In this paper, we focus on the impact of Bitcoin cybersecurity and privacy characteristics on its adoption. A survey (N = 152) has been conducted among users and non-users of Bitcoin in Slovenia to test the proposed research model. The results suggest that in addition to known factors (i.e., usefulness, ease of use and subjective norm) trust into Bitcoin security also influences Bitcoin adoption. The results however show no support for the influence of perceived threat of Bitcoin scams or Bitcoin anonymity on Bitcoin adoption.
Masarah Paquet-Clouston, Matteo Romiti, Bernhard Haslhofer, Thomas Charvat
In the past year, a new spamming scheme has emerged: sexual extortion messages requiring payments in the cryptocurrency Bitcoin, also known as sextortion. This scheme represents a first integration of the use of cryptocurrencies by members of the spamming industry. Using a dataset of 4,340,736 sextortion spams, this research aims at understanding such new amalgamation by uncovering spammers' operations. To do so, a simple, yet effective method for projecting Bitcoin addresses mentioned in sextortion spams onto transaction graph abstractions is computed over the entire Bitcoin blockchain. This allows us to track and investigate monetary flows between involved actors and gain insights into the financial structure of sextortion campaigns. We find that sextortion spammers are somewhat sophisticated, following pricing strategies and benefiting from cost reductions as their operations cut the upper-tail of the spamming supply chain. We discover that one single entity is likely controlling the financial backbone of the majority of the sextortion campaigns and that the 11-month operation studied yielded a lower-bound revenue between $1,300,620 and $1,352,266. We conclude that sextortion spamming is a lucrative business and spammers will likely continue to send bulk emails that try to extort money through cryptocurrencies.
In this explorative study we provide empirical insight into how organized crime offenders use IT to launder their money. Our empirical data consist of 30 large-scale criminal investigations into organized crime. These cases are part of the most recent, fifth data sweep of the Dutch Organized Crime Monitor (DOCM). We do not focus on cybercrime alone. Instead, we explore the financial aspects of criminal operations in a broad range of types of organized crime, i.e. from ‘traditional’ types of organized crime, such as offline drug smuggling, to cybercrime. Regarding the spending of criminal proceeds (consumption and investment), the analyses show several similarities and no major differences between traditional crime and cybercrime. When it comes to concealing criminal earnings (money laundering), we do see important differences. Financial innovation, such as the use of cryptocurrencies, seems to be limited to cases of IT-related crime. One of the most striking similarities between cybercrime and traditional crime is the offenders’ preference for cash. In the analysed cases, malware and phishing offenders as well as online drug traffickers change their digital currencies for cash, at least in part.
Due to increasing popularity of Bitcoin and other cryptocurrencies, proliferation of deceptive cryptocurrencies over the internet is a global concern. In this paper, we have identified a set of 24 features through analyzing Cryptocurrency Market Capitalization (CMC) data and propose a Multilayer Perceptron (MLP) architecture for detecting deceptive cryptocurrencies. The proposed MLP architecture is compared with three traditional machine learning algorithms over a real cryptocurrency dataset crawled from CMC website, and it performs significantly better.
Purpose This paper aims to demonstrate the utility of a target-centric approach to intelligence collection and analysis in the prevention and investigation of ransomware attacks that involve cryptocurrencies. The paper uses the May 2017 WannaCry ransomware usage of the Bitcoin ecosystem as a case study. The approach proves particularly beneficial in facilitating information sharing and an integrated analysis across intelligence domains. Design/methodology/approach This study conducted data collection and analysis of the component Bitcoin elements of the WannaCry ransomware attack. A note of both technicalities of Bitcoin operations and current models for sharing cyber intelligence was made. Our analysis builds on and further develops current definitions and strategies for sharing cyber threat intelligence. It uses the problem definition model (PDM) and generic target network model (TNM) to create an analytic framework for the WannaCry ransomware attack scenario, allowing analysts the ability to test their hypotheses and integrate and share data for collaborative investigation. Findings Using a target-centric intelligence approach to WannaCry 2.0 shows that it is possible to model the intelligence problem of collecting and analysing data related to inflows and outflows of Bitcoin-related ransomware transactions. Bitcoin transactions form graph networks and allow to build a target network model for collecting, analysing and sharing intelligence with multiple stakeholders. Although attribution and anonymity prevail under cryptocurrency usage, there is a means for developing transaction walks using this method to target nefarious cryptocurrency exchanges where criminals are inclined to cash out their proceeds of crime. Originality/value The application of a target-centric intelligence approach to the cryptocurrency components of a ransomware attack provides a framework for intelligence units to break down the problem in the financial domain and model the network behaviour of illicit Bitcoin transactions relating to ransomware.