Abstract Blockchain technology has created a new software development context, with its own peculiarities, mainly due to the guarantees that the technology must satisfy, that is, immutability , distributability , and decentralization of data. Its rapid evolution over the last decade implied a lack of adequate verification tools, exposing developers and users to critical vulnerabilities and bugs. This paper clarifies the extent of block chain-oriented software (BoS), that goes well beyond smart contracts. Moreover, it provides an overview of the challenges related to software verification in the blockchain context, encompassing smart contracts, blockchain layers, cross-chain applications, and, more generally, BoS. This study aims to highlight the shortcomings of the state-of-art and of the state-of-practice of software verification in that context and identify, at the same time, new research directions.
Smart contracts (SCs) significance and popularity increased exponentially with the escalation of decentralised applications (dApps), which revolutionised programming paradigms where network controls rest within a central authority. Since SCs constitute the core of such applications, developing and deploying contracts without vulnerability issues become key to improve dApps robustness to external attacks. This paper introduces a dataset that combines smart contract metrics with vulnerability data identified using Slither, a leading static analysis tool proficient in detecting a wide spectrum of vulnerabilities. Our primary goal is to provide a resource for the community that supports exploratory analysis, such as investigating the relationship between contract metrics and vulnerability occurrences. Further, we discuss the potential of this dataset for the development and validation of predictive models aimed at identifying vulnerabilities, thereby contributing to the enhancement of smart contract security. Through this dataset, we invite researchers and practitioners to study the dynamics of smart contract vulnerabilities, fostering advancements in detection methods and ultimately, fortifying the resilience of smart contracts.
The contemporary era experiences an unprecedented dependence on data generated by individuals via an array of interconnected devices constituting the Internet of Things (IoT). The information amassed through IoT devices serves many objectives, including prescriptive analytics and predictive maintenance, preemptive healthcare measures, disaster mitigation, operational efficiency, and increased yield. In contrast, most applications or systems that rely on user-generated data to fulfill their business objectives face challenges in adhering to privacy protocols. Consequently, users are exposed to many privacy risks. Such infringements upon privacy provisions give rise to apprehensions regarding the authenticity of the processed data. Hence, this paper presents weaknesses and challenges in current practices and proposes “PrivySeC,” a distributed ledger technology (DLT) based framework for privacy-preserving and secure sharing of personally and non-personally identifiable information. The security analysis indicates that the proposed solution ensures data privacy by design and complies with most of the requirements mandated by various privacy regulations. Similarly, PrivySeC promises low transaction latency and provides high throughput. Although we have created a privacy-preserving solution for sharing smart farm data, it can be customized to meet the specific privacy requirements of individual applications.
Solidity compiler plays a key role in enabling the development of smart contract applications on Ethereum by governing the syntax of a domain-specific language called Solidity and performing compilation and optimization of Solidity code. The correctness of Solidity compiler is critical in fostering transparency, efficiency, and trust in industries reliant on smart contracts. However, like other software systems, Solidity compiler is prone to bugs, which may produce incorrect bytecodes on blockchain platforms, resulting in severe security concerns. As a domain-specific compiler for smart contracts, Solidity compiler differs from other compilers in many perspectives, posing unique challenges to detect its bugs. To understand the bugs in Solidity compiler and benefit future research, in this paper, we present the first systematic study on 533 Solidity compiler bugs. We carefully examined their characteristics (including symptoms, root causes, and distribution), and their triggering test cases. Our study leads to seven bug-revealing takeaways for Solidity compiler. Moreover, to study the limitations of Solidity compiler fuzzers and bring our findings into practical scenarios, we evaluate three Solidity compiler fuzzers on our constructed benchmark. The results show that these fuzzers are inefficient in detecting Solidity compiler bugs. The inefficiency arises from their failure to consider the interesting bug-inducing features, bug-related compilation flags, and test oracles.
Junaid Arshad, Muhammad Talha, Bilal Saleem, Zoha Shah · 6 authors
The increasing reliance on computer networks and blockchain technology has led to a growing concern for cybersecurity and privacy. The emergence of zero-day vulnerabilities and unexpected exploits has highlighted the need for innovative solutions to combat these threats. Bug bounty programs have gained popularity as a cost-effective way to crowdsource the task of identifying vulnerabilities, providing a secure and efficient means of enhancing cybersecurity. This paper provides a comprehensive survey of various free and paid bug bounty programs in the computer networks and blockchain industry, evaluating their effectiveness, impact, and credibility. The study explores the structure, incentives, and nature of vulnerabilities uncovered by these programs, as well as their unique value proposition. A comparative analysis is conducted to identify advantages and disadvantages, highlighting the strengths and weaknesses of each program. The paper also examines the role of ethical hackers in bug bounty programs and their contributions to strengthening cybersecurity and privacy. Finally, the study concludes with recommendations for addressing the challenges faced by bug bounty programs and suggests potential future directions to enhance their impact on computer networks and blockchain security.
Smart contracts, known for their immutable nature to ensure trust via automated enforcement, have evolved to require upgradeability due to unforeseen vulnerabilities and the need for feature enhancements post-deployment. This contradiction between immutability and the need for modifications has led to the development of upgradeable smart contracts. These contracts are immutable in principle yet upgradable by design, allowing updates without altering the underlying data or state, thus preserving the contract's intent while allowing improvements. This study aims to understand the application and implications of upgradeable smart contracts on the Ethereum blockchain. By introducing a dataset that catalogs the versions and evolutionary trajectories of smart contracts, the research explores key dimensions: the prevalence and adoption patterns of upgrade mechanisms, the likelihood and occurrences of contract upgrades, the nature of modifications post-upgrade, and their impact on user engagement and contract activity. Through empirical analysis, this study identifies upgradeable contracts and examines their upgrade history to uncover trends, preferences, and challenges associated with modifications. The evidence from analyzing over 44 million contracts shows that only 3% have upgradeable characteristics, with only 0.34% undergoing upgrades. This finding underscores a cautious approach by developers towards modifications, possibly due to the complexity of upgrade processes or a preference for maintaining stability. Furthermore, the study shows that upgrades are mainly aimed at feature enhancement and vulnerability mitigation, particularly when the contracts' source codes are accessible. However, the relationship between upgrades and user activity is complex, suggesting that additional factors significantly affect the use of smart contracts beyond their evolution.
Function-as-a-Service providers manage security devices that are shared among multiple tenants. It is undesirable to give them access to cleartext HTTP requests to perform tasks such as traffic inspection. The recent Zero-Knowledge Middlebox (ZKMB) can be used to enforce network policies on TLS traffic without revealing any information on the content to the policy verifier. In this paper, we describe a ZKMB implementation and a policy designed to check whether the HTTPS function invocations by the clients follow a legitimate pattern. We also present and compare two strategies to distribute allowed patterns, introducing a Moving-Target Defense approach for the function URI randomization, which shows a good tradeoff between detection effectiveness and confidentiality. Performance assessment in our prototype implementation shows that the ZK algorithms are not yet suitable for real-time execution, but current research interest in this technology is expected to narrow this gap.
H S Byun, Jueun Kim, Yun-Seok Jeong, Byoungjin Seok · 6 authors
Currently, the monetary value of cryptocurrencies is extremely high, leading to frequent theft attempts. Cyberattacks targeting cryptocurrency wallets and the scale of these attacks are also increasing annually. However, many studies focus on large-scale exchanges, leading to a lack of research on cryptocurrency wallet security. Nevertheless, the threat to individual wallets is real and can lead to severe consequences for individuals. In this paper, we analyze the security of the open-source cryptocurrency wallets Sparrow, Etherwall, and Bither against brute-force attacks, a fundamental threat in password-based systems. As cryptocurrency wallets use passwords to manage users’ private keys, we analyzed the private key management mechanism and implemented a password verification oracle. We used this oracle for brute-force attacks. We identified the private key management mechanism by conducting a code-level investigation and evaluated the three wallets’ security through practical experimentation. The experiment results revealed that the wallets’ security, which depends on passwords, could be diminished due to the password input space and the configuration of password length settings. We propose a general methodology for analyzing the security of desktop cryptocurrency wallets against brute-force attacks and provide practical guidelines for designing secure wallets. By using the analysis methods suggested in this paper, one can evaluate the security of wallets.
Solana gained considerable attention as one of the most popular blockchain platforms for deploying decentralized applications. Compared to Ethereum, however, we observe a lack of research on how Solana smart contract developers handle security, what challenges they encounter, and how this affects the overall security of the ecosystem. To address this, we conducted the first comprehensive study on the Solana platform consisting of a 90-minute Solana smart contract code review task with 35 participants followed by interviews with a subset of seven participants. Our study shows, quite alarmingly, that none of the participants could detect all important security vulnerabilities in a code review task and that 83% of the participants are likely to release vulnerable smart contracts. Our study also sheds light on the root causes of developers' challenges with Solana smart contract development, suggesting the need for better security guidance and resources. In spite of these challenges, our automated analysis on currently deployed Solana smart contracts surprisingly suggests that the prevalence of vulnerabilities - especially those pointed out as the most challenging in our developer study - is below 0.3%. We explore the causes of this counter-intuitive resilience and show that frameworks, such as Anchor, are aiding Solana developers in deploying secure contracts.
Lukas Ingmarsson, Karl Duckert Karlsson, Niklas Carlsson
Bitcoin and the Dark Web present an interesting synergy that enables both legitimate anonymity and illicit activities, making it an important landscape to understand, especially as the Dark Web, with its hidden services, relies heavily on Bitcoin as a pseudonymous currency for transactions. However, a lack of scalable tools and timely datasets has limited systematic analysis of this ecosystem. To address this gap, we introduce Venom, a scalable framework for mapping Bitcoin activity on the Dark Web. Venom integrates multithreaded crawling, data extraction, and dataset generation, resulting in a comprehensive resource that allows us to easily collect snapshots of over 177,000 onion sites in roughly 24 hours. With the paper, we share both the tool and an example snapshot containing both per-site metadata and Bitcoin transaction data. Preliminary analysis reveals concentrated activity among key players and widespread content mirroring, offering new insights into the Dark Web's economic structure. Venom provides a critical resource for advancing research and monitoring in this domain.
The rapid evolution of ransomware attacks necessitates robust and scalable detection mechanisms to safeguard digital assets. This study leverages the Bitcoin Ransomware Dataset, comprising 2,916,697 transactions, to evaluate the effectiveness of the Random Forest algorithm in classifying ransomware-related activities. Through comprehensive preprocessing, including feature encoding and standardization, and exploratory data analysis (EDA), the dataset is prepared for modeling. The Random Forest model achieves an overall accuracy of 99%, demonstrating exceptional performance in identifying the majority class. However, challenges persist in classifying minority classes, highlighting the impact of class imbalance. Feature importance analysis reveals that attributes such as income, weight, and length play pivotal roles in the classification process. The study underscores the potential of Random Forest for ransomware detection while emphasizing the need for advanced techniques to address class imbalance and improve minority class performance.
As the field of Web3 continues its rapid expansion, the security of Web3 authentication, often the gateway to various Web3 applications, becomes increasingly crucial. Despite its widespread use as a login method by numerous Web3 applications, the security risks of Web3 authentication have not received much attention. This paper investigates the vulnerabilities in the Web3 authentication process and proposes a new type of attack, dubbed blind message attacks. In blind message attacks, attackers trick users into blindly signing messages from target applications by exploiting users' inability to verify the source of messages, thereby achieving unauthorized access to the target application. We have developed Web3AuthChecker, a dynamic detection tool that interacts with Web3 authentication-related APIs to identify vulnerabilities. Our evaluation of real-world Web3 applications shows that a staggering 75.8% (22/29) of Web3 authentication deployments are at risk of blind message attacks. In response to this alarming situation, we implemented Web3AuthGuard on the open-source wallet MetaMask to alert users of potential attacks. Our evaluation results show that Web3AuthGuard can successfully raise alerts in 80% of the tested Web3 authentications. We have responsibly reported our findings to vulnerable websites and have been assigned two CVE IDs.
The openness and transparency of Ethereum transaction data make it easy to be exploited by any entities, executing malicious attacks. The sandwich attack manipulates the Automated Market Maker (AMM) mechanism, profiting from manipulating the market price through front or after-running transactions. To identify and prevent sandwich attacks, we propose a cascade classification framework GasTrace. GasTrace analyzes various transaction features to detect malicious accounts, notably through the analysis and modeling of Gas features. In the initial classification, we utilize the Support Vector Machine (SVM) with the Radial Basis Function (RBF) kernel to generate the predicted probabilities of accounts, further constructing a detailed transaction network. Subsequently, the behavior features are captured by the Graph Attention Network (GAT) technique in the second classification. Through cascade classification, GasTrace can analyze and classify the sandwich attacks. Our experimental results demonstrate that GasTrace achieves a remarkable detection and generation capability, performing an accuracy of 96.73% and an F1 score of 95.71% for identifying sandwich attack accounts.
Wenrui Zuo, Raúl J. Mondragón, Aravindh Raman, Gareth Tyson
There have been numerous recent attempts to “decentralize” social media platforms, loosely referred to as Web3. Such ideas, often underpinned by blockchain solutions, offer decentralized equivalents of well-known services (e.g., forums, social networks, video sharing sites, microblogs). One particularly challenging function to implement in such a design is content moderation, due to the lack of central control. Consequently, they often rely on user-controlled moderation, whereby each user must create their own personal block list to filter out content they do not wish to see. This paper presents a first study of user-controlled moderation on one exemplar Web3 social microblogging platform called memo.cash. Based on a dataset covering 391K posts, we study the factors that lead users to “mute” each other. We find that the most crucial factor is the platform action count, rather than the presence of things like hate speech. We also show that the followership network plays a pivotal role in determining their visibility on the platform, further influencing their muting behavior. This leads us to design tooling to automate the muting process on a per-user basis. We model this as a recommendation problem, and experiment with a number of state-of-the-art recommender engines. We show that our system can generate effective personalized mute lists for users.
Distributed ledger technology such as blockchain is considered essential for supporting large numbers of micro-transactions in the Machine Economy, which is envisioned to involve billions of connected heterogeneous and decentralized cyber-physical systems. This stresses the need for performance and scalability of distributed ledger technologies. Addressing this, sharding techniques that divide the blockchain network into multiple committees are a common approach to improve scalability. However, with current sharding approaches, costly cross-shard verification is needed to prevent double-spending. This article proposes a novel and more scalable distributed ledger method named ScaleGraph that implements dynamic sharding by using routing and logical proximity concepts from distributed hash tables. ScaleGraph addresses cybersecurity in terms of integrity and availability to support frequent micro-transactions between autonomous devices. Benefits of ScaleGraph include a total storage space complexity of \(O(t)\) , where \( t \) is the global number of transactions (assuming a constant replication degree). This space is sharded over \( N \) nodes so that each node needs \(O(t/N)\) storage in expectation, which provides a high level of concurrency and data localization as compared to other delegated consensus proposals. ScaleGraph allows for a dynamic grouping of validators that are selected based on a distance metric. We analyze the consensus requirements in such a dynamic setting and show that a synchronous consensus protocol allows shards to be smaller than an asynchronous one, and likely yields better performance. Moreover, we provide an experimental analysis of security aspects regarding the required size of the consensus groups with ScaleGraph. Our analysis shows that dynamic sharding based on proximity concepts brings attractive scalability properties in general, especially when the fraction of corrupt nodes is small.
Abstract This paper introduces a novel integrated hybrid malware attack detection algorithm, focusing on enhancing cybersecurity within blockchain systems by addressing the prevalent challenges of Byzantine fault tolerance, Reentrancy, and DDOS attacks. The significance of this research lies in its contribution to safeguarding blockchain technology, a cornerstone for secure, decentralized digital transactions, against sophisticated malware threats. Current cybersecurity solutions frequently fall short of offering a complete defense mechanism, making it difficult to effectively combat a variety of dynamic malware attacks at the same time. Thus, the main objective of this research is to provide a hybrid framework that combines DDOS attack prevention, reentrancy attack detection, and Byzantine fault tolerance detection into a single, cohesive architecture. The proposed hybrid framework encompasses a detailed algorithmic approach integrating SHA-256 and DSA to analyze the aforementioned three malware attacks. A hybrid model combining these algorithms, implemented in one block, has been developed to mitigate malicious activity. These measures aim to improve computational complexity and expedite execution within the network of nodes. To test the efficacy of the proposed framework, the approach is tested on the NSL-KDD dataset to analyze the malicious activities. The performance analysis of the proposed frameworks presents a recall and F1 score of 73 and .68 respectively. Furthermore, for efficient mitigation, the time and space complexity analysis is performed on proposed algorithms for attack analysis, which resulted in a combination of constant and linear time complexity operations. The findings reveal that the proposed algorithm successfully identifies and mitigates the targeted malware attacks and maintains optimal performance in terms of time and space complexity. Specifically, the algorithm showcases linear and constant time complexities across different attack vectors, ensuring swift and scalable defense capabilities. This research’s contribution to the cybersecurity field is significant, offering a robust, scalable solution that enhances the resilience of blockchain networks against a broad spectrum of malware attacks.
Ethereum has become one of the primary global platforms for cryptocurrency, playing an important role in promoting the diversification of the financial ecosystem. However, the relative lag in regulation has led to a proliferation of malicious activities in Ethereum, posing a serious threat to fund security. Existing regulatory methods usually detect malicious accounts through feature engineering or large-scale transaction graph mining. However, due to the immense scale of transaction data and malicious attacks, these methods suffer from inefficiency and low robustness during data processing and anomaly detection. In this regard, we propose an Ethereum Transaction Graph Compression method named TGC4Eth, which assists malicious account detection by lightweighting both features and topology of the transaction graph. At the feature level, we select transaction features based on their low importance to improve the robustness of the subsequent detection models against feature evasion attacks; at the topology level, we employ focusing and coarsening processes to compress the structure of the transaction graph, thereby improving both data processing and inference efficiency of detection models. Extensive experiments demonstrate that TGC4Eth significantly improves the computational efficiency of existing detection models while preserving the connectivity of the transaction graph. Furthermore, TGC4Eth enables existing detection models to maintain stable performance and exhibit high robustness against feature evasion attacks.
Love Allen Chijioke Ahakonye, Cosmas Ifeanyi Nwakanma, Dong‐Seong Kim
This paper surveys the implementation of blockchain technology in cybersecurity in Internet of Things (IoT) networks, presenting a comprehensive framework that integrates blockchain technology with intrusion detection systems (IDS) to enhance IDS performance. This paper reviews articles from various domains, including AI, blockchain, IDS, IoT, and Industrial IoT (IIoT), to identify emerging trends and challenges in this field. An analysis of various approaches incorporating AI and blockchain demonstrates the potentiality of integrating AI and blockchain to transform IDS. This paper's structure establishes the foundation for further investigation and provides a blueprint for the development of IDS that is accessible, scalable, transparent, immutable, and decentralized. A demonstration from case studies integrating AI and blockchain shows the viability of combining the duo to enhance performance. Despite the challenges posed by resource constraints and privacy concerns, it is notable that blockchain is the key to securing IoT networks and that continued innovation in this area is necessary. Further research into lightweight cryptography, efficient consensus mechanisms, and privacy-preserving techniques is needed to realize all of the potential of blockchain-powered cybersecurity in IoT.
Decentralized finance (DeFi) protocols are crypto projects developed on the blockchain to manage digital assets. Attacks on DeFi have been frequent and have resulted in losses exceeding \$77 billion. However, detection methods for malicious DeFi events are still lacking. In this paper, we propose DeFiTail, the first framework that utilizes deep learning to detect access control and flash loan exploits that may occur on DeFi. Since the DeFi protocol events involve invocations with multi-account transactions, which requires execution path unification with different contracts. Moreover, to mitigate the impact of mistakes in Control Flow Graph (CFG) connections, we validate the data path by employing the symbolic execution stack. Furthermore, we feed the data paths through our model to achieve the inspection of DeFi protocols. Experimental results indicate that DeFiTail achieves the highest accuracy, with 98.39% in access control and 97.43% in flash loan exploits. DeFiTail also demonstrates an enhanced capability to detect malicious contracts, identifying 86.67% accuracy from the CVE dataset.
In public blockchains, leaking secret keys can cause the permanent loss of crypto assets. It is imperative to understand the illicit activities on blockchains related to leaked keys. This paper presents the first measurement study that uncovers, quantifies, and characterizes the actual misuses of the leaked keys from top websites on the Internet to withdraw assets on Ethereum. By finding key-leaking web pages and joining them with transactions, the study reveals 7.29*10^6/0.59*10^6 USD worth of assets on Ethereum mainnet/Binance Smart Chain (BSC) are withdrawn from 1421/1514 leaked secret keys. Mitigations are proposed to avoid the financial loss caused by leaked keys.
Open access
User Authentication and Security Systems
Advanced Malware Detection Techniques
Advanced Steganography and Watermarking Techniques
Tanusree Sharma, Vivek Nair, Henry Wang, Yang Wang · 5 authors
Key management has long remained a difficult unsolved problem in the field of usable security. While password-based key derivation functions (PBKDFs) are widely used to solve this problem in centralized applications, their low entropy and lack of a recovery mechanism make them unsuitable for use in decentralized contexts. The multi-factor key derivation function (MFKDF) is a recently proposed cryptographic primitive that aims to address these deficiencies by incorporating commonly used authentication factors into the key derivation process. In this paper, we implement an MFKDF-based Ethereum wallet and perform a user study with 27 participants to directly compare its usability against traditional cryptocurrency wallet architectures. Our results show that MFKDF-based applications outperform conventional key management approaches on both subjective and objective metrics, with a 37% higher average SUS score (p < 0.0001) and 71% faster task completion times (p < 0.0001) for the MFKDF-based wallet.
The Internet of Things (IoT) refers to a complex network comprising interconnected devices that transmit their data via the Internet. Due to their open environment, limited computation power, and absence of built-in security, IoT environments are susceptible to various cyberattacks. Denial of service (DDoS) attacks are among the most destructive types of threats. The Multi-vector DDoS attack is a contemporary and formidable form of DDoS wherein the attacker employs a collection of compromised IoT devices as zombies to initiate numerous DDoS attacks against a target server. A Blockchain-based Operational Threat Intelligence framework, OTI-IoT, is proposed in this article to counter multi-vector DDoS attacks in IoT networks. A “Prevent-then-Detect” methodology was utilized to deploy the OTI-IoT framework in two distinct stages. During Phase 1, the consortium Blockchain network validators employ the IPS module, composed of a smart contract for attack prevention and access control, and Proof of Voting consensus, to thwart attacks. Validators are outfitted with deep learning-based IDS instances to detect multi-vector DDoS attacks during Phase 2. Alert messages are generated by the IDS module’s alert generation and propagation smart contract in response to identifying malicious IoT sources. The feedback loop from the IDS module to the IPS module prevents incoming traffic from malicious sources. The proposed OTI framework capabilities are realized as an outcome of combining and storing the outcomes of the IDS and IPS modules on the consortium Blockchain. Each validator maintains a shared ledger containing information regarding threat sources to ensure robust security, transparency, and integrity. The operational execution of OTI-IoT occurs on an individual Ethereum Blockchain. The empirical findings indicate that our proposed framework is most suitable for real-time applications due to its ability to lower attack detection time, decreased block validation time, and higher attack prevention rate.
Abstract: Eclipse attacks pose a serious threat to blockchain networks. Research has proven that Ethereum is more vulnerable to the Eclipse attack than the Bitcoin peer-to-peer network. Therefore, related research on Eclipse attacks on Ethereum is of great value. This paper proposes an improved XGBoost algorithm based on Bagging. It simulates a variety of random situations through the Bagging method, introduces randomness, reduces the risk of high errors, reduces the variance of the XGBoost model output, and improves the generalization ability of the model. It further enhances the model performance on binary classification problems and achieves efficient identification of Eclipse attack traffic and normal traffic.
Krishnendu Chatterjee, A. Ebrahimzadeh, Mehrdad Karrabi, Krzysztof Pietrzak · 6 authors
We study selfish mining attacks in longest-chain blockchains like Bitcoin, but where the proof of work is replaced with efficient proof systems -- like proofs of stake or proofs of space -- and consider the problem of computing an optimal selfish mining attack which maximizes expected relative revenue of the adversary, thus minimizing the chain quality. To this end, we propose a novel selfish mining attack that aims to maximize this objective and formally model the attack as a Markov decision process (MDP). We then present a formal analysis procedure which computes an $ε$-tight lower bound on the optimal expected relative revenue in the MDP and a strategy that achieves this $ε$-tight lower bound, where $ε>0$ may be any specified precision. Our analysis is fully automated and provides formal guarantees on the correctness. We evaluate our selfish mining attack and observe that it achieves superior expected relative revenue compared to two considered baselines. In concurrent work [Sarenche FC'24] does an automated analysis on selfish mining in predictable longest-chain blockchains based on efficient proof systems. Predictable means the randomness for the challenges is fixed for many blocks (as used e.g., in Ouroboros), while we consider unpredictable (Bitcoin-like) chains where the challenge is derived from the previous block.