Blockchain technology becomes increasingly popular. It also attracts scams, for example, a Ponzi scheme, a classic fraud, has been found making a notable amount of money on Blockchain, which has a very negative impact. To help to deal with this issue and to provide reusable research data sets for future research, this paper collects real-world samples and proposes an approach to detect Ponzi schemes implemented as smart contracts (i.e., smart Ponzi schemes) on the blockchain. First, 200 smart Ponzi schemes are obtained by manually checking more than 3,000 open source smart contracts on the Ethereum platform. Then, two kinds of features are extracted from the transaction history and operation codes of the smart contracts. Finally, a classification model is presented to detect smart Ponzi schemes. The extensive experiments show that the proposed model performs better than many traditional classification models and can achieve high accuracy for practical use. By using the proposed approach, we estimate that there are more than 500 smart Ponzi schemes running on Ethereum. Based on these results, we propose to build a uniform platform to evaluate and monitor every created smart contract for early warning of scams.
The electronic voting has emerged over time as a replacement to the paper-based voting to reduce the redundancies and inconsistencies. The historical perspective presented in the last two decades suggests that it has not been so successful due to the security and privacy flaws observed over time. This paper suggests a framework by using effective hashing techniques to ensure the security of the data. The concept of block creation and block sealing is introduced in this paper. The introduction of a block sealing concept helps in making the blockchain adjustable to meet the need of the polling process. The use of consortium blockchain is suggested, which ensures that the blockchain is owned by a governing body (e.g., election commission), and no unauthorized access can be made from outside. The framework proposed in this paper discusses the effectiveness of the polling process, hashing algorithms' utility, block creation and sealing, data accumulation, and result declaration by using the adjustable blockchain method. This paper claims to apprehend the security and data management challenges in blockchain and provides an improved manifestation of the electronic voting process.
The online social networks facilitate naturally for the users to share information. On these platforms, each user shares information based on his or her interests. The particular information being shared by a user may be legitimate or fake. Sometimes a misinformation, propagated by users and group can create chaos or in some cases, might leads to cases of riots. Nowadays the third party like ALT news and Cobrapost check the information authenticity, but it takes too much time to validate the news. Therefore, a robust and new system is required to check the information authenticity within the network, to stop the propagation of misinformation. In this paper, we propose a blockchain based framework for sharing the information securely at the peer level. In the blockchain model, a chain is created by combining blocks of information. Each node of network propagates the information based on its credibility to its peer nodes. The credibility of a node will vary according to the respective information. Trust is calculated between sender and receiver in two ways:(i) Local trust used for sharing information at the peer level and (ii) global trust is used for a credibility check of each user in the network. We evaluate our framework using real dataset derived from Facebook. Our approach achieves an accuracy of 83% which shows the effectiveness of our proposed framework.
Ali Dorri, Clemence Roulin, Raja Jurdak, Salil S. Kanhere
Security is one of the fundamental challenges in the Internet of Things (IoT) due to the heterogeneity and resource constraints of the IoT devices. Device classification methods are employed to enhance the security of IoT by detecting unregistered devices or traffic patterns. In recent years, blockchain has received tremendous attention as a distributed trustless platform to enhance the security of IoT. Conventional device identification methods are not directly applicable in blockchain-based IoT as network layer packets are not stored in the blockchain. Moreover, the transactions are broadcast and thus have no destination IP address and contain a public key as the user identity, and are stored permanently in blockchain which can be read by any entity in the network. We show that device identification in blockchain introduces privacy risks as the malicious nodes can identify users' activity pattern by analyzing the temporal pattern of their transactions in the blockchain. We study the likelihood of classifying IoT devices by analyzing their information stored in the blockchain, which to the best of our knowledge, is the first work of its kind. We use a smart home as a representative IoT scenario. First, a blockchain is populated according to a real-world smart home traffic dataset. We then apply machine learning algorithms on the data stored in the blockchain to analyze the success rate of device classification, modeling both an informed and a blind attacker. Our results demonstrate success rates over 90\% in classifying devices. We propose three timestamp obfuscation methods, namely combining multiple packets into a single transaction, merging ledgers of multiple devices, and randomly delaying transactions, to reduce the success rate in classifying devices. The proposed timestamp obfuscation methods can reduce the classification success rates to as low as 20%.
The adoption of blockchain technology is taking place at a fast pace. Security features inherent in blockchain make it resistant to attack, but they do not make it immune, and blockchain security risks do exists. This paper details the associated risks and concerns of the blockchain. We explore relevant standards and regulations related to blockchain and survey and analyze 38 blockchain incidents to determine the root cause to provide a view of the most frequent vulnerabilities exploited. The paper reviews six of these 38 incidents in greater detail. The selection is made by choosing incidents with the most frequent root cause. In the review of the incidents, the paper details what happened and why and aims to address what could have been done to mitigate the attack. The paper concludes with a recommendation on a framework to reduce cyber security risks when using blockchain technologies.
With the more and more extensive application of blockchain, blockchain security has been widely concerned by the society and deeply studied by scholars. Moreover, the security of blockchain data directly affects the security of various applications of blockchain. In this survey, we perform a comprehensive classification and summary of the security of blockchain data. First, we present classification of blockchain data attacks. Subsequently, we present the attacks and defenses of blockchain data in terms of privacy, availability, integrity and controllability. Data privacy attacks present data leakage or data obtained by attackers through analysis. Data availability attacks present abnormal or incorrect access to blockchain data. Data integrity attacks present blockchain data being tampered. Data controllability attacks present blockchain data accidentally manipulated by smart contract vulnerability. Finally, we present several important open research directions to identify follow-up studies in this area.
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Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Existing education systems are facing a threat of question paper leaking\n(QPL) in the exam which jeopardizes the quality of education. Therefore, it is\nhigh time to think about a more secure and flexible question sharing system\nwhich can prevent QPL issue in the future education system. Blockchain enables\na way of creating and storing transactions, contracts or anything that requires\nprotection against tampering, accessing etc. This paper presents a new scheme\nfor smart education, by utilizing the concept of blockchain, for question\nsharing. A two-phase encryption technique for encrypting question paper (QSP)\nis proposed. In the first phase, QSPs are encrypted using timestamp and in the\nsecond phase, previous encrypted QSPs are encrypted again using a timestamp,\nsalt hash and hashes from previous QSPs. These encrypted QSPs are stored in the\nblockchain along with a smart contract which helps the user to unlock the\nselected QSP. An algorithm is also proposed for selecting a QSP for the exam\nwhich picks a QSP randomly. Moreover, a timestamp based lock is imposed on the\nscheme so that no one can decrypt the QSP before the allowed time. Finally,\nsecurity is analyzed by proving different propositions and the superiority of\nthe proposed scheme over existing schemes is proven through a comparative study\nbased on the different features.\n
The cryptocurrency is attracting more and more attention because of the blockchain technology. Ethereum is gaining a significant popularity in blockchain community, mainly due to the fact that it is designed in a way that enables developers to write smart contracts and decentralized applications (Dapps). There are many kinds of cryptocurrency information on the social network. The risks and fraud problems behind it have pushed many countries including the United States, South Korea, and China to make warnings and set up corresponding regulations. However, the security of Ethereum smart contracts has not gained much attention. Through the Deep Learning approach, we propose a method of sentiment analysis for Ethereum's community comments. In this research, we first collected the users' cryptocurrency comments from the social network and then fed to our LSTM + CNN model for training. Then we made prediction through sentiment analysis. With our research result, we have demonstrated that both the precision and the recall of sentiment analysis can achieve 0.80+. More importantly, we deploy our sentiment analysis1 on RatingToken and Coin Master (mobile application of Cheetah Mobile Blockchain Security Center23). We can effectively provide detail information to resolve the risks of being fake and fraud problems.
The problem of transparency in donation systems has long been a topic for discussion. However, the emphasis on transparency raises privacy concerns for donors and recipients, with some people attempting to hide donations or the receipt of money. Therefore, a donation system that guarantees transparency and privacy is required to avoid negative side effects. In this study, we developed a system that protects personal information by using a one-time account address system based on a blockchain while emphasizing transparency. The developed system could contribute to the creation of a sustainable and safe donation environment and culture.
Wesley Joon-Wie Tann, Xing Han, Sourav Sen Gupta, Yew-Soon Ong
Symbolic analysis of security exploits in smart contracts has demonstrated to be valuable for analyzing predefined vulnerability properties. While some symbolic tools perform complex analysis steps, they require a predetermined invocation depth to search vulnerable execution paths, and the search time increases with depth. The number of contracts on blockchains like Ethereum has increased 176 fold since December 2015. If these symbolic tools fail to analyze the increasingly large number of contracts in time, entire classes of exploits could cause irrevocable damage. In this paper, we aim to have safer smart contracts against emerging threats. We propose the approach of sequential learning of smart contract weaknesses using machine learning---long-short term memory (LSTM)---that allows us to be able to detect new attack trends relatively quickly, leading to safer smart contracts. Our experimental studies on 620,000 smart contracts prove that our model can easily scale to analyze a massive amount of contracts; that is, the LSTM maintains near constant analysis time as contracts increase in complexity. In addition, our approach achieves $99\%$ test accuracy and correctly analyzes contracts that were false positive (FP) errors made by a symbolic tool.
There has been increased interest in the use of blockchains to control Internet of Things devices either directly, or through smart contracts. Many blockchains, such as Ethereum and Fabric, have support for smart contracts. The use of public blockchains while attractive due to their decentralization and availability, do pose challenges, such as unpredictable transaction latencies and cryptocurrency price fluctuations. Transactions in the Ethereum network, such as invokations of smart contracts used to control an IoT device, have no fairness or eventuality guarantees. In this work we describe a “spam attack” method available to parties with sufficient cryptocurrency reserves to delay a statistically significant portion of transactions submitted to the Ethereum network. This paper derives estimations on the costs and effects of such an attack, and is based on an analysis of historical transactions.
The Bitcoin network is a complex network that records anonymous financial transactions while encapsulating the relationships among its pseudonymous users. This paper proposes graph mining techniques to explore the relationships among wallet addresses (pseudonyms for Bitcoin users) suspected to be involved in a given extortion racket, exploiting the anonymity of the Bitcoin network to collect and launder money. Starting around Bitcoin addresses of potential interest, neighborhood subgraphs are analyzed in terms of path length and confluence to detect suspicious Bitcoin flow and other wallet addresses controlled by the suspected perpetrators. We show with a dataset of the Ashley Madison blackmail campaign from August 2015 how the mechanisms can be used both to estimate the amount of money that was extorted by the suspected perpetrators under the specific blackmail campaign, and also estimate the amount of money handled by them during the same period of time.
Marc Jourdan, Sébastien Blandin, Laura Wynter, Pralhad Deshpande
Bitcoin has created a new exchange paradigm within which financial transactions can be trusted without an intermediary. This premise of a free decentralized transactional network however requires, in its current implementation, unrestricted access to the ledger for peer-based transaction verification. A number of studies have shown that, in this pseudonymous context, identities can be leaked based on transaction features or off-network information. In this work, we analyze the information revealed by the pattern of transactions in the neighborhood of a given entity transaction. By definition, these features which pertain to an extended network are not directly controllable by the entity, but might enable leakage of information about transacting entities. We define a number of new features relevant to entity characterization on the Bitcoin Blockchain and study their efficacy in practice. We show that even a weak attacker with shallow data mining knowledge is able to leverage these features to characterize the entity properties.
A wave of alternative coins that can be effectively mined without specialized hardware, and a surge in cryptocurrencies' market value has led to the development of cryptocurrency mining ( cryptomining ) services, such as Coinhive, which can be easily integrated into websites to monetize the computational power of their visitors. While legitimate website operators are exploring these services as an alternative to advertisements, they have also drawn the attention of cybercriminals: drive-by mining (also known as cryptojacking ) is a new web-based attack, in which an infected website secretly executes JavaScript code and/or a WebAssembly module in the user's browser to mine cryptocurrencies without her consent. In this paper, we perform a comprehensive analysis on Alexa's Top 1 Million websites to shed light on the prevalence and profitability of this attack. We study the websites affected by drive-by mining to understand the techniques being used to evade detection, and the latest web technologies being exploited to efficiently mine cryptocurrency. As a result of our study, which covers 28 Coinhive-like services that are widely being used by drive-by mining websites, we identified 20 active cryptomining campaigns. Motivated by our findings, we investigate possible countermeasures against this type of attack. We discuss how current blacklisting approaches and heuristics based on CPU usage are insufficient, and present MineSweeper, a novel detection technique that is based on the intrinsic characteristics of cryptomining code, and, thus, is resilient to obfuscation. Our approach could be integrated into browsers to warn users about silent cryptomining when visiting websites that do not ask for their consent.
Oct 1, 2018·2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
The widespread adoption of fax machines in the 1980s revolutionised everyday communications. It was quickly adopted as the standard form of communication across the globe. Since then, the internet has replaced fax as a truly global form of instant communication. However, the fax machine still reigns as the primary form of communication in a number of industries, healthcare being one of them. This paper presents a system that uses a blockchain and an off-chain centralised data storage to give patients and medical professionals instant access to their medical records from anywhere. By assigning each medical record a pseudo anonymous identifier, a second layer "blockchain" for each user can be created allowing for the rapid collection and querying of data. The off-chain pseudo anonymous data storage allows for the data to remain unencrypted enabling the rapid generation of anonymous medical datasets which can be used for machine learning and data mining on the data, potentially bringing many benefits to the healthcare industry.
Background/Objectives: The blockchain has been applied to many fields. Users are concerned about its security. The primary goal of this study is supporting the security service to protect DDoS attack.Methods/Statistical analysis: To provide security service in the blockchain, the security model is suggested. This model can filter out illegitimate traffic and exchange information with other security switches to determine whether a connected node is a normal node or an abnormal node. Each procedure of the proposed model has been described. Also, two different attack types are used to show the operation process of suggested model.Findings: Cyberattacks attempting to impact technology services availability continue to increase. Thus, DDoS is one of the most common type of attacks can also cause the most disruption to internet services. But blockchain has the characteristics of decentralization and peer to peer. This makes it harder to disrupt than conventional distributed application. Nevertheless, DDoS attacks remain a persistent threat. Therefore, a security model is suggested which can effectively block and respond to DDoS attacks.Improvements/Applications: The suggested model makes it possible to protect the spam transaction attacks in blockchain network.
Marius Musch, Christian Wressnegger, Martin Johns, Konrad Rieck
With the introduction of memory-bound cryptocurrencies, such as Monero, the implementation of mining code in browser-based JavaScript has become a worthwhile alternative to dedicated mining rigs. Based on this technology, a new form of parasitic computing, widely called cryptojacking or drive-by mining, has gained momentum in the web. A cryptojacking site abuses the computing resources of its visitors to covertly mine for cryptocurrencies. In this paper, we systematically explore this phenomenon. For this, we propose a 3-phase analysis approach, which enables us to identify mining scripts and conduct a large-scale study on the prevalence of cryptojacking in the Alexa 1 million websites. We find that cryptojacking is common, with currently 1 out of 500 sites hosting a mining script. Moreover, we perform several secondary analyses to gain insight into the cryptojacking landscape, including a measurement of code characteristics, an estimate of expected mining revenue, and an evaluation of current blacklist-based countermeasures.
The exponential growth of wireless-based solutions, such as those related to the mobile smart devices (e.g., smart-phones and tablets) and Internet of Things (IoT) devices, has lead to countless advantages in every area of our society. Such a scenario has transformed the world a few decades back, dominated by latency, into a new world based on an efficient real-time interaction paradigm.Recently, cryptocurrency have contributed to this technological revolution, the fulcrum of which are a decentralization model and a certification function offered by the so-called blockchain infrastructure, which make it possible to certify the financial transactions, anonymously. However, it should be observed how this challenging scenario has generated new security problems directly related to the involved new technologies (e.g., e-commerce frauds, mobile bot-net attacks, blockchain DoS attacks, cryptocurrency scams, etc.). In this context, we can acknowledge that the scientific community efforts are usually oriented toward specific solutions, instead to exploit all the available technologies, synergistically, in order to define more efficient security paradigms. This paper aims to indicate a possible approach able to improve the security of people and things by introducing a novel paradigm to security defined Internet of Entities (IoE). It is a mechanism for the localization of people and things, which exploits both the huge number of existing wireless-based devices and the blockchain-based distributed ledger technology, overcoming the limits of traditional localization approaches, but without jeopardizing the user privacy. Its operation is based on two core elements with interchangeable roles, entities and trackers, which can be very common elements such as smart-phones, tablets, and IoT devices, and its implementation requires minimal efforts thanks to the existing infrastructures and devices.
In recent years, the Darknet has become one of the most discussed topics in cyber security circles. Current academic studies and media reports tend to highlight how the anonymous nature of the Darknet is used to facilitate criminal activities. This paper reports on a recent research in four Darknet forums that reveals a different aspect of the Darknet. Drawing on our qualitative findings, we suggest that many users of the Darknet might not perceive it as intrinsically criminogenic, despite their acknowledgement of various kinds of criminal activity in this network. Further, our research participants emphasised on the achievement of constructive socio-political values through the use of the Darknet. This achievement is enabled by various characteristics that are rooted in the Darknet’s technological structure, such as anonymity, privacy, and the use of cryptocurrencies. These characteristics provide a wide range of opportunities for good as well as for evil.
Aug 2, 2018·Jan Rüth, Torsten Zimmermann, Konrad Wolsing, and Oliver Hohlfeld. 2018. Digging into Browser-based Crypto Mining. In IMC '18: Internet Measurement Conference, October 31-November 2, 2018, Boston, MA, USA. ACM, New York, NY, USA, 7 pages
Jan Rüth, Torsten Zimmermann, Konrad Wolsing, Oliver Hohlfeld
Mining is the foundation of blockchain-based cryptocurrencies such as Bitcoin rewarding the miner for finding blocks for new transactions. The Monero currency enables mining with standard hardware in contrast to special hardware (ASICs) as often used in Bitcoin, paving the way for in-browser mining as a new revenue model for website operators. In this work, we study the prevalence of this new phenomenon. We identify and classify mining websites in 138M domains and present a new fingerprinting method which finds up to a factor of 5.7 more miners than publicly available block lists. Our work identifies and dissects Coinhive as the major browser-mining stakeholder. Further, we present a new method to associate mined blocks in the Monero blockchain to mining pools and uncover that Coinhive currently contributes 1.18% of mined blocks having turned over 1293 Moneros in June 2018.
Cryptocurrencies like Bitcoin not only provide a decentralized currency, but also provide a programmatic way to process transactions. Ethereum, the second largest cryptocurrency next to Bitcoin, is the first to provide a Turing-complete language to specify transaction processing, thereby enabling so-called smart contracts. This provides an opportune setting for attackers, as security vulnerabilities are tightly intertwined with financial gain. In this paper, we consider the problem of automatic vulnerability identification and exploit generation for smart contracts. We develop a generic definition of vulnerable contracts and use this to build TEE THER, a tool that allows creating an exploit for a contract given only its binary bytecode. We perform a large-scale analysis of all 38,757 unique Ethereum contracts, 815 out of which our tool finds working exploits for—completely automated.
Banking malware is malicious software that aims to steal money from victims via manipulated bank transfers in online banking. This paper describes how the profits of banking malware are generated and subsequently laundered, with a particular focus on the use of bitcoins and other digital payment methods. Computers are infected with banking malware via phishing emails, in which people are persuaded in various ways to click on links or open attachments, or via exploit kits, programs that try to find weak spots in the security of computer systems. After infection, bank transfers of the online banking accounts of victims are manipulated via fake website screens (web injects). Behind the screens the amounts and beneficiaries of transactions are modified, emptying the victims’ bank accounts. In the next step, the banking malware profits are laundered. In this paper we describe two models that are used in particular (next to more traditional money laundering methods). The first model involves the use of money mules and a quick cash-out. The second model focuses on direct spending via (a) direct purchases of products via online shopping, (b) direct purchases of bitcoins via Bitcoin exchanges or (c) direct purchases of luxury goods. Bitcoins can be further laundered via so-called mixing services. All in all, these methods allow criminals to launder profits in relative anonymity and prevent seizure of the illegal profits.
Lars Brünjes, Aggelos Kiayias, Ηλίας Κουτσουπιάς, Aikaterini-Panagiota Stouka
We introduce and study reward sharing schemes (RSS) that promote the fair formation of {\em stake pools}\ in collaborative projects that involve a large number of stakeholders such as the maintenance of a proof-of-stake (PoS) blockchain. Our mechanisms are parameterized by a target value for the desired number of pools. We show that by properly incentivizing participants, the desired number of stake pools is a Nash equilibrium arising from rational play. Our equilibria also exhibit an efficiency / security tradeoff via a parameter that calibrates between including pools with the smallest cost and providing protection against Sybil attacks, the setting where a single stakeholder creates a large number of pools in the hopes to dominate the collaborative project. We then describe how RSS can be deployed in the PoS setting, mitigating a number of potential deployment attacks and protocol deviations that include censoring transactions, performing Sybil attacks with the objective to control the majority of stake, lying about the actual cost and others. Finally, we experimentally demonstrate fast convergence to equilibria in dynamic environments where players react to each other's strategic moves over an indefinite period of interactive play. We also show how simple reward sharing schemes that are seemingly more "fair", perhaps counterintuitively, converge to centralized equilibria.