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1,600 results · page 43 of 67
Wenli Yang, Saurabh Garg, Zhiqiang Huang, Byeong Ho Kang
No abstract is available for this record.
Guénolé Le Pennec, Ingo Fiedler, Lennart Ante
No abstract is available for this record.
Mojtaba Eshghie, Cyrille Artho, Dilian Gurov
In this work we propose Dynamit, a monitoring framework to detect reentrancy vulnerabilities in Ethereum smart contracts. The novelty of our framework is that it relies only on transaction metadata and balance data from the blockchain system; our approach requires no domain knowledge, code instrumentation, or special execution environment. Dynamit extracts features from transaction data and uses a machine learning model to classify transactions as benign or harmful. Therefore, not only can we find the contracts that are vulnerable to reentrancy attacks, but we also get an execution trace that reproduces the attack. Using a random forest classifier, our model achieved more than 90 percent accuracy on 105 transactions, showing the potential of our technique.
Andrea Lisi, Andrea De Salve, Paolo Mori, Laura Ricci · 5 authors
Recommender Systems (RSs) are becoming increasingly popular in the last years. They collect reviews concerning several types of items (e.g., shops, professionals, services, songs or videos) in order to rank them according to a given criterion, and to suggest the most relevant ones to their users. However, most of the currently used RSs exhibit two main drawbacks: they are based on a centralized control model and they do not provide reward mechanisms to encourage the participation of users. To deal with these challenges, the architectures of current RSs could be enhanced through blockchain technology, thus providing novel solutions to decentralize them. As a matter of fact, the blockchain technology could be successfully adopted in this context because smart contracts would allow the decentralization of system control, while cryptocurrency and tokens could be used to implement the reward mechanism. In the light of the above considerations, this manuscript presents a decentralized rating framework aimed to support the users of RSs based on blockchain technology, providing a token-based reward mechanism that remunerates users submitting their reviews to incentivize their participation. Moreover, the proposed system provides a flexible strategy to rank items, allowing users to choose among different functions to combine reviews to obtain item ranking. The performance and the cost of using the proposed system have been evaluated on the Ropsten Ethereum test network. For instance, our experiments have shown that the median time required to store a batch of 35 ratings is about 47 s, while the average time required to obtain the score of an item having 6000 ratings is less than 2.5 s.
Friedhelm Victor, Andrea Marie Weintraud
Dataset retrieved with an Ethereum client, and used by the code hosted here for this paper published in the Proceedings of the Web Conference 2021 (WWW ’21) Abstract: Cryptoassets such as cryptocurrencies and tokens are increasingly traded on decentralized exchanges. The advantage for users is that the funds are not in custody of a centralized external entity. However, these exchanges are prone to manipulative behavior. In this paper, we illustrate how wash trading activity can be identified on two of the first popular limit order book-based decentralized exchanges on the Ethereum blockchain, IDEX and EtherDelta. We identify a lower bound of accounts and trading structures that meet the legal definitions of wash trading, discovering that they are responsible for a wash trading volume in equivalent of 159 million U.S. Dollars. While self-trades and two-account structures are predominant, complex forms also occur. We quantify these activities, finding that on both exchanges, more than 30% of all traded tokens have been subject to wash trading activity. On EtherDelta, 10% of the tokens have almost exclusively been wash traded. All data is made available for future research. Our findings underpin the need for countermeasures that are applicable in decentralized systems.
Hao Wang, Chunpeng Ge, Zhe Liu
Blockchain technology enables global mutually trustless participants to reach a consensus on the final state of permissionless distributed and decentralized ledgers. Due to its properties of openness, transparency, irreversibility, and credibility, many systems have been built based on blockchains' structure, such as Bitcoin and Ethereum. With the wide application of blockchains, however, many security problems still exist in blockchains and there have been many malicious attacks against blockchain systems. Although these attacks have been proposed, there lacks a systematic exploration of how these attacks have been conducted and what underlying relationship they have. In this paper, we firstly present and summarize several current attacks on blockchains in three aspects: system deficiency attacks, mining attacks, and network-level attacks. Secondly, we conduct a systematic analysis and evaluation of the possibility of these attacks occurring on major blockchain platforms. Finally, we motivate some research perspectives and challenges for blockchain system security and highlight some potential solutions to these problems.
Berat ÇAĞLAR, Uğur Yavuz
Bilgisayar ve internet teknolojilerindeki gelişmeler, hayatımızın her alanında etkisini gösterdiği gibi ekonomi boyutunda da etkileri yadsınamaz. Ekonomi alanındaki yeni ekonomik reform mahiyetinde kabul gören bu yenilikler FinTek (Finans ve Teknoloji) kapsamı içerisinde yer almaktadır. FinTech inovasyonu ile ekonomide ademi merkeziyetçi bir akım başlatan Bitcoin, şu an dünya ekonomisinde önemli bir yere sahiptir. Bitcoin ile hayatımıza giren Blockchain (Blokzincir) teknolojisinin ise yakın gelecekte hayatımızın vazgeçilmez bir parçası haline geleceği öngörülmektedir. Tüm bu gelişmeler ışığında günümüzün popüler teknolojilerinden olan yapay zeka yöntemlerinden yararlanılarak, kitle haberleşme aracı olarak tanımlanan gazetelerde yer alan, insan ve toplumu ilgilendiren ve toplumun en önemli ihtiyaçlarından olan haberlerin, geleceğin para birimi olarak görülen Bitcoin üzerindeki etkileri ortaya konmak istenmiştir. Bu bağlamda 5 ulusal finans gazetesi belirlenip, Bitcoin’in ilk halka arz yılından itibaren yayınlanan haberleri olumlu ve olumsuz yorum içeriklerine göre sayısallaştırarak, Bitcoin altyapı teknolojisi olan blokzinciri verileri ile ikinci en popüler kripto para olan Ethereum’un ABD dolar karşılığı alınarak, yapay sinir ağları teknikleri ile oluşturulan ağ içerisinde ilişkilendirilmiştir. Çalışma sonucunda %99’luk tahminsel başarı içeren yapay sinir ağında, finansal gazetelerin yayınlamış olduğu Bitcoin içerikli haberlerin, Bitcoin fiyat tahminine güçlü bir etkisinin olmadığı sonucuna varılmıştır. Bunulan birlikte seçilen finansal gazetelerden The Wall Street gazetesinin diğer finansal gazetelere oranla Bitcoin fiyat tahminde nispeten etkisinin olduğu saptanmıştır.
Rachit Agarwal, Tanmay Thapliyal, Sandeep K. Shukla
Different types of malicious activities have been flagged in multiple\npermissionless blockchains such as bitcoin, Ethereum etc. While some malicious\nactivities exploit vulnerabilities in the infrastructure of the blockchain,\nsome target its users through social engineering techniques. To address these\nproblems, we aim at automatically flagging blockchain accounts that originate\nsuch malicious exploitation of accounts of other participants. To that end, we\nidentify a robust supervised machine learning (ML) algorithm that is resistant\nto any bias induced by an over representation of certain malicious activity in\nthe available dataset, as well as is robust against adversarial attacks. We\nfind that most of the malicious activities reported thus far, for example, in\nEthereum blockchain ecosystem, behaves statistically similar. Further, the\npreviously used ML algorithms for identifying malicious accounts show bias\ntowards a particular malicious activity which is over-represented. In the\nsequel, we identify that Neural Networks (NN) holds up the best in the face of\nsuch bias inducing dataset at the same time being robust against certain\nadversarial attacks.\n
Wei Liang, Lijun Xiao, Ke Zhang, Mingdong Tang · 6 authors
Blockchain technology is rapidly changing the transaction behavior and efficiency of businesses in recent years. Data privacy and system reliability are critical issues that is highly required to be addressed in Blockchain environments. However, anomaly intrusion poses a significant threat to a Blockchain, and therefore, it is proposed in this article a collaborative clustering-characteristic-based data fusion approach for intrusion detection in a Blockchain-based system, where a mathematical model of data fusion is designed and an AI model is used to train and analyze data clusters in Blockchain networks. The abnormal characteristics in a Blockchain data set are identified, a weighted combination is carried out, and the weighted coefficients among several nodes are obtained after multiple rounds of mutual competition among clustering nodes. When the weighted coefficient and a similarity matching relationship follow a standard pattern, an abnormal intrusion behavior is accurately and collaboratively detected. Experimental results show that the proposed algorithm has high recognition accuracy and promising performance in the real-time detection of attacks in a Blockchain.
Pei Xu, Joonghee Lee, James R. Barth, R. Glenn Richey
Purpose This paper discusses how the features of blockchain technology impact supply chain transparency through the lens of the information security triad (confidentiality, integrity and availability). Ultimately, propositions are developed to encourage future research in supply chain applications of blockchain technology. Design/methodology/approach Propositions are developed based on a synthesis of the information security and supply chain transparency literature. Findings from text mining of Twitter data and a discussion of three major blockchain use cases support the development of the propositions. Findings The authors note that confidentiality limits supply chain transparency, which causes tension between transparency and security. Integrity and availability promote supply chain transparency. Blockchain features can preserve security and increase transparency at the same time, despite the tension between confidentiality and transparency. Research limitations/implications The research was conducted at a time when most blockchain applications were still in pilot stages. The propositions developed should therefore be revisited as blockchain applications become more widely adopted and mature. Originality/value This study is among the first to examine the way blockchain technology eases the tension between supply chain transparency and security. Unlike other studies that have suggested only positive impacts of blockchain technology on transparency, this study demonstrates that blockchain features can influence transparency both positively and negatively.
Christof Ferreira Torres, Antonio Ken Iannillo, Arthur Gervais, Radu State
In recent years, Ethereum gained tremendously in popularity, growing from a\ndaily transaction average of 10K in January 2016 to an average of 500K in\nJanuary 2020. Similarly, smart contracts began to carry more value, making them\nappealing targets for attackers. As a result, they started to become victims of\nattacks, costing millions of dollars. In response to these attacks, both\nacademia and industry proposed a plethora of tools to scan smart contracts for\nvulnerabilities before deploying them on the blockchain. However, most of these\ntools solely focus on detecting vulnerabilities and not attacks, let alone\nquantifying or tracing the number of stolen assets. In this paper, we present\nHorus, a framework that empowers the automated detection and investigation of\nsmart contract attacks based on logic-driven and graph-driven analysis of\ntransactions. Horus provides quick means to quantify and trace the flow of\nstolen assets across the Ethereum blockchain. We perform a large-scale analysis\nof all the smart contracts deployed on Ethereum until May 2020. We identified\n1,888 attacked smart contracts and 8,095 adversarial transactions in the wild.\nOur investigation shows that the number of attacks did not necessarily decrease\nover the past few years, but for some vulnerabilities remained constant.\nFinally, we also demonstrate the practicality of our framework via an in-depth\nanalysis on the recent Uniswap and Lendf.me attacks.\n
Tee Wee Jing, Raja Kumar Murugesan
No abstract is available for this record.
Keshav Sinha, Madhav Verma
In today's world, the storage of data needs a huge amount of space. Meanwhile, cloud and distributed environments provide sufficient storage space for the data. One of the challenging tasks is the privacy prevention of storage data. To overcome the problem of privacy, the blockchain-based database is used to store the data. There are various attacks like denial of service attacks (DoS) and insider attacks that are performed by the adversary to compromise the security of the system. In this chapter, the authors discussed a blockchain-based database, where data are encrypted and stored. The Web API is used as an interface for the storage and sharing of data. Here, they are mainly focused on the SQL injection attack, which is performed by the adversary on Web API. To cope with this problem, they present the case study based on the Snort and Moloch for automated detection of SQL attack, network analysis, and testing of the system.
Ning Hu, Teng Yu, Yan Zhao, Yan Zhao · 7 authors
The rapid development of blockchain technology has provided new ideas for network security research. Blockchain-based network security enhancement solutions are attracting widespread attention. This paper proposes an Internet... | Find, read and cite all the research you need on Tech Science Press
Runchao Han, Jiangshan Yu, Haoyu Lin, Shiping Chen · 5 authors
No abstract is available for this record.
Vincenzo De Angelis, Francesco Buccafurri
The introduction of a review system in e-commerce platforms results in an effective business model. The effects of positive/negative reviews on the success of a product are well studied in the literature. Therefore, for the vendors, it is crucial to obtain positive reviews of their products. This fact opens fraudulent scenarios. Indeed, some vendors can pay an incentive to the buyers to obtain, in exchange, a fake positive review. This fake-review system (FRS) is so widespread that vendors rely on ad-hoc companies that, through intermediaries, find buyers available to release fake reviews. In this paper, we propose a game-theory-based strategy to discourage the above fraudulent business model, and an effective way to implement this strategy leveraging an Ethereum smart contract. Specifically, the contribution of the paper is two-fold. First, we formalize the current business model as a sequential game, and we identify sufficient conditions making FRS advantageous. Second, we propose to introduce in the review system a new mechanism, thanks to which, the corresponding sequential game requires conditions necessary to make advantageous FRS that are strictly less convenient than the previous ones. We implemented the solution and evaluate the cost of the smart contract execution.
Weili Chen, Jiahui Cui, Xiongfeng Guo, Zhiguang Chen · 5 authors
No abstract is available for this record.
Jinhee Lee, Minjae Kim, Junbeom Hur
No abstract is available for this record.
Shunchao Luo, Yingpeng Sang, Mingyang Song, Zeng Yuying
No abstract is available for this record.
Shafiya Afzal Sheikh, M. Tariq
Sending bulk e-mail is commercially cheap and technically easy, making it profitable for spammers, even if a tiny percentage of recipients falls for the attacks or turn into customers. Some researchers have proposed making e-mail paid so that sending bulk e-mail becomes expensive, making spamming unprofitable and a futile exercise unless many victims respond to spam. On the other hand, the small sending fee is negligible for legitimate e-mail users. Making e-mail paid is a challenging task if implemented using a conventional payment system or developing new cryptocurrencies. Traditional payment systems are challenging to integrate with e-mail systems, and new cryptocurrencies will have challenges in adoption by users on the required scale. This work proposes using cryptocurrency payments to make e-mail senders pay for sending an e-mail without creating a new cryptocurrency or a new blockchain. In the proposed system, the recipients of the e-mail can collect the payments and use the collected revenues to send e-mail messages or even sell them on an exchange. The proposed solution has been implemented using Ropsten, an Ethereum Test Network and tested using enhanced E-mail Client and Server software.
Authors unavailable
Blockchain is really trendy these days. A distributed ledger on a peer-to-peer network that is completely open to everyone was the block chain. It is composed of blocks that include hash values and data. Before a new transaction can be added to the block chain, researchers must validate it; this process is called mining. Mining is expensive and requires a lot of processing power. Since the block chain is a peer-to-peer network, the data is maintained in every node. The block chain network has increased to 190GB thanks to the increasing number of transactions that are processed through it. It is a problem because a cheap laptop can only hold so much data. This study developed a revolutionary, less expensive system than the block chain method. We choose web applications as our use case since they are increasingly overtaking all other methods of accessing internet services in popularity. The immutability, data security, and data dissemination features of the block chain were all taken into account. The Merle tree concept provides immutability, hashing was used to achieve security, and an open source data distribution tool is used to spread the data. This paper provides innovative methods for preventing malicious data upload using MIME, cross-site programming, and cross-site request manipulation.
Simon Butler
No abstract is available for this record.
Weili Han, Ding-Jie Chen, Jun Pang, Kai Wang · 7 authors
No abstract is available for this record.
Zeinab Shahbazi, Yung-Cheol Byun
Social media network is one of the important parts of human life based on the recent technologies and developments in terms of computer science area. This environment has become a famous platform for sharing information and news on any topics and daily reports, which is the main era for collecting data and data transmission. There are various advantages of this environment, but in another point of view there are lots of fake news and information that mislead the reader and user for the information needed. Lack of trust-able information and real news of social media information is one of the huge problems of this system. To overcome this problem, we have proposed an integrated system for various aspects of blockchain and natural language processing (NLP) to apply machine learning techniques to detect fake news and better predict fake user accounts and posts. The Reinforcement Learning technique is applied for this process. To improve this platform in terms of security, the decentralized blockchain framework applied, which provides the outline of digital contents authority proof. More specifically, the concept of this system is developing a secure platform to predict and identify fake news in social media networks.