Decentralized Finance (DeFi) leverages blockchain-enabled smart contracts to deliver automated and trustless financial services without the need for intermediaries. However, the public visibility of financial transactions on the blockchain can be exploited, as participants can reorder, insert, or remove transactions to extract value, often at the expense of others. This extracted value is known as the Maximal Extractable Value (MEV). MEV causes financial losses and consensus instability, disrupting the security, efficiency, and decentralization goals of the DeFi ecosystem. Therefore, it is crucial to analyze, detect, and mitigate MEV to safeguard DeFi. Our comprehensive survey offers a holistic view of the MEV landscape in the DeFi ecosystem. We present an in-depth understanding of MEV through a novel taxonomy of MEV transactions supported by real transaction examples. We perform a critical comparative analysis of various MEV detection approaches, evaluating their effectiveness in identifying different transaction types. Furthermore, we assess different categories of MEV mitigation strategies and discuss their limitations. We identify the challenges of current mitigation and detection approaches and discuss potential solutions. This survey provides valuable insights for researchers, developers, stakeholders, and policymakers, helping to curb and democratize MEV for a more secure and efficient DeFi ecosystem.

Permissionless blockchains promise resilience against censorship by a single entity. This suggests that deterministic rules, not third-party actors, decide whether a transaction is appended to the blockchain. In 2022, the U.S. ØFAC sanctioned a Bitcoin mixer and an Ethereum application, challenging the neutrality of permissionless blockchains.

Geopolitical conflicts significantly impact financial networks and systems, e.g., Russia and Ukraine. Cryptoeconomic blockchains such as Bitcoin and Ethereum were introduced as substitutes for traditional financial systems and might behave differently under significant stress. The Russia–Ukraine conflict allowed us to analyze the impact of such complex geopolitical conflicts on the user behaviors of cryptoeconomic blockchains. This article investigates the early stage of such geopolitical conflict using time-varying graphs. We collected and analyzed all the transactions for Bitcoin and Ethereum that took place 2 weeks before and after the conflict started, i.e., we focused on what can be defined as the acute impact of such an event. Our results suggest that the early stage of such geopolitical conflicts may significantly affect cryptoeconomic blockchains’ user behaviors. For instance, we detected that some users behaved more cautiously during the preconflict phase and resumed normalcy during the postconflict phase but exhibited a shift in their behavior. This article analyzes the relationship between the early stages of geopolitical conflicts and cryptoeconomic systems.

Privacy within the Bitcoin ecosystem has been critical for the operation and propagation of the system since its very first release. While various entities have sought to deanonymize and reveal user identities, the default semi-anonymous approach to privacy was judged as insufficient and the community developed a number of advanced privacy-preservation mechanisms. In this study, we propose an improved variant of the multiple-input clustering approach that incorporates advanced privacy-enhancing techniques. We examine the CoinJoin-adjusted user graph of Bitcoin through quantitative network analysis and draw conclusions on the effectiveness of our proposed clustering method compared to naive multiple-input clustering. Our findings indicate that CoinJoin transactions can significantly distort commonly applied address clustering approaches. Moreover, we demonstrate that Bitcoin's user graph has become less dense in recent years, concurrent with the collapse of several independent user clusters. Our results contribute to a more comprehensive understanding of privacy aspects in the Bitcoin transaction network and lay the groundwork for developing enhanced measures to prevent money laundering and terrorism financing.

Stealth addresses represent an approach to enhancing privacy within public and distributed blockchains, such as Ethereum and Bitcoin. Stealth address protocols employ a distinct, randomly generated address for the recipient, thereby concealing interactions between entities. In this study, we introduce BaseSAP, an autonomous base-layer protocol for embedding stealth addresses within the application layer of programmable blockchains. BaseSAP expands upon previous research to develop a modular protocol for executing unlinkable transactions on public blockchains. BaseSAP allows for the development of additional stealth address layers using different cryptographic algorithms on top of the primary implementation, capitalizing on its modularity. To demonstrate the effectiveness of our proposed protocol, we present simulations of an advanced Secp256k1-based dual-key stealth address protocol. This protocol is developed on top of BaseSAP and deployed on the Ethereum test network as the first prototype implementation. Furthermore, we provide cost analyses and underscore potential security ramifications and attack vectors that could affect the privacy of stealth addresses. Our study highlights the flexibility of the BaseSAP protocol and provides insights into the broader implications of stealth address technology in the realm of blockchain privacy.

Industrial advancements in information and communications technology facilitated the widespread use of line current differential relays (LCDRs) for protecting critical transmission lines due to their fast, sensitive, selective, and secure performance. Despite their advantages, LCDRs' reliance on vulnerable communication networks to swap current measurements makes them vulnerable to cyberattacks. In this article, a scheme is proposed to protect LCDRs from direct-false-tripping (DFT), fault-masking (FM), and sympathetic-tripping (ST) cyberattacks, which have not been studied together before for transmission-level LCDRs. The proposed scheme utilizes a deep neural network (DNN), trained offline on features extracted from only the measurements available for LCDRs. The trained DNN model can then be implemented within LCDRs. Unlike the previous solutions, which only differentiate between faults and DFT cyberattacks, the proposed scheme actively differentiates between authentic and manipulated LCDR measurements to detect and mitigate possible cyberattacks. The performance of the proposed scheme is evaluated using the IEEE 39-bus benchmark system. Our results show that the proposed scheme can accurately detect different forms of DFT, ST, and FM cyberattacks while maintaining the LCDR's protective characteristics. The proposed scheme is tested for real-time capability using an OPAL-RT simulator.

Line current differential relays (LCDRs) are high-speed relays progressively used to protect critical transmission lines. However, LCDRs are vulnerable to cyberattacks. Fault-masking attacks (FMAs) are stealthy cyberattacks performed by manipulating the remote measurements of the targeted LCDR to disguise faults on the protected line. Hence, they remain undetected by this LCDR. In this article, we propose a two-module framework to detect FMAs. The first module is a mismatch index (MI) developed from the protected transmission line’s equivalent physical model. The MI is triggered only if there is a significant mismatch in the LCDR’s local and remote measurements while the LCDR itself is untriggered, which indicates an FMA. After the MI is triggered, the second module, a neural network-based classifier, promptly confirms that the triggering event is a physical fault that lies on the line protected by the LCDR before declaring the occurrence of an FMA. The proposed framework is tested using the IEEE 39-bus benchmark system. Our simulation results confirm that the proposed framework can accurately detect FMAs on LCDRs and is not affected by normal system disturbances, variations, or measurement noise. Our experimental results using OPAL-RT’s real-time simulator confirm the proposed solution’s real-time performance capability.

Distributed Volt-Var Control (VVC) is a widely used control mode of smart inverters. However, necessary VVC curve parameters are remotely communicated to the smart inverter, which opens doors for cyberattacks. If VVC curves of an inverter are maliciously manipulated, the attacked inverter's reactive power injection will oscillate, causing undesirable voltage oscillations to manifest in the distribution system, which, in turn, threatens the system's stability. In contrast with previous works which proposed methods to mitigate the oscillations after they are already present in the system, this paper presents an intrusion detection method to detect malicious VVC curves once they are communicated to the inverter. The proposed method utilizes a Multi-Layer Perceptron (MLP) that is trained on features extracted from only the local measurements of the inverter. After a smart inverter is equipped with the proposed method, any communicated VVC curve will be verified by the MLP once received. If the curve is found to be malicious, it will be rejected, thus preventing unwanted oscillations beforehand. Otherwise, legitimate curves will be permitted. The performance of the proposed scheme is verified using the 9-bus Canadian urban benchmark distribution system simulated in PSCAD/EMTDC environment. Our results show that the proposed solution can accurately detect malicious VVC curves.

The potential of Bitcoin for money laundering and terrorist financing represents a significant challenge in law enforcement. In recent years, the use of privacy-improving CoinJoin transactions has grown significantly and helped criminal actors obfuscate Bitcoin money flows. In this study, we use unsupervised machine learning to analyze the complete Bitcoin user graph in order to identify suspicious actors potentially involved in illegal activities. In contrast to the existing studies, we introduce a novel set of features that we use to identify potential criminal activity more accurately. Furthermore, we apply our clustering algorithm to a CoinJoin-adjusted variant of the Bitcoin user graph, which enables us to analyze the network at a more detailed, user-centric level while still offering opportunities to address advanced privacy-enhancing techniques at a later stage. By comparing the results with our ground truth data set, we find that our improved clustering method is able to capture significantly more illicit activity within the most suspicious clusters. Finally, we find that users associated with illegal activities commonly have significant short paths to CoinJoin wallets and show tendencies toward outlier behavior. Our results have potential contributions to anti-money laundering efforts and combating the financing of terrorism and other illegal activities.

Cross-device federated learning (CDFL) systems enable fully decentralized training networks whereby each participating device can act as a model-owner and a model-producer. CDFL systems need to ensure fairness, trustworthiness, and high-quality model availability across all the participants in the underlying training networks. This article presents a blockchain-based framework, TrustFed, for CDFL systems to detect the model poisoning attacks, enable fair training settings, and maintain the participating devices' reputation. TrustFed provides fairness by detecting and removing the attackers from the training distributions. It uses blockchain smart contracts to maintain participating devices' reputations to compel the participants in bringing active and honest model contributions. We implemented the TrustFed using a Python-simulated federated learning framework, blockchain smart contracts, and statistical outlier detection techniques. We tested it over the large-scale industrial Internet of things dataset and multiple attack models. We found that TrustFed produces better results regarding multiple aspects compared with the conventional baseline approaches.

Bitcoin is a cryptocurrency and a decentralized semi-anonymous peer-to-peer payment system in which the transactions are verified by network nodes and recorded in a public massively replicated ledger called the blockchain. Bitcoin is currently considered as one of the most disruptive technologies. Bitcoin represents a paradox of opposing forces. On one hand, it is fundamentally social, allowing people to transact in a peer-to-peer manner to create and exchange value. On the other hand, Bitcoin's core design philosophy and user base contain strong anti-social elements and constraints, emphasizing anonymity, privacy, and subversion of traditional centralized financial systems. We believe that the success of Bitcoin, and the financial ecosystem built around it, will likely rely on achieving an optimal balance between these social and anti-social forces. To elucidate the role of these forces, we analyze the evolution of the entire Bitcoin transaction graph from its inception, and quantify the evolution of its key structural properties. We observe that despite its different nature, the Bitcoin transaction graph exhibits many universal dynamics typical of social networks. However, we also find that Bitcoin deviates in important ways due to anonymity-seeking behavioral patterns of its users. As a result, the network exhibits a two-orders-of-magnitude larger diameter, sparse treelike communities, and an overwhelming majority of transitional or intermediate accounts with incoming and outgoing edges but zero cumulative balances. These results illuminate the evolutionary dynamics of the most popular cryptocurrency, and provide us with initial understanding of social networks rooted in and driven by anti-social constraints.

Bitcoin is a cryptocurrency and a financial transaction network implemented using blockchain technology. Users in the Bitcoin network use pseudonymous Bitcoin addresses and conduct transactions with others without revealing their real identities. In order to further enhance their privacy and convenience, users often use a large number of different addresses. In this paper, we analyze different patterns of transactions occurring in the Bitcoin network in order to cluster addresses that share the same ownership. In order to evaluate the proposed clustering approach, Bitcoin addresses belonging to known entities are tagged and these are used in conjunction with the Gini impurity index to test the accuracy of the recovered identity-based clusters. The results show that our heuristic was able to detect relationships between Bitcoin addresses that were missed by the existing heuristics.

The omnipresence of Internet of Things (IoT) devices nowadays has resulted in a large amount of data transferred regularly to the cloud. This continuous data transfer degrades the application performance in terms of latency, bandwidth consumption, connectivity, security, privacy, user experiences, and energy efficiency. Fog computing brings cloud services closer to the IoT devices. In public settings, variety of handheld and IoT devices need to connect to public fog nodes in order to access localized compute, storage, and networking capabilities. Therefore, the reputations of publicly available fog nodes become critical. Maintaining a reputation score is one of the popular techniques to ensure trust for fog nodes. This paper introduces a blockchain-based solution to establish trust in public fog nodes that provides services for IoT devices in a decentralized manner. Our proposed solution exploits blockchain smart contracts to compute the reputation in a decentralized manner by capturing and analyzing its past interactions with IoT devices. The solution also penalizes IoT devices that may collude to provide dishonest reputation scores.

The papers in this special section focus on blockchain for the Industrial Internet of Things (IoT). Industrial IoT is reshaping various industrial sectors, such as manufacturing, logistics, transportation, healthcare, energy, and utilities. IIoT consists of various smart objects distributed throughout the whole industrial system to collect massive ambient data, which can be used to identify performance bottlenecks, troubleshoot faults, and detect malicious behaviors consequently enforcing effective control to the physical world. However, there are several challenges posed on IIoT before the formal adoption of IIoT across various industrial sectors. Among them, security and privacy preservation on IIoT data are the most crucial concerns. On the other hand, the blockchain technology is transforming industries by enabling anonymous and trustful transactions in decentralized and trustless environment. As a result, blockchains help to reduce system risks, mitigate financial fraud, and cut down operational cost. The convergence of IIoT and blockchains can potentially overcome the deficiencies of IIoT consequently resulting in the realization of IIoT in various industrial sectors. Both industry practitioners and academic researchers aim at realizing general, scalable and deployable blockchain-based IIoT platforms in various application domains while there are a number of challenges such as distributed consensus algorithms and data analytics with privacy-preservation in IIoT systems.

Smart grids equipped with bi-directional communication flow are expected to provide more sophisticated consumption monitoring and energy trading. However, the issues related to the security and privacy of consumption and trading data present serious challenges. In this paper we address the problem of providing transaction security in decentralized smart grid energy trading without reliance on trusted third parties. We have implemented a proof-of-concept for decentralized energy trading system using blockchain technology, multi-signatures, and anonymous encrypted messaging streams, enabling peers to anonymously negotiate energy prices and securely perform trading transactions. We conducted case studies to perform security analysis and performance evaluation within the context of the elicited security and privacy requirements.

This paper proposes a novel semiautomatic system domain data analysis method. The method is based on the iterative acquisition and analysis of a large body of bibliometric data, generation of domain taxonomies, and creation of domain models. The method was applied on a smart grid case study through collection and analysis of more than 6000 documents. We have found that our method produces domain models of comparable quality to the traditional manually produced domain models in a more cost-effective way.

Financial transaction networks are some of the largest networks in existence. A relatively new type of financial networks is the digital (crypto) currency network, e.g., Bitcoin. Namecoin is an alternative crypto currency, based on Bitcoin, with additional features such as DNS. Namecoin network has more than 2 million nodes and almost 17 million edges. The analysis of such a crypto currency network can help us model or predict the future growth of the transaction networks. In order to analyze the transaction network graph over time, we analyzed the Namecoin blockchain data in 7 six months intervals. Our findings suggest different user behavior and developing pattern compared to Bitcoin.

This paper presents a computational method that employs Natural Language Processing (NLP) and text mining techniques to support requirements engineers in extracting and modeling goals from textual documents. We developed a NLP-based goal elicitation approach within the context of KAOS goal-oriented requirements engineering method. The hierarchical relationships among goals are inferred by automatically building taxonomies from extracted goals. We use smart metering system as a case study to investigate the proposed approach. Smart metering system is an important subsystem of the next generation of power systems (smart grids). Goals are extracted by semantically parsing the grammar of goal-related phrases in abstracts of research publications. The results of this case study show that the developed approach is an effective way to model goals for complex systems, and in particular, for the research-intensive complex systems.

A modern and efficient information and communication technology (ICT) infrastructure is essential for managing the challenges in the complex urban systems development. The ICT infrastructure is a complex system consisting of many subsystems and interconnections, which makes the process of planning, designing, and maintaining a comprehensive ICT infrastructure expensive and difficult. Most approaches used for the ICT infrastructure modeling focus typically on a single ICT system, for example, a wireless network. This paper presents a systems modeling approach based on integrating different subsystems and their characteristics into a single model, applying system decomposition, establishing the logical relations between system components, and defining relevant key performance indicators. It is shown that this systems modeling approach facilitates holistic planning, design, and evaluation of the complex ICT infrastructure for a sustainable city. This is demonstrated in the form of a two-scenario Masdar city case study. The case study exhibits the practicality of the derived ICT model and the feasibility of the results.

Anyone who has taught object-oriented domain analysis or any other software process requiring concept identification has undoubtedly observed that some students just don't get it. Our evaluation of the work of over 740 University of Waterloo students on over 135 software requirements specifications during the last four years supports this same observation. The students' task was to specify a telephone exchange or a voice-over-IP telephone system and the related accounts management subsystem, based on models they developed using object-oriented analysis. A detailed comparative study of three much smaller specifications, all of an elevator system, suggests that object orientation is poorly suited to domain analysis, even of small-sized domains, and that the difficulties we have observed are independent both of the size of the system under specification and of the overall abilities of the students.