Handling congestion in blockchain systems is a fundamental problem given that the security and decentralization objectives of such systems lead to designs that compromise on (horizontal) scalability (what sometimes is referred to as the "blockchain trilemma"). Motivated by this, we focus on the question whether it is possible to design a transaction inclusion policy for block producers that facilitates fee and delay predictability while being incentive compatible at the same time. Reconciling these three properties is seemingly paradoxical given that the dominant approach to transaction processing is based on first-price auctions (e.g., as in Bitcoin) or dynamic adjustment of the minimum admissible fee (e.g. as in Ethereum EIP-1559) something that breaks fee predictability. At the same time, in fixed fee mechanisms (e.g., as in Cardano), fees are trivially predictable but are subject to relatively inexpensive bribing or denial of service attacks where transactions may be delayed indefinitely by a well funded attacker, hence breaking delay predictability. In this work, we set out to address this problem by putting forward blockchain space tokenization (BST), namely a new capability of a blockchain system to tokenize its capacity for transactions and allocate it to interested users who are willing to pay ahead of time for the ability to post transactions regularly for a period of time. We analyze our system in the face of worst-case transaction-processing attacks by introducing a security game played between the mempool mechanism and an adversary. Leveraging this framework, we prove that BST offers predictable and asymptotically optimal delays, predictable fees, and is incentive compatible, thus answering the question posed in the affirmative.
Junaid Nasir Qureshi, Muhammad Shoaib Farooq, Adel Khelifi, Zabihullah Atal
Distributed Scrum of Scrums Agile Software Development (DSsASD) plays a crucial role in modern software development, enabling collaboration across vast distances in software development. However, existing tools and frameworks have struggled to effectively address communication, teamwork, and collaboration challenges within DSsASD teams. These challenges include transparency, trust, traceability, auditability, and security concerns, these issues are related to project delays, client dissatisfaction, contract cancellations, miscommunication collaboration, and payment disputes between stakeholders. To tackle these persistent concerns and issues, this article introduces ChainAgilePlus, a novel framework that integrates blockchain technology in the distributed scrum of scrum agile software development. ChainAgilePlus implements smart contracts by utilizing a private Ethereum blockchain to govern acceptance acknowledgment, deployment testing, secure payments, verification of developer payments, and automated payment distribution to team wallets. Smart contracts also enforce penalties for delayed payments and overdue tasks, promoting accountability and adherence to deadlines. Additionally, ChainAgilePlus mitigates blockchain scalability challenges by integrating the Interplanetary File System (IPFS) for off-chain storage. Empirical results from experimental processes demonstrate the effectiveness of ChainAgilePlus in enhancing communication, transparency, coordination, traceability, auditability, security, and trust among clients and developers in DSsASD projects.
The Blockchain is a distributed and decentralized ledger that has been in use since its inception and is being used in a variety of industries, including cross-border payment systems, and cryptocurrencies such as Bitcoin and Ethereum. As a result, various tools and frameworks are available to detect these vulnerabilities. This study identifies various cyber-attacks that occurred in the last two years. Some smart contract-specific vulnerabilities are also investigated, including Call to Unknown, Reentrancy, Immutable bug, Out of Gas, Code Injection, DoS with Failed Call, and Outdated Compiler. Various detection methods are discussed. This allows for the creation of a framework to address these issues and mitigate vulnerabilities such as DoS with Failed call and Outdated compiler, which were not accurately detected by previous symbolic execution-based detection tools. The framework's execution method will be symbolic. It is capable of efficiently identifying and detecting the aforementioned vulnerabilities. Many issues against various vulnerabilities can be solved by detecting and debugging the code itself before deployment.
We present PineSU, a lightweight system that integrates Git with the Ethereum blockchain for sharing electronic documents, enabling decentralized integrity protection and timestamping. PineSU introduces the concept of Storage Unit (SU for short), which is essentially a Git repository along with some descriptor files needed to interact with the blockchain. SUs can be open or closed. Open SUs serve to secure Git repositories whose content may change in the future. At any moment, users can create a Blockchain Synchronization Point (BSP for short) of their open SUs. This allows for a rigorous integrity and authenticity verification of the corresponding digital documents. Whereas closed SUs are mainly a mechanism to invalidate any change to a Git repository. They are useful when a set of files must be definitively archived and made immutable, while enabling their sharing securely. As shown by a case study on clones of two public repositories on GitHub (owned by the Italian government) containing reports and data about the COVID-19 diffusion, PineSU has proven to be very effective in protecting Git repositories under a few security hypotheses that are easy to guarantee in many circumstances. Furthermore, an experimental and simulated performance evaluation demonstrates that the system scales well for storage units of increasing sizes and structure complexity. Finally, a qualitative comparison with existing solutions shows the strengths of PineSU against state-of-the-art approaches.
Blockchains like Hyperledger Fabric are comparably faster than public blockchains like Ethereum. These Permissioned Blockchains do not need to consider for in-built security like Bitcoin. The core part of Hyperledger Fabric, the orderer, replicates blocks across the blockchain network. The orderer f
Time-varying graphs are increasingly common in financial, social and biological data analysis applications. Feature extraction that efficiently encodes the complex structure of sparse, multi-layered, dynamic graphs presents computational and methodological challenges. In the past decade, topological data analysis has become a popular method of studying the shape of data. This is achieved by building an increasing sequence of simplicial complexes (called filtration) indexed by a scale parameter on top of the data to keep track of topological changes along with the filtration. This multi-scale summary, called persistence diagram (PD), is often vectorized to be used in machine learning algorithms. This paper introduces a topological approach to extract information on higher-order interactions encoded in persistence diagrams from graph data. Our framework has two main steps: first, we convert the graph into a higher-dimensional simplicial complex by adding structures such as triangles, tetrahedrons etc., and compute a PD using the so-called lower-star filtration which utilizes quantitative node attributes. Then, we vectorize the PD by averaging the associated Betti function over successive scale values of a one-dimensional grid using integration. A notable aspect of our procedure is that it avoids embedding a graph into a metric space. We show that the proposed vectorization summary is robust against input noise with respect to the $ L_1 $ 1-Wasserstein distance. In simulation studies, the proposed approach leads to improved change point detection rates and outperforms one of the state-of-the-art methods for anomaly detection in time-varying graphs. In real data application, our approach leads to up to a 20% gain in anomalous price prediction in the Ethereum cryptocurrency transaction network.
Public blockchains, such as Ethereum, rely on decentralized networks of peer-to-peer nodes known as validators or miners to verify all transactions and create new valid blocks. These validators can prioritize transactions, primarily based on high gas fees, allowing miners to maximize their block rewards, a concept referred to as maximal extractable value (MEV). However, MEV is vulnerable to front-running, back-running, and sandwich attacks (FBSAs), and is exploited by malicious nodes and bots to manipulate users’ valuable transactions. These malicious activities adversely impact the Blockchain’s scalability, transparency, and security. Flashbots, as one of the solutions, introduces centralization since all nodes have to forward all blocks to the central node. To address these issues, we have designed a new Blockchain transaction ordering protocol called Quantum Random Transaction Ordering Protocol (Q-RTOP). The proposed protocol operates on top of the existing Blockchain transaction ordering mechanism. However, instead of allowing validators to select transactions based on high gas fees, decentralized nodes running Q-RTOP securely randomize all transactions and then forward them to the validators, which proceed with the block validation without any change. Our protocol primarily focuses on randomizing transactions before being processed by the validators by utilizing a quantum random generator as a secure source of randomness. The final results demonstrated that Q-RTOP effectively secured user transactions and randomized 8192 transactions within 25 milliseconds.
Abdul Muqsit Haji Jafari, Ravi Kumar Patchmuthu, Sharul Tajuddin
The COVID-19 pandemic has presented numerous challenges around the world, including lockdowns, remote working and studying, and travel restrictions imposed by governments to curb the spread of the virus. As vaccines have become more widely available, restrictions have begun to ease for those vaccinated. Many countries use paper-based COVID-19 vaccination certificates to prove vaccination status. However, traditional certificates are vulnerable to forgery and counterfeiting. We developed a blockchain-based system where vaccination certificates are stored and accessed via smart contracts on an Ethereum blockchain. The certificates are stored in a tamper-proof, decentralized manner, ensuring secure verification of vaccination status. This research project successfully designed and implemented a system that securely stores and verifies vaccination certificates using blockchain, demonstrating the benefits of this approach. According to our knowledge, this is the first kind of research initiative in Brunei to develop a blockchain-based immutable COVID-19 Vaccination certificate. Despite its advantages, blockchain still has its flaws, particularly in scalability and adoption, which should be considered for further optimization of the proposed system.
File sharing has become a common practice in our daily lives, but there are many concerns regarding security and privacy for various reasons. To address these concerns, researchers are exploring different storage and sharing options. Blockchain technology, with its decentralized and immutable nature, provides an appealing solution. The proposed hybrid file-sharing system offers a unique approach to combining the benefits of centralized user management and decentralized storage. By utilizing Ethereum smart contracts for access control, the system ensures secure and transparent sharing of files. Moreover, using the Inter Planetary File System for decentralized storage ensures high availability and scalability. This concept attempts to solve the accessibility, privacy, and security issues related to centralized and decentralized systems by utilizing the advantages of both models. The centralized system will help manage user authentication and authorization, making it easier for individuals unfamiliar with blockchain technology to interact with the system. On the other hand, IPFS provides decentralized storage with data redundancy and reliability. Access control is implemented through Ethereum smart contracts, which restrict file access to unauthorized users. Overall, this paper demonstrates how blockchain technology, with a hybrid model, can create a user-friendly, secure file-sharing environment immune to common vulnerabilities in centralized systems.
Ananda Ravuri, M. Sadish Sendil, Moshe Rani, A. Srikanth · 7 authors
Protection of the Internet of Things (IoT) has become a significant concern due to the widespread use of IoT technologies. Conventional Intrusion Detection Systems (IDS) have challenges when used in IoT networks because of resource restrictions and complexities. Blockchain Technology (BCT) has significantly altered organizations' financial behavior and effectiveness in recent years. Data security and system stability are crucial concerns that must be tackled in blockchain systems. The study suggests a mechanism called Deep Blockchain-Enabled Collaborative Anomaly Detection (DBC-CAD) for security-focused distributed Anomaly Detection (AD) and privacy-focused BC with smart contracts in IoT networks. A Modified - Long Short-Term Memory (M-LSTM) based Deep Learning (DL) algorithm with a multi-variable optimization approach has been used for the AD approach. The multi-variable optimization technique has been used to set the hyperparameters. The Ethereum framework creates privacy-focused BC and smart contract techniques that safeguard decentralized AD engines. The proposed M-LSTM model has the highest detection rate of 99.1%. The findings show the effectiveness of the proposed systems in identifying assaults on IoT networks.
The main goal of the research is to predict the future monthly returns of cryptocurrencies using the Vector Error Correction Model (VECM). Time series for the period 2018-2021 consists o f data on monthly returns for the cryptocurrencies Bitcoin, Ethereum and Ripple, as well as monthly returns on gold and the S&P500 stock index. Within the VECM, using the Johansen and Granger tests, short-term cointegration and causality among variables were determined, without the existence o f long-term equilibrium. The resulting model for short-term prediction o f the monthly returns o f the cryptocurrency Bitcoin was evaluated as unbiased and stable with a realistic forecast error o f 0.168 (16.8%).
Yongxin Zhang, Jiacheng Yang, Hong Lei, Zijian Bao · 7 authors
A verifiable random function (VRF) is a pseudorandom function that enables source verification. By providing a public verification key and accompanying proof with the output, all parties can verify the correctness of the output without interaction. VRF has gained widespread adoption in blockchain applications, including Algorand, Ouroboros, and ChainLink. This article introduces SM2VRF, the first VRF based on the Chinese standard SM2 cryptographic algorithm, and extends it to a batch construction called SM2VRF-B for efficient verification of multiple sources. We showcase the applicability of SM2VRF in an electronic random committee election scenario, where the blockchain is utilized for storing candidate parameters and votes. By employing the Hamming distance, our scheme eliminates the risk of election failure. We provide a security proof for the proposed scheme, followed by an evaluation of the performance of both SM2VRF and SM2VRF-B. We implement our committee election scheme with Ethereum to assess the feasibility and efficiency.
Addressing the shortcomings of traditional voting systems, such as fraud, lack of transparency, and inefficiency, this paper proposes a decentralized voting model based on the Polygon blockchain. The model leverages the inherent characteristics of blockchain, including anti-tampering, transparency, and security, to establish a secure and trustworthy voting mechanism. By utilizing smart contract, the model automates voting, enforces rules, records ballots in real-time, and prevents double voting, thereby significantly reduces human errors and election fraud. It also ensures privacy, eliminates coercion, and realizes on-chain tallying using the Paillier homomorphic encryption. The model is analyzed in terms of security and deployed on Polygon, a sidechain of Ethereum. Comparisons with other schemes demonstrate that this model is also optimized for reducing gas cost.
F Farnuod Ahmadi, Abbas Toloie Eshlaghy, Reza Radfar
Cryptocurrencies have been widely identified and established as a new form of electronic currency exchange, carrying significant implications for emerging economies and the global economy. This research focused on the "examination and comparison of the efficiency of MLP and SimpleRNN algorithms in predicting cryptocurrency prices" using the Python programming language. Price predictions for Bitcoin, Ethereum, Binance Coin, Cardano, and Ripple were made using two deep learning algorithms (including the MLP algorithm and the SimpleRNN algorithm) over the period from 2017 to 2023. The results of cryptocurrency price prediction using deep learning algorithms were satisfactory; and the comparison of predictions across all cryptocurrencies indicated minimal differences between the algorithms studied, suggesting that they were efficient and had low error rates. Based on the obtained results regarding Bitcoin price prediction, the best algorithm was SimpleRNN; for Ethereum price prediction, the best algorithm was MLP; for Binance Coin price prediction, the best algorithm was SimpleRNN; for Cardano price prediction, the best algorithm was MLP; and for Ripple price prediction, the best algorithm was MLP.
In recent years, the convenience and potential use of crypto-assets such as Bitcoin and Ethereum have attracted increasing attention. On the other hand, there have been reports of attacks on the blockchain networks that support crypto-assets in an attempt to steal other users’ assets. In the past, research on attack observation against blockchains has used techniques such as holding real crypto-assets to lure attackers into honeypots or falsifying balances to attackers. However, these methods risk losing crypto-assets to attackers or being exposed as honeypots to attackers. To solve these problems, we propose a new RPC (Remote Procedure Call) honeypot method that returns the wallet address of another partya. holding a high balance in response to an attacker’s request, thereby luring the attacker without having the real crypto-assets. Our experimental evaluation shows that this method can attract more attackers than the method with zero-balance wallets and can observe more sophisticated attacks. Furthermore, we proposed a risk reduction strategy for crypto-asset theft by applying the idea of our method. In the log analysis process, we devised a new clustering method using the number of times an attacker executes a specific method as a feature. By applying this method, we successfully classified attackers based on their objectives, demonstrating the efficient analysis of vast amounts of log data.
M R Shrihari, J Lubna Saira, N Ajay, M. Mahesh · 6 authors
The advancement of smart farming, a crucial aspect of the Internet of Things (IoT), facilitates data-driven insights to enhance agricultural efficiency. However, the widespread deployment of IoT devices presents notable concerns related to data security and integrity. This paper introduces AgriChainSynch, a robust framework integrating blockchain, IoT, and artificial intelligence (AI) to strengthen the security, privacy, and operational efficiency of smart farming ecosystems. The framework utilizes a distributed ledger system to ensure tamper-proof data management, incorporates a Blockchain Integration Layer (BIL) for scalability, and features a Feedback and Adaptation Module (FAM) for continuous performance enhancement. By leveraging AWS Cloud, ESP32, and Ethereum Rinke by smart contracts, the system is capable of detecting and mitigating security threats in real time. Experimental evaluations demonstrate improvements in network efficiency, data storage optimization, and transaction processing speed. Additionally, the study establishes a link between faster threat response times and increased blockchain transaction success rates. The results underscore the feasibility of integrating blockchain, AI, and IoT to develop secure, scalable, and efficient precision agriculture solutions.
Paulo Victor Dias, Helena Gonçalves, Firmino Silva, Jorge Duque · 6 authors
Over the past decade, significant technological advancements have taken place, with a strong focus on systems designed to enhance the security and reliability of distributed data across various sectors, both public and private. Among these technological innovations, blockchain technology has emerged as a standout paradigm, offering unique characteristics that guarantee data security and reliability in different applications. The model presented represents a culmination of efforts that trace their origins to the Ethereum platform, restructured to align with Hyperledger Fabric. This transformation elaborated according to the comprehensive analysis of various Hyperledger frameworks involving segmentation, comparison, and selection of components that augment the model's conceptual foundation. The overarching objective of this article is twofold: firstly, to delve into the exploration of Hyperledger technology, substantiating the rationale behind the chosen implementation model, and secondly, to showcase the model's restructuring within the framework presented. A central facet of this article is a compelling case study that illustrates the transformative potential of blockchain technology in reshaping existing systems and processes. Specifically, integrating Hyperledger Fabric as an enterprise-grade technology underscores its role in facilitating the practical implementation of blockchain solutions. Ultimately, the primary contribution of this article lies in its endeavour to formalise knowledge surrounding blockchain technology applicability and to underscore its potential societal and organisational benefits. By presenting a well-structured model and a practical case study, this work aims to advance the comprehension of blockchain's relevance and capacity to drive positive change across various domains.