Johannes Beck, Roberta Huang, David Lindner, Tian Guo · 7 authors
The ability to track and monitor relevant and important news in real-time is of crucial interest in multiple industrial sectors. In this work, we focus on the set of cryptocurrency news, which recently became of emerging interest to the general and financial audience. In order to track relevant news in real-time, we (i) match news from the web with tweets from social media, (ii) track their intraday tweet activity and (iii) explore different machine learning models for predicting the number of the article mentions on Twitter within the first 24 hours after its publication. We compare several machine learning models, such as linear extrapolation, linear and random forest autoregressive models, and a sequence-to-sequence neural network. We find that the random forest autoregressive model behaves comparably to more complex models in the majority of tasks.
Among the many cryptocurrencies, the most popular is bitcoin. It is a direct electronic system that deals with money and lets us make payments over network. To verify bitcoin transactions involving payments between users, we need to mine the data involved in these transactions. Data mining keeps a record of the various payments made and the services used in an organized form. Since mining is the bedrock of bitcoin framework, it is very important to make sure that the mined data is unalterable and complete by repeatedly verifying and adding new transactions. For this purpose, we use various algorithms and protocols, along with satisfying proof-of-stake and proof-of-work. These algorithms are studied and compared, and it is proposed that we could switch to proof-of-stake for bitcoin to make it more energy efficient as well as cost effective.
Ioannis Karagiannis, Konstantinos Mavrogiannis, John Soldatos, Dimitris Drakoulis · 6 authors
Recent security incidents in the finance sector have demonstrated the importance of sharing security information across financial institutions, as a means of mitigating risks and boosting the early preparedness against relevant attacks. However, financial institutions are in several cases reluctant to share security information beyond what is imposed by applicable regulations. In this paper, we introduce a blockchain-based solution for sharing security information in a decentralized way, which boosts security and trust in the information sharing process. We also illustrate how the information that is shared across financial institutions can serve as a basis for collaborative security services such as risk assessment.
2 source records
Blockchain Technology Applications and Security
Infrastructure Resilience and Vulnerability Analysis
This chapter discusses mining basics, hash rate, required hardware, pooled mining, reward sharing, countries famous for mining and criticisms against the proof-of-work mining process. Cryptocurrency mining is the method of verifying transactions and adding the records to the distributed ledger for various forms of cryptocurrency. Each time a user makes a transaction a cryptocurrency miner is responsible for ensuring the authenticity of information and updating the blockchain with the transaction. The ability to generate it fast has a knock-on effect on the cryptocurrency mining and is considered the single most crucial element in the mining process. Cryptocurrency mining is competitive and requires powerful hardware to compete with other miners in the network. Profitability factors give the potential miners tentative information on how much they could earn by mining a particular cryptocurrency. Pooled mining has been a popular concept in cryptocurrency mining that helps to tackle the growing difficulties of the mining process.
The Dark Web is notorious for being a major distribution channel of harmful content as well as unlawful goods.Perpetrators have also used cryptocurrencies to conduct illicit financial transactions while hiding their identities.The limited coverage and outdated data of the Dark Web in previous studies motivated us to conduct an in-depth investigative study to understand how perpetrators abuse cryptocurrencies in the Dark Web.We designed and implemented MFScope, a new framework which collects Dark Web data, extracts cryptocurrency information, and analyzes their usage characteristics on the Dark Web.Specifically, MFScope collected more than 27 million dark webpages and extracted around 10 million unique cryptocurrency addresses for Bitcoin, Ethereum, and Monero.It then classified their usages to identify trades of illicit goods and traced cryptocurrency money flows, to reveal black money operations on the Dark Web.In total, using MFScope we discovered that more than 80% of Bitcoin addresses on the Dark Web were used with malicious intent; their monetary volume was around 180 million USD, and they sent a large sum of their money to several popular cryptocurrency services (e.g., exchange services).Furthermore, we present two real-world unlawful services and demonstrate their Bitcoin transaction traces, which helps in understanding their marketing strategy as well as black money operations.
Kentaroh Toyoda, P. Takis Mathiopoulos, Tomoaki Ohtsuki
Bitcoin is one of the most popular decentralized cryptocurrencies to date. However, it has been widely reported that it can be used for investment scams, which are referred to as high yield investment programs (HYIP). Although from the security forensic point of view it is very important to identify the HYIP operators' Bitcoin addresses, so far in the open technical literature no systematic method which reliably collects and identifies such Bitcoin addresses has been proposed. In this paper, a novel methodology is introduced, which efficiently collects a large number of the HYIP operators' Bitcoin addresses and identifies them based upon a novel analysis of their transactions history. In particular, a scraping-based method is first proposed which is able to collect more than 2,000 HYIP operators' Bitcoin addresses from the Internet thus providing a large number of the HYIPs' samples. Second, a supervised machine learning technique, which classifies, whether or not, specific Bitcoin addresses belong to the HYIP operators, is introduced and its performance is evaluated. The proposed classification method is based upon two novel approaches, namely the rate conversion technique that mitigates the effect of Bitcoin price volatility and the sampling technique that reduces the computational amount without sacrificing the classification performance. By employing close to 30,000 real Bitcoin addresses, extensive performance evaluation results obtained by means of computer simulation experiments have shown that the proposed methodology achieves excellent performance, i.e., 95% of the HYIP addresses can be correctly classified, while maintaining a false positive rate less than 4.9%. In order to further validate the proposed classifier's ability to detect the HYIP operators' Bitcoin addresses, our designed classifier has been tested against a recently published list of the HYIP addresses maintaining its excellent detection accuracy by achieving a 93.75% success rate.
Open access
Blockchain Technology Applications and Security
Spam and Phishing Detection
Advanced Steganography and Watermarking Techniques
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 reward of the Bitcoin system is designed to be proportional to miner's computational power. However, rogue miners can increase their rewards by using the block withholding attacks. For raising awareness on the Bitcoin reward system, a new attack scheme is proposed, where the attackers infiltrate into an open pool and launch the selfish mining as well as the block withholding attack. The simulation results demonstrate that the proposed attack outperforms the conventional block withholding attacks.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
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.
Open access
2 source records
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
Blockchain has been evolving and gaining new heights over the years. The shift in the perspective is allowing new user cases beyond the cryptocurrency space. Cryptocurrencies are digital assets supported by the complexities of cryptography, game theory and peer-to-peer networks. Blockchain became a popular platform for decentralized applications, as well as a valuable tool for start-ups seeking fundraising. The aim of this research paper is to review and assess the status quo for each branch of use cases, and then analyze the enabling and inhibiting factors influencing the adoption of blockchain. These findings permit a broader comprehension over the concepts backing blockchain. It will help new users to establish strategies, develop solutions and encourage the employment of blockchain technology.
Blockchain is a distributed ledger system composed of a P2P network and is used for a wide range of applications, such as international remittance, inter-individual transactions, and asset conservation. In Blockchain systems, tamper resistance is enhanced by the property of transaction that cannot be changed or deleted by everyone including the creator of the transaction. However, this property also becomes a problem that unintended transaction created by miss operation or secret key theft cannot be corrected later. Due to this problem, once an illegal transaction such as theft occurs, the damage will expand. To suppress the damage, we need countermeasures, such as detecting illegal transaction at high speed and correcting the transaction before approval. However, abnormality detection in the Blockchain at high speed is computationally heavy, because we need to repeat the detection process using various feature quantities and the feature extractions become overhead. In this paper, to accelerate abnormality detection, we propose to cache transaction information necessary for extracting feature in GPU device memory and perform both feature extraction and abnormality detection in the GPU. We employ abnormality detection using K-means algorithm based on the conditional features. When the number of users is one million and the number of transactions is 100 millions, our proposed method achieves 37.1 times faster than CPU processing method and 16.1 times faster than GPU processing method that does not perform feature extraction on the GPU.
Bitcoin is a digital currency that aims to offer anonymity. However, anonymity is less secure than it is believed to be because it is based on using pseudonyms for addresses. To demonstrate this weakness, we used statistics related to transaction histories and the frequency of transactions to deanonymize a set of bitcoin addresses. In this paper, we explore the fundamental properties of Bitcoin addresses based on actual Bitcoin transaction data. We propose a new method for deanonymizing Bitcoin addresses from a set of output addresses and we demonstrate that 80.5% of addresses could be identified.
Blockchain ushers in a new era for the global financial system with the advent of digital currency (cryptocurrency), and its impact can be felt in many related industries. Because of its possible applications, cryptocurrency draws significant attention from researchers. Although there are a number of risks (e.g., speculation, 51% attack) related to cryptocurrency, billions of dollars are invested in them, because of transparency, traceability, low transaction cost, and highly profitable potential. In December 2017, the most famous cryptocurrency, Bitcoin, has reached almost $20,000.00 per coin. Such short-term, high gain potential attracts many new small investors. However, speculative movements raise many questions related to safety and privacy, just to name a few. In order to understand public opinion about cryptocurrency and to protect small investors financial interests, sentiment analysis can be done by using social media activities of individuals who are interested or investing in cryptocurrencies. One of the most important steps in the analysis is to understand the profiles of the users. Therefore, in this paper, we determine education levels of investors or users who are interested in eight cryptocurrencies by using seven readability techniques on Reddit comments as a part of profiling. Results show that the education levels of users are approximately 60% in middle school, 30% in high school, and 10% in other levels according to the average of the seven readability technique results. The results and analysis, which are provided in this paper, help new investors and developers to obtain profile information about the users who are interested or investing in cryptocurrency.
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.