Emanuel Vieira, João Almeida, Joaquim Ferreira, Paulo C. Bartolomeu
Cooperative driving is an emerging paradigm to enhance the safety and efficiency of autonomous vehicles. To ensure successful cooperation, road users must reach a consensus for making collective decisions, while recording vehicular data to analyze and address failures related to such agreements. This data has the potential to provide valuable insights into various vehicular events, while also potentially improving accountability measures. Furthermore, vehicles may benefit from the ability to negotiate and trade services among themselves, adding value to the cooperative driving framework. However, the majority of proposed systems aiming to ensure data security, consensus, or service trading, lack efficient and thoroughly validated mechanisms that consider the distinctive characteristics of vehicular networks. These limitations are amplified by a dependency on the centralized support provided by the infrastructure. Furthermore, corresponding mechanisms must diligently address security concerns, especially regarding potential malicious or misbehaving nodes, while also considering inherent constraints of the wireless medium. We introduce the Verifiable Event Extension (VEE), an applicational extension designed for Intelligent Transportation System (ITS) messages. The VEE operates seamlessly with any existing standardized vehicular communications protocol, addressing crucial aspects of data security, consensus, and trading with minimal overhead. To achieve this, we employ blockchain techniques, Byzantine fault tolerance (BFT) consensus protocols, and cryptocurrency-based mechanics. To assess our proposal's feasibility and lightweight nature, we employed a hardware-in-the-loop setup for analysis. Experimental results demonstrate the viability and efficiency of the VEE extension in overcoming the challenges posed by the distributed and opportunistic nature of wireless vehicular communications.
The dark web has become notorious for its association with illicit activities and there is a growing need for systems to automate the monitoring of this space. This paper proposes an end-to-end scalable architecture for the early identification of new Tor sites and the daily analysis of their content. The solution is built using an Open Source Big Data stack for data serving with Kubernetes, Kafka, Kubeflow, and MinIO, continuously discovering onion addresses in different sources (threat intelligence, code repositories, web-Tor gateways, and Tor repositories), downloading the HTML from Tor and deduplicating the content using MinHash LSH, and categorizing with the BERTopic modeling (SBERT embedding, UMAP dimensionality reduction, HDBSCAN document clustering and c-TF-IDF topic keywords). In 93 days, the system identified 80,049 onion services and characterized 90% of them, addressing the challenge of Tor volatility. A disproportionate amount of repeated content is found, with only 6.1% unique sites. From the HTML files of the dark sites, 31 different low-topics are extracted, manually labeled, and grouped into 11 high-level topics. The five most popular included sexual and violent content, repositories, search engines, carding, cryptocurrencies, and marketplaces. During the experiments, we identified 14 sites with 13,946 clones that shared a suspiciously similar mirroring rate per day, suggesting an extensive common phishing network. Among the related works, this study is the most representative characterization of onion services based on topics to date.
Marco Benedetti, Francesco De Sclavis, Marco Favorito, Giuseppe Galano · 7 authors
We introduce a family of PCNs (Payment Channel Networks) characterized by a semi-hierarchical topology and a custom set of channel rebalancing strategies. This family exhibits two interesting benefits, if used as a platform for large-scale, instant, retail payment systems, such as CBDCs: Technically, the solution offers state-of-the-art guarantees of fault-tolerance and integrity, while providing a latency and throughput comparable to centralized systems; from a business perspective, the solution perfectly suits the 3-tier architecture of the current banking ecosystem (central banks / commercial banks / retail users), assigning a pivotal and peculiar role to the members of each tier. Furthermore, the cryptographic privacy of payments for retail users -- typical of PCNs such as the public Lightning Network -- is largely (possibly fully) retained. We study the system by simulating a scaled-down version of a hypothetical European CBDC, exploring the trade-offs among liquidity locked by market operators, payment success rate, throughput, latency, and load on the underpinning blockchain.
In our digital world, access to personal and public data has become an item of concern, with challenging security and privacy aspects. Modern information systems are heterogeneous in nature and have an inherent security vulnerability, which is susceptible to data interception and data modification due to unsecured communication data pipelines between connected endpoints. This re-search article presents a blockchain-based model for securing data pipelines in a heterogeneous information system using an integrated multi-hazard early warning system (MHEWS) as a case study. The proposed model utilizes the inherent security features of blockchain technology to address the security and privacy concerns that arise in data pipelines. The model is designed to ensure data integrity, confidentiality, and authenticity in a decentralized manner. The model is evaluated in a hybrid environment using a prototype implementation and simulation experiments with outcomes that demonstrate advantages over traditional approaches for a tamper-proof and immutable data pipeline for data authenticity and integrity using a confidential ledger.
We introduce semitopologies, a generalisation of point-set topology that removes the restriction that intersections of open sets need necessarily be open. The intuition is that points are participants in some distributed system, and an open set is a collection of participants that can collaborate to update their local state by taking a distributed collaborative action; we call this an actionable coalition. What constitutes an actionable coalition depends on what actions we want to model. Intuitive examples include 'a group of people that is collectively strong enough to lift a rock', where the state update is very simply 'holding rock low' to 'holding rock high' and this update is common to all participants in the actionable coalition. Or, consider 'two people wishing to barter a can of juice for a bar of chocolate', in which case the coalition is any such pair and the state updates differ between participants to flip them between 'has/has no juice' and 'has/has no chocolate'. A characteristic of these systems is that state updates are local to the coalition, voluntary, may vary between participants, and are not assumed subject to permission or synchronisation by a central authority. Peer-to-peer computer networks, including filesharing and blockchain systems, provide motivating examples from computing. This monograph presents a comprehensive view of semitopologies which includes point-set semitopology, algebra, and logic inspired by these considerations. This is interesting in and of itself and it provides a conceptual framework within which to understand a useful class of distributed systems.
We study a new "laminated" queueing model for orders on batched trading venues such as decentralised exchanges. The model aims to capture and generalise transaction queueing infrastructure that has arisen to organise MEV activity on public blockchains such as Ethereum, providing convenient channels for sophisticated agents to extract value by acting on end-user order flow by performing arbitrage and related HFT activities. In our model, market orders are interspersed with orders created by arbitrageurs that under idealised conditions reset the marginal price to a global equilibrium between each trade, improving predictability of execution for liquidity traders. If an arbitrageur has a chance to land multiple opportunities in a row, he may attempt to manipulate the execution price of the intervening market order by a probabilistic blind sandwiching strategy. To study how bad this manipulation can get, we introduce and bound a price manipulation coefficient that measures the deviation from global equilibrium of local pricing quoted by a rational arbitrageur. We exhibit cases in which this coefficient is well approximated by a "zeta value' with interpretable and empirically measurable parameters.
Fueled by the growing popularity of proof-of-stake blockchains, there has been increasing interest and progress in permissioned consensus protocols, which could provide a simpler alternative to existing protocols, such as Paxos and PBFT. In particular, the recently proposed Streamlet protocol provides a surprisingly simple and streamlined consensus approach, which crystallizes years of research in simplifying and improving classical consensus protocols. While the simplicity of Streamlet is a major accomplishment, the protocol lacks certain practical features, such as supporting a stable block proposer, and it makes strong assumptions, such as synchronized clocks and the implicit echoing of all messages. Most importantly, it requires sending $O(N^3)$ messages per block in a network of $N$ nodes, which poses a significant challenge to its application in larger networks. To address these limitations, we introduce Pipelet, a practical streamlined consensus protocol. Pipelet employs the same block-finalization rule as Streamlet, but attains state-of-the-art performance in terms of communication complexity and provides features that are crucial for practical applications, such as clock synchronization and stable block proposers. At the same time, Pipelet retains the simplicity of Streamlet, which presents significant practical advantages, such as ease of implementation and verification.
Kenan Wood, Maurice Herlihy, Hammurabi Mendes, Jonad Pulaj
An automated market maker (AMM) is a state machine that manages pools of assets, allowing parties to buy and sell those assets according to a fixed mathematical formula. AMMs are typically implemented as smart contracts on blockchains, and its prices are kept in line with the overall market price by arbitrage: if the AMM undervalues an asset with respect to the market, an "arbitrageur" can make a risk-free profit by buying just enough of that asset to bring the AMM's price back in line with the market. AMMs, however, are not designed for assets that expire: that is, assets that cannot be produced or resold after a specified date. As assets approach expiration, arbitrage may not be able to reconcile supply and demand, and the liquidity providers that funded the AMM may have excessive exposure to risk due to rapid price variations. This paper formally describes the design of a decentralized exchange (DEX) for assets that expire, combining aspects of AMMs and limit-order books. We ensure liveness and market clearance, providing mechanisms for liquidity providers to control their exposure to risk and adjust prices dynamically in response to situations where arbitrage may fail.
Ignacio Amores-Sesar, Christian Cachin, Philipp Schneider
A family of leaderless, decentralized consensus protocols, called Snow consensus was introduced in a recent whitepaper by Yin et al. These protocols address limitations of existing consensus methods, such as those using proof-of-work or quorums, by utilizing randomization and maintaining some level of resilience against Byzantine participants. Crucially, Snow consensus underpins the Avalanche blockchain, which provides a popular cryptocurrency and a platform for running smart contracts. Snow consensus algorithms are built on a natural, randomized routine, whereby participants continuously sample subsets of others and adopt an observed majority value until consensus is achieved. Additionally, Snow consensus defines conditions based on participants' local views and security parameters. These conditions indicate when a party can confidently finalize its local value, knowing it will be adopted by honest participants. Although Snow consensus algorithms can be formulated concisely, there is a complex interaction between randomization, adversarial influence, and security parameters, which requires a formal analysis of their security and liveness. Snow protocols form the foundation for Avalanche-type blockchains, and this work aims to increase our understanding of such protocols by providing insights into their liveness and safety characteristics. First, we analyze these Snow protocols in terms of latency and security. Second, we expose a design issue where the trade-off between these two is unfavorable. Third, we propose a modification of the original protocol where this trade-off is much more favorable.
Root cause analysis (RCA) in Micro-services architecture (MSA) with escalating complexity encounters complex challenges in maintaining system stability and efficiency due to fault propagation and circular dependencies among nodes. Diverse root cause analysis faults require multi-agents with diverse expertise. To mitigate the hallucination problem of large language models (LLMs), we design blockchain-inspired voting to ensure the reliability of the analysis by using a decentralized decision-making process. To avoid non-terminating loops led by common circular dependency in MSA, we objectively limit steps and standardize task processing through Agent Workflow. We propose a pioneering framework, multi-Agent Blockchain-inspired Collaboration for root cause analysis in micro-services architecture (mABC), where multiple agents based on the powerful LLMs follow Agent Workflow and collaborate in blockchain-inspired voting. Specifically, seven specialized agents derived from Agent Workflow each provide valuable insights towards root cause analysis based on their expertise and the intrinsic software knowledge of LLMs collaborating within a decentralized chain. Our experiments on the AIOps challenge dataset and a newly created Train-Ticket dataset demonstrate superior performance in identifying root causes and generating effective resolutions. The ablation study further highlights Agent Workflow, multi-agent, and blockchain-inspired voting is crucial for achieving optimal performance. mABC offers a comprehensive automated root cause analysis and resolution in micro-services architecture and significantly improves the IT Operation domain. The code and dataset are in https://github.com/zwpride/mABC.
The emergence of distributed systems has revolutionized the financial technology (Fintech) landscape, offering unprecedented opportunities for enhancing security, scalability, and efficiency in financial operations. This paper explores the role of distributed systems in Fintech, analyzing their architecture, benefits, challenges, and applications. It examines key distributed technologies such as blockchain, decentralized finance (DeFi), and distributed ledger technology (DLT), and their impact on various aspects of the financial industry, and future directions for distributed systems in Fintech.
Burak Can Sahin, Abdulrezzak Zekiye, Oznur Ozkasap
For the purpose of enabling, democratizing, and reducing the fees of peer-to-peer energy trading for battery-powered devices, we propose ANKA as a fully decentralized energy marketplace for peers with battery-powered devices. ANKA utilizes state-of-the-art technologies, namely blockchain, smart contracts, and decentralized applications. Within this marketplace, users who possess surplus energy actively offer their excess energy for trading. Concurrently, consumers can readily explore the energy options available and make purchases according to their individual preferences while taking into consideration the location of the offered energy and voltage compatibility. In addition, we provide a comparison between a centralized traditional market and our proposed solution, identifying that the cost of deploying and operating ANKA is less than the centralized approach. We also position ANKA in comparison to the recent blockchain-based decentralized energy marketplaces by considering the metrics of blockchain type, scope, trading entities and the presence of third parties.
A myriad of authentication mechanisms embody a continuous evolution from verbal passwords in ancient times to contemporary multi-factor authentication. Nevertheless, digital asset heists and numerous identity theft cases illustrate the urgent need to revisit the fundamentals of user authentication. We abstract away credential details and formalize the general, common case of asynchronous authentication, with unbounded message propagation time. Our model, which might be of independent interest, allows for eventual message delivery, while bounding execution time to maintain cryptographic guarantees. Given credentials' fault probabilities (e.g., loss or leak), we seek mechanisms with the highest success probability. We show that every mechanism is dominated by some Boolean mechanism -- defined by a monotonic Boolean function on presented credentials. We present an algorithm for finding approximately optimal mechanisms. Previous work analyzed Boolean mechanisms specifically, but used brute force, which quickly becomes prohibitively complex. We leverage the problem structure to reduce complexity by orders of magnitude. The algorithm is readily applicable to practical settings. For example, we revisit the common approach in cryptocurrency wallets that use a handful of high-quality credentials. We show that adding low-quality credentials improves security by orders of magnitude.
In the PoS blockchain landscape, the challenge of achieving full decentralization is often hindered by a disproportionate concentration of staked tokens among a few validators. This study analyses this challenge by first formalizing decentralization metrics for weighted consensus mechanisms. An empirical analysis across ten permissionless blockchains uncovers significant weight concentration among validators, underscoring the need for an equitable approach. To counter this, we introduce the Square Root Stake Weight (SRSW) model, which effectively recalibrates staking weight distribution. Our examination of the SRSW model demonstrates notable improvements in the decentralization metrics: the Gini index improves by $37.16 \%$ on average, while Nakamoto coefficients for liveness and safety see mean enhancements of $101.04 \%$ and $80.09 \%$, respectively. This research is a pivotal step toward a more fair and equitable distribution of staking weight, advancing the decentralization in blockchain consensus mechanisms.
Decentralized finance revolutionizes traditional financial systems by leveraging blockchain technology to reduce trust. However, some vulnerabilities persist, notably front-running by malicious actors who exploit transaction information to gain financial advantage. Consensus with a fair order aims at preventing such attacks, and in particular, the differential order fairness property addresses this problem and connects fair ordering to the validity of consensus. The notion is implemented by the Quick Order-Fair Atomic Broadcast (QOF) protocol (Cachin et al., FC ‘22). This paper revisits the QOF protocol and describes a modular implementation that uses a generic consensus component. Moreover, an empirical evaluation is performed to compare the performance of QOF to a consensus protocol without fairness. Measurements show that the increased complexity comes at a cost, throughput decreases by at most 5%, and latency increases by roughly 50 ms, using an emulated ideal network. This paper contributes to a comprehensive understanding of practical aspects regarding differential order fairness with the QOF protocol and also connects this with similar fairness-imposing protocols like Themis and Pompē.
Dec 19, 2023·In: Miraz, M.H., Southall, G., Ali, M., Ware, A. (eds) Emerging Technologies in Computing. iCETiC 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 538. Springer, Cham
Mohammad Ali Tareq, Piyush Tripathi, Nurhayati Md. Issa, Mahdi H. Miraz
Although blockchain technology was first introduced in 2008 and materialised in 2009, the early usage of blockchain were mainly limited to financial technologies, particularly cryptocurrencies. Later, blockchain became a widespread emerging technology, utilised in multifaceted sectors and applications. In fact, various new and innovative application of blockchain and distributed ledger technologies are still continuously being researched and explored. On the other hand, smart-contracts were first introduced in 1990s, however, it did not gain enough popularity until being integrated with blockchain technologies lately. The duo lately been seen as the key to many innovations in various industries and sectors. So, we took data from 1445 blockchain-related patent documents and tried to map out the historical and current trends in patenting activities in the blockchain field. This helps us get a better grasp of how blockchain technologies are evolving and being tracked. In addition to serving as an indicator of science and technology growth, patents are also used to judge the research potential and development of a particular technology.
Ryan Zarick, Bryan Pellegrino, Isaac Zhang, Thomas Kim · 5 authors
In this paper, we present the first intrinsically secure and semantically universal omnichain interoperability protocol: LayerZero. Utilizing an immutable endpoint, append-only verification modules, and fully-configurable verification infrastructure, LayerZero provides the security, configurability, and extensibility necessary to achieve omnichain interoperability. LayerZero enforces strict application-exclusive ownership of protocol security and cost through its novel trust-minimized modular security framework which is designed to universally support all blockchains and use cases. Omnichain applications (OApps) built on the LayerZero protocol achieve frictionless blockchain-agnostic interoperation through LayerZero's universal network semantics.
The core of a blockchain network is its consensus algorithm. Starting with the Proof-of-Work, there have been various versions of consensus algorithms, such as Proof-of-Stake (PoS), Proof-of-Authority (PoA), and Practical Byzantine Fault Tolerance (PBFT). Each of these algorithms focuses on different aspects to ensure efficient and reliable processing of transactions. Blockchain operates in a decentralized manner where there is no central authority and the network is composed of diverse users. This openness creates the potential for malicious nodes to disrupt the network in various ways. Therefore, it is crucial to embed a mechanism within the blockchain network to constantly monitor, identify, and eliminate these malicious nodes. However, there is no one-size-fits-all mechanism to identify all malicious nodes. Hence, the dynamic adaptability of the blockchain network is important to maintain security and reliability at all times. This paper introduces MRL-PoS, a Proof-of-Stake consensus algorithm based on multi-agent reinforcement learning. MRL-PoS employs reinforcement learning for dynamically adjusting to the behavior of all users. It incorporates a system of rewards and penalties to eliminate malicious nodes and incentivize honest ones. Additionally, MRL-PoS has the capability to learn and respond to new malicious tactics by continually training its agents.
Although blockchains have become widely popular for their use in cryptocurrencies, they are now becoming pervasive as more traditional applications adopt blockchain to ensure data security. Despite being a secured network, blockchains have some tradeoffs such as high latency, low throughput, and transaction failures. One of the core problems behind these is improper management of "conflicting transactions", which is also known as "contention". When there is a large pool of pending transactions in a blockchain and some of them are conflicting, a situation of contention occurs, and as a result, the latency of the network increases, and a substantial amount of resources are wasted which results in low throughput and transaction failures. In this paper, we proposed ConChain, a novel blockchain scheme that combines transaction parallelism and an intelligent dependency manager to minimize conflicting transactions in blockchain networks as well as improve performance. ConChain is also capable of ensuring proper defense against major attacks due to contention.
Yanni Georghiades, Rajesh Mishra, Karl Kreder, Sriram Vishwanath
We study the incentives behind double-spend attacks on Nakamoto-style Proof-of-Work cryptocurrencies. In these systems, miners are allowed to choose which transactions to reference with their block, and a common strategy for selecting transactions is to simply choose those with the highest fees. This can be problematic if these transactions originate from an adversary with substantial (but less than 50\%) computational power, as high-value transactions can present an incentive for a rational adversary to attempt a double-spend attack if they expect to profit. The most common mechanism for deterring double-spend attacks is for the recipients of large transactions to wait for additional block confirmations (i.e., to increase the attack cost). We argue that this defense mechanism is not satisfactory, as the security of the system is contingent on the actions of its users. Instead, we propose that defending against double-spend attacks should be the responsibility of the miners; specifically, miners should limit the amount of transaction value they include in a block (i.e., reduce the attack reward). To this end, we model cryptocurrency mining as a mean-field game in which we augment the standard mining reward function to simulate the presence of a rational, double-spending adversary. We design and implement an algorithm which characterizes the behavior of miners at equilibrium, and we show that miners who use the adversary-aware reward function accumulate more wealth than those who do not. We show that the optimal strategy for honest miners is to limit the amount of value transferred by each block such that the adversary's expected profit is 0. Additionally, we examine Bitcoin's resilience to double-spend attacks. Assuming a 6 block confirmation time, we find that an attacker with at least 25% of the network mining power can expect to profit from a double-spend attack.
Ethereum introduced Transaction Access Lists (TALs) in 2020 to optimize gas costs during transaction execution. In this work, we present a comprehensive analysis of TALs in Ethereum, focusing on adoption, quality, and gas savings. Analyzing a full month of mainnet data with 31,954,474 transactions, we found that only 1.46% of transactions included a TAL, even though 42.6% of transactions would have benefited from it. On average, access lists can save around 0.29% of gas costs, equivalent to approximately 3,450 ETH (roughly US$ 5 Mio) per year. However, 19.6% of TALs included by transactions contained imperfections, causing almost 11.8% of transactions to pay more gas with TAL than without. We find that these inaccuracies are caused by the unknown state at the time of the TAL computation as well as imperfect TAL computations provided by all major Ethereum clients. We thus compare the gas savings when calculating the TAL at the beginning of the block vs. calculating it on the correct state, to find that the unknown state is a major source of TAL inaccuracies. Finally, we implement an ideal TAL computation for the Erigon client to highlight the cost of these flawed implementations.
Reddiment is a web-based dashboard that links sentiment analysis of subreddit texts with share prices. The system consists of a backend, frontend and various services. The backend, in Node.js, manages the data and communicates with crawlers that collect Reddit comments and stock market data. Sentiment is analyzed with the help of Vader and TextBlob. The frontend, based on SvelteKit, provides users with a dashboard for visualization. The distribution is carried out via Docker containers and Docker Compose. The project offers expansion options, e.g. the integration of cryptocurrency rates. Reddiment enables the analysis of sentiment and share prices from subreddit data.
In recent years, we have seen an increase in the adoption of blockchain-based systems in non-financial applications, looking to benefit from what the technology has to offer. Although many fields have managed to include blockchain in their core functionalities, the adoption of blockchain, in general, is constrained by the so-called trilemma trade-off between decentralization, scalability, and security. In our previous work, we have shown that using a digital twin for dynamically managing blockchain systems during runtime can be effective in managing the trilemma trade-off. Our Digital Twin leverages DDDAS feedback loop, which is responsible for getting the data from the system to the digital twin, conducting optimisation, and updating the physical system. This paper examines how leveraging DDDAS feedback loop can support the optimisation component of the trilemma benefiting from Reinforcement Learning agents and a simulation component to augment the quality of the learned model while reducing the computational overhead required for decision-making.
We present parallel proof-of-work with DAG-style voting, a novel proof-of-work cryptocurrency protocol that, compared to Bitcoin, provides better consistency guarantees, higher transaction throughput, lower transaction confirmation latency, and higher resilience against incentive attacks. The superior consistency guarantees follow from implementing parallel proof-of-work, a recent consensus scheme that enforces a configurable number of proof-of-work votes per block. Our work is inspired by another recent protocol, Tailstorm, which structures the individual votes as tree and mitigates incentive attacks by discounting the mining rewards proportionally to the depth of the tree. We propose to structure the votes as a directed acyclic graph (DAG) instead of a tree. This allows for a more targeted punishment of offending miners and, as we show through a reinforcement learning based attack search, makes the protocol even more resilient to incentive attacks. An interesting by-product of our analysis is that parallel proof-of-work without reward discounting is less resilient to incentive attacks than Bitcoin in some realistic network scenarios.