Siddhartha R. Dalal, Zihe Wang, Siddhanth Sabharwal
Due to the pseudo-anonymity of the Bitcoin network, users can hide behind their bitcoin addresses that can be generated in unlimited quantity, on the fly, without any formal links between them. Thus, it is being used for payment transfer by the actors involved in ransomware and other illegal activities. The other activity we consider is related to gambling since gambling is often used for transferring illegal funds. The question addressed here is that given temporally limited graphs of Bitcoin transactions, to what extent can one identify common patterns associated with these fraudulent activities and apply them to find other ransomware actors. The problem is rather complex, given that thousands of addresses can belong to the same actor without any obvious links between them and any common pattern of behavior. The main contribution of this paper is to introduce and apply new algorithms for local clustering and supervised graph machine learning for identifying malicious actors. We show that very local subgraphs of the known such actors are sufficient to differentiate between ransomware, random and gambling actors with 85% prediction accuracy on the test data set.
Social media news are most important in today's worlds, it puts positive or negative influence on social views. There is a wide propagation of fake news on social media so it will be difficult to believe on the news. Fake news has negative impacts on individuals as well as on society. Information spreads rapidly over the social media and so there is a need of mechanism which detects and stops the spreading of fake news. Therefore, detection of fake news is the need of time and also a challenging problem. The goal of this proposed research work is to detect fake news and minimize spreading of the fake news. In the proposed research a machine learning approach is used for detection of fake news with blockchain framework. In first section a supervised machine learning techniques is design to identify the trustiness of specific news while blockchain framework revoke the malicious activity of spreading fake news. A blockchain environment is created with mining, smart contract as well as Proof of Work (PoW) of consensus. The current systematic review broadly focuses on the various methods to detect fake news in social media. After partial implementation of system, performance evaluation has done with traditional blockchain framework. It is found that 10% less time for transaction verification by consensus in P2P environment over the existing systems.
Ke Huang, Yi Mu, Fatemeh Rezaeibagha, Xiaosong Zhang
Hash function is generally known as symmetric and private for use. However, when applying under public key infrastructure, it immediately yields a notion called chameleon hash function. Based on the chameleon hash, this chapter generalizes and formalizes the notion of public key hash (PKH) scheme by rigorous algorithmic design and security models of indistinguishability and collision-resistance. PKH is another form of the trapdoor commitment, which serves as a basic element for zero knowledge and IND-CCA2 secure public key encryption schemes. Notably, PKH (e.g. chameleon hash) has been recently used to build the redactable blockchain where block hash and transaction are supposed to be redacted without impairing consistency. To begin with, this chapter first review background information of PKH. Then, it instantiates by various PKH schemes: ACH, HCCH, PCH, TUCH and RCH which are mostly from our related works. By rich analysis of algorithmic design and proof techniques of security reduction, this chapter gives more empirical knowledge, insightful instruction and practising sources for the readers to follow. To summarize, PHK is considered as a basic primitive for many other cryptographic schemes. It is an easy starting point for a beginner to follow but can also extend to sophisticated design. The simplicity of the instantiated cases helps the reader quickly grasp and practise proof techniques given in this chapter
Cüneyt Gürcan Akçora, Murat Kantarcıoğlu, Yulia R. Gel
Blockchain technology garners an ever-increasing interest of researchers in various domains that benefit from scalable cooperation among trust-less parties. As blockchains and their applications proliferate, so do the complexity and volume of data stored by Blockchains. Analyzing this data has emerged as an important research topic, already leading to methodological advancements in information sciences.
In recent years, a lot of vulnerabilities of smart contracts have been found. Hackers used these vulnerabilities to attack the corresponding contracts developed in the blockchain system such as Ethereum, and it has caused lots of economic losses. Therefore, it is very important to find out the potential problems of the smart contracts and develop more secure smart contracts. As blockchain security events have raised more important issues, more and more smart contract security analysis methods have been developed. Most of these methods are based on traditional static analysis or dynamic analysis methods. There are only a few methods that use emerging technologies, such as machine learning. Some models that use machine learning to detect smart contract vulnerabilities cost much time in extracting features manually. In this paper, we introduce a novel machine learning-based analysis model by introducing the shared child nodes for smart contract vulnerabilities. We build the Abstract-Syntax-Tree (AST) for smart contracts with some vulnerabilities from two data sets including SmartBugs and SolidiFI-benchmark. Then, we build the Abstract-Syntax-Tree (AST) of the labeled smart contract for data sets named Smartbugs-wilds. Next, we get the shared child nodes from both of the ASTs to obtain the structural similarity, and then, we construct a feature vector composed of the values that measure structural similarity automatically to build our machine learning model. Finally, we get a KNN model that can predict eight types of vulnerabilities including Re-entrancy, Arithmetic, Access Control, Denial of Service, Unchecked Low Level Calls, Bad Randomness, Front Running, and Denial of Service. The accuracy, recall, and precision of our KNN model are all higher than 90%. In addition, compared with some other analysis tools including Oyente and SmartCheck, our model has higher accuracy. In addition, we spent less time for training .
Public blockchain records are widely studied in various aspects such as cryptocurrency abuse, anti-money-laundering, and monetary flow of businesses. However, the final blockchain records, usually available from block explorer services or querying locally stored data of blockchain nodes, do not provide abundant and dynamic event logs that are only visible from a live large-scale measurement. In this paper, we collect the network logs of three popular permissionless blockchains, that is, Bitcoin, Ethereum, and EOS. The discrepancy between observed events and the public block data is studied via a noble analysis model provided with the soundness of measurement. We share our key findings including a false universal assumption of previous mining-related studies and the block/transaction arrival characteristics.
Fault tolerance of a blockchain is often characterized by the fraction $f$ of "adversarial power" that it can tolerate in the system. Despite the fast progress in blockchain designs in recent years, existing blockchain systems can still only tolerate $f$ below $0.5$. Can practically usable blockchains tolerate a malicious majority, i.e., $f$ above $0.5$? This work presents a positive answer to this question. We first note that the well-known impossibility of {\em byzantine consensus} for $f$ above $0.5$ does not carry over to blockchains. To tolerate $f$ above $0.5$, we use {\em byzantine broadcast}, instead of byzantine consensus, as the core of the blockchain. A major obstacle in doing so, however, is that the resulting blockchain may have extremely low throughput. To overcome this central technical challenge, we propose a novel byzantine broadcast protocol OverlayBB, that can tolerate $f$ above $0.5$ while achieving good throughput. Using OverlayBB as the core, we present the design, implementation, and evaluation of a novel Proof-of-Stake blockchain called BCube. BCube can tolerate a malicious majority, while achieving practically usable transaction throughput and confirmation latency in our experiments with $10000$ nodes and under $f = 0.7$. To our knowledge, BCube is the first blockchain that can achieve such properties.
Abstract In a decentralized network every user makes use of personal identity details at different places for various services and these details are shared with third-parties without their consent and stored at an unknown location. Organizations like government, banks and social platforms are considered to be the weakest point in the current identity management system as they are vulnerable which leads to compromising billions of user identity data. Block chain based User Identity Management is a solution which provides a decentralized environment that manages the user identity data and their related Know-Your-Customer (KYC) documents in a distributed ledger. All the transactions of the network are stored in the block which is a type of a data structure and these blocks are validated using the powerful consensus algorithms and linked to form a block chain. Smart contracts will act as an interface between the client and the block chain network. User’s information cannot be provided to any third party vendors without the explicit consent of the user. This paper proposes a framework for User Identity Management using Block chain technology in a decentralized Network. The proposed framework ensures a high level privacy and security for the personal identity details and the documents. In addition to that the performance analysis of the framework is presented in terms of Transaction, Mining Resource and Difficulty Variation.
Open access
2 source records
Blockchain Technology Applications and Security
Spam and Phishing Detection
Advanced Steganography and Watermarking Techniques
Blockchain is a method of storing data that makes it extremely difficult, if not impossible, to edit, hack, or update the system. As time passes, blockchain technology becomes more popular, and blockchain-based applications, also known as DApps, which will be used in this article, are becoming more widely recognised. We will construct a chat application based on a decentralised network in this article to eliminate its absolute reliance on centralised players. On a peer-to-peer network like Ethereum, the actual message and data will be kept. To offer decentralised storage and efficient lookup, we will employ a blockchain and distributed hash table (DHT). which will describe what we will do, which is a trendy issue among academics and trades. Except for the fact that DApps are important, we currently have a limited grasp of DApps and nature. To close the data gap, this paper presents a file for the most comprehensive blockchain-based DApps analysis to date, which includes 995 Ethereum DApps data and 29,846,075 dealings logs above them. We usually do a descriptive investigation of Dapp preferences, characterise patterns of how Dapps use good contracts to gain access to fundamental blockchain, and evaluate the results issues with the post and misuse of DApps In support of the findings, we propose a large number of Results for DApp users to choose the best DApps. Dapp developers must work harder in order to create more efficient DApps, and vendors must likewise support them.
Security attacks in smart contracts have drawn extensive attention due to the financial loss and erosion of trust caused by vulnerabilities. Even worse, smart contract is a tamper proof digital agreement and fixing bugs in it is difficult, so it is necessary for developers to detect security vulnerabilities in smart contract before deployment. Researchers have proposed several methods on smart contract vulnerability detection. However, despite the numerous vulnerability-finding tools, few of them have ideal detection performance because most of them rely on fixed rules, which is inefficient. In this paper, a modularized and systematic Deep Learning-based framework is proposed to automatically detect smart contracts vulnerability, called DeeSCVHunter. Particularly, we focus on two types of smart contract vulnerabilities: reentrancy and time dependence. And we propose a novel notion of Vulnerability Candidate Slice (VCS) to help models capture the key point of vulnerability. We conduct experiments on real-world dataset and the results prove the effectiveness of VCS, which leads to 25.76% improvement in F1-score at most. And extensive experiments also show that our proposed framework significantly outperforms state-of-the-art methods.
Routa Moussaileb, Nora Cuppens, Jean‐Louis Lanet, Hélène Le Bouder
Ransomware remains an alarming threat in the 21st century. It has evolved from being a simple scare tactic into a complex malware capable of evasion. Formerly, end-users were targeted via mass infection campaigns. Nevertheless, in recent years, the attackers have focused on targeted attacks, since the latter are profitable and can induce severe damage. A vast number of detection mechanisms have been proposed in the literature. We provide a systematic review of ransomware countermeasures starting from its deployment on the victim machine until the ransom payment via cryptocurrency. We define four stages of this malware attack: Delivery, Deployment, Destruction, and Dealing. Then, we assign the corresponding countermeasures for each phase of the attack and cluster them by the techniques used. Finally, we propose a roadmap for researchers to fill the gaps found in the literature in ransomware’s battle.
Cyberattacks constitute a significant threat to information technology systems. Computer worms are used to conduct cyberattacks to compromise computers and the data stored on them. The self-propagation characteristic of computer worms allows them to spread fast and infect many hosts in a computer network. Thus, this makes it difficult for humans to deploy a timely countermeasure to confront worm infections within the attacked network. Worm containment is utilized to stop worm spread in a computer network. The containment technique should be automatic, timely, reliable, and implemented in a distributed manner. In this paper, we introduce Rearguard, a novel blockchain-based automatic worm containment system. Rearguard achieves worm containment by creating and distributing vulnerability-based filters for the vulnerabilities being exploited. A vulnerability-based filter is employed to drop any received network message contains variants of a worm that attempts to exploit the same vulnerability. The vulnerability-based filter generation is carried out utilizing a blockchain smart contract deployed in the attacked network. The blockchain ensures reliability, timely response, trustworthy filters, and the availability of all filters in a distributed ledger that is maintained by network hosts. Rearguard has been implemented against a synthetic worm. The obtained results show that Rearguard introduces low overhead as well as ensures timely and automatic response to worm attacks.
Blockchain analysis is essential for understanding how cryptocurrencies like Bitcoin are used in practice, and address clustering is a cornerstone of blockchain analysis. However, current techniques rely on heuristics that have not been rigorously evaluated or optimized. In this paper, we tackle several challenges of change address identification and clustering. First, we build a ground truth set of transactions with known change from the Bitcoin blockchain that can be used to validate the efficacy of individual change address detection heuristics. Equipped with this data set, we develop new techniques to predict change outputs with low false positive rates. After applying our prediction model to the Bitcoin blockchain, we analyze the resulting clustering and develop ways to detect and prevent cluster collapse. Finally, we assess the impact our enhanced clustering has on two exemplary applications.
As more investors adopt to enter the field of blockchain investment, the Ponzi scheme, a traditional investment scam, has emerged as a hidden fraud in smart contracts. Although some proposed solutions have paid attention to detecting Ponzi schemes in the blockchain, two problems remain: features for detecting Ponzi schemes are incomplete, and algorithms for detecting Ponzi schemes are not sufficiently efficient. Therefore, we innovatively extract the bytecode feature and combine it with user transaction and opcode frequencies to get more comprehensive features. With these features, we propose a smart contract Ponzi scheme identification method based on the improved LightGBM algorithm. Experiments conducted on the real data set of Ethereum prove that our proposed method has improved accuracy dramatically in terms of the$F$-score index and the AUC index compared with the state-of-the-art methods. In addition, model training speed is improved significantly. Therefore, our method more accurately identifies Ponzi schemes in smart contracts, thus reducing investment risk.
Chargeback fraud is a fast-growing threat to e-commerce, bringing huge losses to online sellers, but there is still a lack of effective technical counter measures. To combat chargeback fraud, this paper proposes a consortium blockchain based chargeback fraud data sharing mechanism — CFLedger, which includes multi-source reporting, anti-tamper accounting, and post-supervision. This paper also analyzes the efficiency, effectiveness, and practicality of CFLedger, proving that it is an effective and feasible mechanism to prevent chargeback fraud.
The popularity and amazing attractiveness of cryptocurrencies, and especially Bitcoin, absorb countless enthusiasts daily. Although Blockchain technology prevents fraudulent behavior, it cannot detect fraud on its own. There are always unimaginable ways to commit fraud, and the need to use anomaly detection methods to identify abnormal and fraudulent behaviors has become a necessity. The main purpose of this study is to present a new method for detecting anomalies in Bitcoin with more appropriate efficiency. For this purpose, in this study, the diagnosis of the collective anomaly was used, and instead of diagnosing the anomaly of individual addresses and wallets, the anomaly of users was examined, and the anomaly was more visible among users who had multiple wallets. In addition to using the collective anomaly detection method in this study, the Trimmed_Kmeans algorithm was used for clustering and the proposed method succeeded in identifying 14 users who had committed theft, fraud, and hack with 26 addresses in 9 cases. Compared to previous works, which detected a maximum of 7 addresses in 5 cases of fraud, the proposed method has performed well. Therefore, the proposed method, by presenting a new approach, in addition to reducing the processing power to extract features, succeeded in detecting abnormal users and also was able to find more transactions and addresses committed a scam.
S. Adarsh, Shon G. Joseph, Franklin John, M. B. Lekshmi · 5 authors
Tracing the vaccines from the manufacturing labs till the health centers administering doses to child is a cumbersome process with the involvement of large number of stakeholders and with multiple levels of book-keeping mechanisms. This work primarily focuses on the problem of traceability of immunization vaccines from manufacturing facility, through the distribution mechanisms in cold storage network and the vaccine handling/administration facilities, till the end consumer in the Indian context. The proposed research named, Immunochain is a state-of-the-art big data and blockchain technologies powered, mobile/web-enabled vaccine traceability solution for immunization programs in India. It is vendor neutral, scalable, replicable, and reusable across different immunization programs in any geographical context. Experiments and pilot testing on Indian geography indicates that the proposed approach outperforms some of the existing approaches in vaccine traceability and record management systems.
Badis Hammi, Sherali Zeadally, Yves Christian Elloh Adja, Manlio Del Giudice · 5 authors
Fake check scam is one of the most common attacks used to commit fraud against consumers. This fraud is particularly costly for victims because they generally lose thousands of dollars as well as being exposed to judicial proceedings. Currently, there is no existing solution to authenticate checks and detect fake ones instantly. Instead, banks must wait for a period of more than 48 h to detect the scam. In this context, we propose a blockchain-based scheme to authenticate checks and detect fake check scams. Moreover, our approach allows the revocation of used checks. More precisely, our approach helps the banks to share information about provided checks and used ones, without exposing the banks’ customers’ personal data. We demonstrate a proof of concept of our proposed approach usingNamecoinandHyperledgerblockchain technologies.
Eisa Shaheen, Mohamed Abdl Hamed, Walaa Zaghloul, Eman Al Mostafa · 9 authors
Charities face a difficult funding environment, mainly because charities are not highly transparent. Day over day It became more difficult to know whether donations are reaching the right place, or taking another course, such as financing suspicious campaigns, terrorist projects, etc. As a result, donors lose faith in these organizations, leading to a halt to interactions with charitable organizations. This makes it more difficult for charities to raise money at a great cost. This research proposed a solution to the mentioned problem using Blockchain technology, a decentralized database that provides security, transparency, and lower funding cost by eliminating the presence of third parties between donors and charities. A new track donation model was proposed which introduced several new participants that control the process of donation and remove suspicion of the charity. All donations can be tracked in Blockchain, allowing donors to know where and how their money is being used. The proposed model was implemented using Hyperledger composer then it was tested to obtain a live full track of a donation process. All donations are made using smart contracts, which allow donors to know exactly when and how their donations will be received. The Blockchain track donation system results in time-saving, reducing the cost of donation, and reduces the risk of reaching suspicious campaign donations or terrorist projects. A website was created as a facilitator for users for ease of use of the proposed system.
Tim Weingärtner, Danielle Alves Batista, Sandro Köchli, Gilles Voutat
Corruption in public procurement is a worldwide appearance that causes immense financial and reputational damages. Especially in developing countries, corruption is a widespread issue due to secrecy and lack of transparency. An important instrument for transparency and accountability assurance is the record which is managed and controlled by recordkeeping systems. Blockchain technology and more precisely blockchain-based smart contracts are emerging technological tools that can be used as recordkeeping systems and a tool to mitigate some of the fraud involving public procurement records. Immutability, transparency, distribution and automation are some of the features of smart contracts already implemented in several applications to avoid malicious human interference. In this paper, we discuss some of the frauds in public procurement, and we propose smart contracts to automatize different stages of the public procurement procedure attempting to fix their biggest current weaknesses. The processes we have focused on include the bidding process, supplier habilitation and delivery verification. In the three subprocesses, common irregularities include human fallibility, improper information disclosure and hidden agreements which concern not only governments but also civil society. To show the feasibility and usability of our proposal, we have implemented a prototype that demonstrates the process using sample data.
One of the requirements for mining cryptocurrency (Crypto) is that the secured ledger of the blockchain must be updated. However, updating the secured ledger requires that the miner develop and solve complex mathematical equations in higher orders hexadecimal 64-digit solution called a hash. In addition to this challenge, the mining processes of cryptocurrency are both resource and cost-intensive. The resources required include, but not limited to mining software, hardware, power (energy usage), CPU or compute cycles, and NP-hard problem. Apart from these numerous challenges that are associated with mining cryptocurrency, the amount of speed that is required to mine a single block is core. Cryptocurrency mining speed requirement is significantly important because only miners that can have the fastest mining device are most likely to get the reward (profit) from competing for a block. In this paper, we designed and implemented a model to speed up mining process which is capable of giving miners an advantage to arrive at a block earlier. The novelty of our architecture is that our design is based on high performance computing paradigm where we achieve processor speed up by parallelizing the number of processors p. We experimented by varying p = 4, 8, 16. Our experimental results where we used the MC6800 simulated on Easy68k emulator demonstrate feasibility of our proposed model and prove that speed was an essential key to cryptocurrency mining. Keywords: Cryptocurrency, Blockchain, Architecture, Mining, Speedup, Bitcoin. CISDI Journal Reference Format Allenotor, D. & Oyemade, D. A. (2021): An Optimized Parallel Hybrid Architecture for Cryptocurrency Mining. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 12 No 1, Pp 95-104 DOI - https://doi.org/ 10.22624/AIMS/CISDI/V12N1P10. Available online at www.isteams.net/cisdijournal