In this paper, I discuss an approach to teaching cryptocurrencies as cryptocultures. Cryptocurrencies are traditionally approached as technical or financial phenomena, but I argue that for students outside those specialist areas, a cultural approach will orient them better. I encourage teachers to first focus on situating students in a place, a shared public commons inhabited by the community of a cryptocurrency. I offer a way to model this place using a hash, bash, cash model of decentralised organisation. I build on this sense of place by then revealing to students the cultural context of a cryptocurrency. The context is comprised of the social imaginaries and the overarching goal animating a cryptocurrency culture. I show how this cryptocultural approach can be used to analyse different cryptocultures through an examination of the competing environmental imaginaries of Bitcoin and Ethereum.
We review different classes of cryptocurrencies with emphasis on their economic properties. Pure-asset coins such as Bitcoin, Ethereum and Ripple are characterized by not being a liability of any economic agent and most resemble commodities such as gold. Central bank digital currencies, at the other end of the economic spectrum, are liabilities of a Central Bank and most resemble cash. In between, there exist a range of so-called stable coins, with varying degrees of economic complexity. We use balance sheet operations to highlight the properties of each class of cryptocurrency and their potential uses. In addition, we propose the basic structure for a macroeconomic model incorporating all the different types of cryptocurrencies under consideration.
Esam Mahdi, Víctor Leiva, Saed Mara’Beh, Carlos Martín-Barreiro
In a real-world situation produced under COVID-19 scenarios, predicting cryptocurrency returns accurately can be challenging. Such a prediction may be helpful to the daily economic and financial market. Unlike forecasting the cryptocurrency returns, we propose a new approach to predict whether the return classification would be in the first, second, third quartile, or any quantile of the gold price the next day. In this paper, we employ the support vector machine (SVM) algorithm for exploring the predictability of financial returns for the six major digital currencies selected from the list of top ten cryptocurrencies based on data collected through sensors. These currencies are Binance Coin, Bitcoin, Cardano, Dogecoin, Ethereum, and Ripple. Our study considers the pre-COVID-19 and ongoing COVID-19 periods. An algorithm that allows updated data analysis, based on the use of a sensor in the database, is also proposed. The results show strong evidence that the SVM is a robust technique for devising profitable trading strategies and can provide accurate results before and during the current pandemic. Our findings may be helpful for different stakeholders in understanding the cryptocurrency dynamics and in making better investment decisions, especially under adverse conditions and during times of uncertain environments such as in the COVID-19 pandemic.
Ethereum smart contracts have been gaining popularity toward the automation of so many domains, i.e., FinTech, IoT, and supply chain, which are based on blockchain technology. The most critical domain, e.g., FinTech, has been targeted by so many successful attacks due to its financial worth of billions of dollars. In all attacks, the vulnerability in the source code of smart contracts is being exploited and causes the steal of millions of dollars. To find the vulnerability in the source code of smart contracts written in Solidity language, a state-of-the-art work provides a lot of solutions based on dynamic or static analysis. However, these tools have shown a lot of false positives/negatives against the smart contracts having complex logic. Furthermore, the output of these tools is not reported in a standard way with their actual vulnerability names as per standards defined by the Ethereum community. To solve these problems, we have introduced a static analysis tool, SESCon (secure Ethereum smart contract), applying the taint analysis techniques with XPath queries. Our tool outperforms other analyzers and detected up to 90% of the known vulnerability patterns. SESCon also reports the detected vulnerabilities with their titles, descriptions, and remediations as per defined standards by the Ethereum community. SESCon will serve as a foundation for the standardization of vulnerability detection.
Anderson Melo de Morais, Jorge da Silva Correia Neto, Robson Wagner Albuquerque de Medeiros, Obionor de Oliveira Nóbrega · 5 authors
Education generates a considerable amount of data and information, it is necessary to manage this data securely, avoiding problems of falsification and tampering with diplomas, certificates, and other important student documents. In addition, distance learning is becoming more popular due to the Covid-19 pandemic period, where courses are offered in virtual learning environments such as Moodle. Blockchain technology can be used as a tool to ensure the security of data managed by Moodle. However, there is a gap in current state-of-the-art solutions for integrating these data with Blockchain platforms. This work proposes BlockMoodle, a solution developed to integrate Moodle with Blockchain Ethereum. This work also performs an analysis of the financial cost of using the tool. In addition, a performance evaluation of the proposed solution is also carried out considering different usage scenarios. Finally, the conclusions and future work to be developed are presented.
Sentiments are extracted from tweets with the hashtag of cryptocurrencies to predict the price and sentiment prediction model generates the parameters for optimization procedure to make decision and re-allocate the portfolio in the further step. Moreover, after the process of prediction, the evaluation, which is conducted with RMSE, MAE and R2, select the KNN and CART model for the prediction of Bitcoin and Ethereum respectively. During the process of portfolio optimization, this project is trying to use predictive prescription to robust the uncertainty and meanwhile take full advantages of auxiliary data such as sentiments. For the outcome of optimization, the portfolio allocation and returns fluctuate acutely as the illustration of figure.
Ethereum is a public blockchain platform with smart contract. However, it has transaction privacy issues due to the openness of the underlying ledger. Decentralized mixing schemes are presented to hide transaction relationship and transferred amount, but suffer from high transaction cost and long transaction latency. To overcome the two challenges, we propose the idea of batch accounting, adopting batch processing at the time of accounting. For further realization, we introduce payment channel technology into decentralized mixer. Since intermediate transactions between two parties do not need network consensus, our scheme can reduce both transaction cost and transaction latency. Moreover, we provide informal definitions and proofs of our scheme's security. Finally, our scheme is implemented based on zk-SNARKs and Ganache, and experimental results show that the higher number of transactions in batch, the better our scheme performs.
Open access
Blockchain Technology Applications and Security
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
In this paper we describe LUNES-Blockchain, an agent-based simulator of blockchains that relies on Parallel and Distributed Simulation (PADS) techniques to obtain high scalability. The software is organized as a multi-level simulator that permits to simulate a virtual environment, made of many nodes running the protocol of a specific Distributed Ledger Technology (DLT), such as the Bitcoin or the Ethereum blockchains. This virtual environment is executed on top of a lower-level Peer-to-Peer (P2P) network overlay, which can be structured based on different topologies and with a given number of nodes and edges. Functionalities at different levels of abstraction are managed separately, by different software modules and with different time granularity. This allows for accurate simulations, where (and when) it is needed, and enhances the simulation performance. Using LUNES-Blockchain, it is possible to simulate different types of attacks on the DLT. In this paper, we specifically focus on the P2P layer, considering the selfish mining, the 51% attack and the Sybil attack. For which concerns selfish mining and the 51% attack, our aim is to understand how much the hash-rate (i.e. a general measure of the processing power in the blockchain network) of the attacker can influence the outcome of the misbehaviour. On the other hand, in the filtering denial of service (i.e. Sybil Attack), we investigate which dissemination protocol in the underlying P2P network makes the system more resilient to a varying number of nodes that drop the messages. The results confirm the viability of the simulation-based techniques for the investigation of security aspects of DLTs.
In this paper we describe LUNES-Blockchain, an agent-based simulator of\nblockchains that relies on Parallel and Distributed Simulation (PADS)\ntechniques to obtain high scalability. The software is organized as a\nmulti-level simulator that permits to simulate a virtual environment, made of\nmany nodes running the protocol of a specific Distributed Ledger Technology\n(DLT), such as the Bitcoin or the Ethereum blockchains. This virtual\nenvironment is executed on top of a lower-level Peer-to-Peer (P2P) network\noverlay, which can be structured based on different topologies and with a given\nnumber of nodes and edges. Functionalities at different levels of abstraction\nare managed separately, by different software modules and with different time\ngranularity. This allows for accurate simulations, where (and when) it is\nneeded, and enhances the simulation performance. Using LUNES-Blockchain, it is\npossible to simulate different types of attacks on the DLT. In this paper, we\nspecifically focus on the P2P layer, considering the selfish mining, the 51%\nattack and the Sybil attack. For which concerns selfish mining and the 51%\nattack, our aim is to understand how much the hash-rate (i.e. a general measure\nof the processing power in the blockchain network) of the attacker can\ninfluence the outcome of the misbehaviour. On the other hand, in the filtering\ndenial of service (i.e. Sybil Attack), we investigate which dissemination\nprotocol in the underlying P2P network makes the system more resilient to a\nvarying number of nodes that drop the messages. The results confirm the\nviability of the simulation-based techniques for the investigation of security\naspects of DLTs.\n
Vishal Sharma, Zengpeng Li, Paweł Szałachowski, Teik Guan Tan · 5 authors
Decentralized control, low-complexity, flexible and efficient communications are the requirements of an architecture that aims to scale blockchains beyond the current state. Such properties are attainable by reducing ledger size and providing parallel operations in the blockchain. Sharding is one of the approaches that lower the burden of the nodes and enhance performance. However, the current solutions lack the features for resolving concurrency during cross-shard communications. With multiple participants belonging to different shards, handling concurrent operations is essential for optimal sharding. This issue becomes prominent due to the lack of architectural support and requires additional consensus for cross-shard communications. Relying on the advantages of hybrid Proof-of-Work/Proof-of-Stake (PoW/PoS), like Ethereum , hybrid consensus and 2-hop blockchain , we propose Reinshard , a new blockchain that inherits the properties of hybrid consensus for optimal sharding. Reinshard uses PoW and PoS chain-pairs with PoS sub-chains for all the valid chain-pairs where the hybrid consensus is attained through Verifiable Delay Function (VDF). Our architecture provides a secure method of arranging nodes in shards and resolves concurrency conflicts using the delay factor of VDF. The applicability of Reinshard is demonstrated through security and experimental evaluations. A practical concurrency problem is considered to show the efficacy of Reinshard in providing optimal sharding.
Bertrand Meyer, Alisa Arkadova, Alexander Kogtenkov
Class invariants -- consistency constraints preserved by every operation on objects of a given type -- are fundamental to building, understanding and verifying object-oriented programs. For verification, however, they raise difficulties, which have not yet received a generally accepted solution. The present work introduces a proof rule meant to address these issues and allow verification tools to benefit from invariants. It clarifies the notion of invariant and identifies the three associated problems: callbacks, furtive access and reference leak. As an example, the 2016 Ethereum DAO bug, in which $50 million were stolen, resulted from a callback invalidating an invariant. The discussion starts with a simplified model of computation and an associated proof rule, demonstrating its soundness. It then removes one by one the three simplifying assumptions, each removal raising one of the three issues, and leading to a corresponding adaptation to the proof rule. The final version of the rule can tackle tricky examples, including "challenge problems" listed in the literature.
Cryptocurrency adalah sebuah teknologi berbasis blockchain yang sering digunakan pada saat ini sebagai mata uang digital. Bentuk cryptocurrency di minati oleh masyarakat karena pengelolaannya yang terdesentraliasi, prosesnya yang mudah, serta memungkinkan transaksi tanpa perlu adanya perantara pihak ketiga. Teknologi internet of things (IoT) telah memberikan pengaruh besar terhadap perkembangan informasi yang didapatkan untuk memantau secara langsung perubahan informasi tanpa memerlukan adanya interaksi dari manusia ke manusia ataupun dari manusia ke perangkat komputer, seperti menampilkan stabilitas cryptocurrency terutama pada Bitcoin, Ethereum, dan Binance. Penelitian ini bertujuan untuk memonitoring data secara real time dengan menggunakan aplikasi Thingspeak.com, Arduino IDE sebagai pemroses data, dan jalur pengiriman data secara real time memanfaatkan, modul ESP8266. Hasil implementasi olahan data ditampilkan pada layar oled.
Philipp Winter, Anna Harbluk Lorimer, Peter Snyder, Benjamin Livshits
Much of the recent excitement around decentralized finance (DeFi) comes from hopes that DeFi can be a secure, private, less centralized alternative to traditional finance systems but the accuracy of these hopes has to date been understudied; people moving to DeFi sites to improve their privacy and security may actually end up with less of both.
In this work, we improve the state of DeFi by conducting the first measurement of the privacy and security properties of popular DeFi applications. We find that DeFi applications suffer from the same kinds of privacy and security risks that frequent other parts of the Web. For example, we find that one common tracker has the ability to record Ethereum addresses on over 56% of websites analyzed. Further, we find that many trackers on DeFi sites can trivially link a user's Ethereum address with PII (e.g., name or demographic information) or phish users.
This work also proposes remedies to the vulnerabilities we identify, in the form of improvements to the most common cryptocurrency wallet. Our wallet modification replaces the user's real Ethereum address with site-specific addresses, making it harder for DeFi sites and third parties to (i) learn the user's real address and (ii) track them across sites.
Philipp Winter, Anna Harbluk Lorimer, Peter J. Snyder, Benjamin Livshits
Much of the recent excitement around decentralized finance (DeFi) comes from hopes that DeFi can be a secure, private, less centralized alternative to traditional finance systems. However, people moving to DeFi sites in hopes of improving their security and privacy may end up with less of both as recent attacks have demonstrated. In this work, we improve the understanding of DeFi by conducting the first Web measurements of the security, privacy, and decentralization properties of popular DeFi front ends. We find that DeFi applications -- or dapps -- suffer from the same security and privacy risks that frequent other parts of the Web but those risks are greatly exacerbated considering the money that is involved in DeFi. Our results show that a common tracker can observe user behavior on over 56% of websites we analyzed and many trackers on DeFi sites can trivially link a user's Ethereum address with PII (e.g., user name or demographic information), or phish users by initiating fake Ethereum transactions. Lastly, we establish that despite claims to the opposite, because of companies like Amazon and Cloudflare operating significant Web infrastructure, DeFi as a whole is considerably less decentralized than previously believed.
Jon Oakley, Carl Worley, Lu Yu, Richard Brooks · 7 authors
Clinical trials are a multi-billion dollar industry. One of the biggest challenges facing the clinical trial research community is satisfying Part 11 of Title 21 of the Code of Federal Regulations and ISO 27789. These controls provide audit requirements that guarantee the reliability of the data contained in the electronic records. Context-aware smart devices and wearable IoT devices have become increasingly common in clinical trials. Electronic Data Capture (EDC) and Clinical Data Management Systems (CDMS) do not currently address the new challenges introduced using these devices. The healthcare digital threat landscape is continually evolving, and the prevalence of sensor fusion and wearable devices compounds the growing attack surface. We propose Scrybe, a permissioned blockchain, to store proof of clinical trial data provenance. We illustrate how Scrybe addresses each control and the limitations of the Ethereum-based blockchains. Finally, we provide a proof-of-concept integration with REDCap to show tamper resistance.
Walid Mensi, Mobeen Ur Rehman, Muhammad Shafiullah, Khamis Hamed Al‐Yahyaee · 5 authors
This paper examines the high frequency multiscale relationships and nonlinear multiscale causality between Bitcoin, Ethereum, Monero, Dash, Ripple, and Litecoin. We apply nonlinear Granger causality and rolling window wavelet correlation (RWCC) to 15 min-data. Empirical RWCC results indicate mostly positive co-movements and long-term memory between the cryptocurrencies, especially between Bitcoin, Ethereum, and Monero. The nonlinear Granger causality tests reveal dual causation between most of the cryptocurrency pairs. We advance evidence to improve portfolio risk assessment, and hedging strategies.
Mirza Jabbar Aziz Baig, M. Tariq Iqbal, Mohsin Jamil, Jahangir Khan
An open-source P2P energy trading platform facilitates energy trading amongst the peers. The proposed system provides real time data acquisition, monitoring and control of self-generated energy at a remote location. The trading activities are done on a web interface that uses a private Ethereum blockchain. A smart contract is deployed on the Ethereum blockchain and the trading activities performed on the web interface are recorded on a tamper-proof blockchain network. An internet of things platform is used to monitor and control the self-generated energy. Energy data is collected and processed by means of ESP32-S2 microcontrollers using field instrumentation devices which are connected to the voltage source and load. An open-source decentralized Peer-to-Peer (P2P) energy trading system, designed on the blockchain and internet of things (IoT) architecture is proposed. The hardware setup includes a relay, a current sensor, a voltage sensor, a Wi-Fi router and ESP32-S2 microcontroller. For data transfer the Message Queuing Telemetry Transport (MQTT) protocol is used over a local network. ESP32-S2 is set up as MQTT client and Node-Red IoT server is used as MQTT broker. Hypertext Transfer Protocol (http) request method is implemented to connect the Node-Red server with the web interface developed using React.JS library. The system design, implementation, testing, and results are presented in this paper.
Sep 9, 2021·(2021, August). Application of Blockchain Technology for Educational Platform. In International Conference on Human Interaction and Emerging Technologies (pp. 1283-1287). Springer, Cham
Matija Šipek, Martin Žagar, Branko Mihaljević, Nikola Drašković
Nowadays, huge amounts of data are generated every second, and a quantity of that data can be defined as sensitive. Blockchain technology has private, secure, transparent and decentralized exchange of data as native. It is adaptable and can be used in a wide range of Internet-based interactive systems in academic and industrial settings. The essential part of programmable distributed ledgers such as Ethereum, Polkadot, Cardano and other Web 3.0 technologies are smart contracts. Smart contracts are programs executed on the global blockchain, the code is public as well as all of the data managed within the transactions, thus creating a system that is reliable and cannot be cheated if designed properly. In this paper, in order to make the educational system more transparent and versatile we will describe an educational learning platform designed as a distributed system.
Timothy Tzen Vun Yap, Ting Fong Ho, Hu Ng, Vik Tor Goh
<ns3:p> <ns3:bold>Background:</ns3:bold> This research uses exploratory graph analysis to analyze the transaction data of the Ethereum network. This is achieved through network visualization and mathematical and statistical modelling of the network data. </ns3:p> <ns3:p> <ns3:bold>Methods:</ns3:bold> The dataset used in this study was extracted from the Ethereum in the BigQuery public dataset, specifically selected transactions in July 2019. The transactions were firstly modelled as network graphs and then visualized using the Kamada-Kawai and force-directed graphs layouts. Further modelling was explored with classical random graph and network block, with emphasis on network cohesion, hierarchical clustering and community membership. </ns3:p> <ns3:p> <ns3:bold>Results:</ns3:bold> Looking at the network visualization and hierarchical clustering of the data, the network shows 170 clusters, the largest having 135 members. Through random graph modelling the optimum number of clusters is shown to be 95. Referring to the generated dendrograms, notable large transactions center around the DRINK token, the Maximine Exchange, the Upbit2 Exchange and the IDEX Exchange, identified through public disclosure of their Ethereum addresses. The network graphs tend to go towards the DRINK smart contract and the Maximine Exchange, indicating deposit actions, while it is the opposite for the IDEX Exchange. Further analysis also shows a different number of communities than the expected number. Falling short of the expected 170 clusters, the model is not able to capture additional mechanism that may be present at the density and social interaction distribution level of the network. On the other hand, network block modelling shows only four major clusters out of the 170 expected clusters, an indication that the model is not able to capture the network sufficiently. </ns3:p> <ns3:p> <ns3:bold>Conclusions:</ns3:bold> The study was able to capture and model the interconnectedness of the system with its notion of elements, in this case, the transactions on the network. </ns3:p>
Kushal Babel, Philip Daian, Mahimna Kelkar, Ari Juels
We introduce the Clockwork Finance Framework (CFF), a general purpose, formal verification framework for mechanized reasoning about the economic security properties of composed decentralized-finance (DeFi) smart contracts. CFF features three key properties. It is contract complete, meaning that it can model any smart contract platform and all its contracts--Turing complete or otherwise. It does so with asymptotically constant model overhead. It is also attack-exhaustive by construction, meaning that it can automatically and mechanically extract all possible economic attacks on users' cryptocurrency across modeled contracts. Thanks to these properties, CFF can support multiple goals: economic security analysis of contracts by developers, analysis of DeFi trading risks by users, fees UX, and optimization of arbitrage opportunities by bots or miners. Because CFF offers composability, it can support these goals with reasoning over any desired set of potentially interacting smart contract models. We instantiate CFF as an executable model for Ethereum contracts that incorporates a state-of-the-art deductive verifier. Building on previous work, we introduce extractable value (EV), a new formal notion of economic security in composed DeFi contracts that is both a basis for CFF and of general interest. We construct modular, human-readable, composable CFF models of four popular, deployed DeFi protocols in Ethereum: Uniswap, Uniswap V2, Sushiswap, and MakerDAO, representing a combined 24 billion USD in value as of March 2022. We use these models along with some other common models such as flash loans, airdrops and voting to show experimentally that CFF is practical and can drive useful, data-based EV-based insights from real world transaction activity. Without any explicitly programmed attack strategies, CFF uncovers on average an expected $56 million of EV per month in the recent past.
Selma Steinhoff, Chrysoula Stathakopoulou, Matej Pavlovič, Marko Vukolić
Reconfiguration of long-lived blockchain and Byzantine fault-tolerant (BFT) systems poses fundamental security challenges. In case of state-of-the-art Proof-of-Stake (PoS) blockchains, stake reconfiguration enables so-called long-range attacks, which can lead to forks. Similarly, permissioned blockchain systems, typically based on BFT, reconfigure internally, which makes them susceptible to a similar "I still work here" attack. In this work, we propose BMS (Blockchain/BFT Membership Service) offering a secure and dynamic reconfiguration service for BFT and blockchain systems, preventing long-range and similar attacks. In particular: (1) we propose a root BMS for permissioned blockchains, implemented as an Ethereum smart contract and evaluate it reconfiguring the recently proposed Mir-BFT protocol, (2) we discuss how our BMS extends to PoS blockchains and how it can reduce PoS stake unbonding time from weeks/months to the order of minutes, and (3) we discuss possible extensions of BMS to hierarchical deployments as well as to multiple root BMSs.
Blockchains are gaining momentum due to the interest of industries and people in \emph{decentralized applications} (Dapps), particularly in those for trading assets through digital certificates secured on blockchain, called tokens. As a consequence, providing a clear unambiguous description of any activities carried out on blockchains has become crucial, and we feel the urgency to achieve that description at least for trading. This paper reports on how to leverage the \emph{Ontology for Agents, Systems, and Integration of Services} ("\ONT{}") as a general means for the semantic representation of smart contracts stored on blockchain as software agents. Special attention is paid to non-fungible tokens (NFTs), whose management through the ERC721 standard is presented as a case study.
Blockchains are gaining momentum due to the interest of industries and people in \emph{decentralized applications} (Dapps), particularly in those for trading assets through digital certificates secured on blockchain, called tokens. As a consequence, providing a clear unambiguous description of any activities carried out on blockchains has become crucial, and we feel the urgency to achieve that description at least for trading. This paper reports on how to leverage the \emph{Ontology for Agents, Systems, and Integration of Services} ("\ONT{}") as a general means for the semantic representation of smart contracts stored on blockchain as software agents. Special attention is paid to non-fungible tokens (NFTs), whose management through the ERC721 standard is presented as a case study.