Ethereum smart contracts hold tens of billions of USD in DeFi and NFTs, yet comprehensive security analysis remains difficult due to unverified code, proxy-based architectures, and the reliance on manual inspection of complex execution traces. Existing approaches fall into two main categories: anomaly transaction detection, which flags suspicious transactions but offers limited insight into specific attack strategies hidden in execution traces inside transactions, and code vulnerability detection, which cannot analyze unverified contracts and struggles to show how identified flaws are exploited in real incidents. As a result, analysts must still manually align transaction traces with contract code to reconstruct attack scenarios and conduct forensics. To address this gap, TraceLLM is proposed as a framework that leverages LLMs to integrate execution trace-level detection with decompiled contract code. We introduce a new anomaly execution path identification algorithm and an LLM-refined decompile tool to identify vulnerable functions and provide explicit attack paths to LLM. TraceLLM establishes the first benchmark for joint trace and contract code-driven security analysis. For comparison, proxy baselines are created by jointly transmitting the results of three representative code analysis along with raw traces to LLM. TraceLLM identifies attacker and victim addresses with 85.19\% precision and produces automated reports with 70.37\% factual precision across 27 cases with ground truth expert reports, achieving 25.93\% higher accuracy than the best baseline. Moreover, across 148 real-world Ethereum incidents, TraceLLM automatically generates reports with 66.22\% expert-verified accuracy, demonstrating strong generalizability.
Blockchain address poisoning is an emerging phishing attack that crafts "similar-looking" transfer records in the victim's transaction history, which aims to deceive victims and lure them into mistakenly transferring funds to the attacker. Recent works have shown that millions of Ethereum users were targeted and lost over 100 million US dollars. Ethereum crypto wallets, serving users in browsing transaction history and initiating transactions to transfer funds, play a central role in deploying countermeasures to mitigate the address poisoning attack. However, whether they have done so remains an open question. To fill the research void, in this paper, we design experiments to simulate address poisoning attacks and systematically evaluate the usability and security of 53 popular Ethereum crypto wallets. Our evaluation shows that there exist communication failures between 12 wallets and their transaction activity provider, which renders them unable to download the users' transaction history. Besides, our evaluation also shows that 16 wallets pose a high risk to their users due to displaying fake token phishing transfers. Moreover, our further analysis suggests that most wallets rely on transaction activity providers to filter out phishing transfers. However, their phishing detection capability varies. Finally, we found that only three wallets throw an explicit warning message when users attempt to transfer to the phishing address, implying a significant gap within the broader Ethereum crypto wallet community in protecting users from address poisoning attacks. Overall, our work shows that more efforts are needed by the Ethereum crypto wallet developer community to achieve the highest usability and security standard. Our bug reports have been acknowledged by the developer community, who are currently developing mitigation solutions.
Sohel Rana, Rizal Mohd Nor, Mohammad Enayet Hossain, Md Amiruzzaman
The increasing adoption of cryptocurrency has underscored the critical need for robust security measures to protect digital assets stored in cryptocurrency wallets. Traditional security approaches have often proven inadequate in addressing the rapidly evolving threats in the digital landscape. In response, cloud-based security solutions have emerged as a promising method to enhance wallet protection, leveraging scalability, flexibility, and advanced security features. This study investigates the security challenges faced by cryptocurrency wallets and explores the potential of cloud-based solutions, focusing on multi-factor authentication, encryption protocols, real-time monitoring, and secure backup and recovery. The research assesses the effectiveness of these solutions in mitigating risks such as unauthorized access, data breaches, and digital asset theft. Findings reveal that cloud-based security solutions significantly improve protection by offering scalable, adaptable frameworks. However, challenges remain, including privacy concerns, regulatory compliance, and the cost of implementation. The research introduces a cost-efficient approach that integrates cloud-based technologies to optimize the total cost of ownership while maintaining robust security. This study also discusses the regulatory and privacy implications of cloud security in cryptocurrency ecosystems. In conclusion, this research provides novel insights into the integration of cloud-based security solutions, offering a comprehensive framework for safeguarding digital assets in cryptocurrency wallets. It contributes to the growing body of knowledge on the feasibility and impact of cloud technologies in enhancing the security of cryptocurrency systems.
Secure Digital Evidence Management Systems (DEMSs) ae crucial for law enforcement agencies, because traditional systems are prone to tampering and unauthorised access. Blockchain technology, particularly private blockchains, offers a solution by providing a centralised and tamper-proof system. This study proposes a private blockchain using Proof of Work (PoW) to securely manage digital evidence. Miners are assigned specific nonce ranges to accelerate the mining process, called collaborative mining, to enhance the scalability challenges in DEMSs. Transaction data includes digital evidence to generate a Non-Fungible Token (NFT). Miners use NFTs to solve the puzzle according to the assigned difficulty level d, so as to generate a hash using SHA-256 and add it to the ledger. Users can verify the integrity and authenticity of records by re-generating the hash and comparing it with the one stored in the ledger. Our results show that the data was verified with 100% precision. The mining time was 2.5 s, and the nonce iterations were as high as 80×103 for d=5. This approach improves the scalability and integrity of digital evidence management by reducing the overall mining time.
This paper presents the design, development, and thorough evaluation of a novel network security prototype that integrates Artificial Intelligence (AI) and blockchain technology to significantly enhance cyber security. As AI becomes increasingly embedded in cybersecurity solutions, ensuring the provenance, accountability, and integrity of AI-generated decisions has emerged as a critical challenge. Without reliable logging mechanisms, AI models remain vulnerable to adversarial manipulation and pose significant risks to critical security infrastructure. To address this, our research combines a state-of-the-art Convolutional Neural Network (CNN)-based threat detection module with a permissioned Ethereum-compatible blockchain. A custom-designed Solidity smart contract ensures secure, structured storage of comprehensive AI model metadata, while interactions with the blockchain are seamlessly managed through a lightweight Flask-based REST API. Each recorded transaction generates a unique cryptographic fingerprint, providing robust evidence for audits and forensic analyses. We evaluated the system's effectiveness through rigorous experimentation on a controlled test network, confirming immutability, traceability, and verifiable integrity of all logged metadata entries. Results demonstrated significant improvements in anomaly detection accuracy, reduced false-positive rates, and ensured real-time responsiveness essential for effective intrusion prevention. Despite controlled-environment limitations, such as transaction latency and blockchain-related operational costs, our prototype successfully establishes proof-of-concept for leveraging blockchain as an immutable audit trail for AI-driven cybersecurity systems. Future research directions include integrating advanced scaling techniques, such as layer 2 solutions, and extending the blockchain logging capabilities to cover the entire AI model lifecycle, including detailed training logs and comprehensive version histories. This work provides foundational contributions towards building trusted, auditable, and transparent AI solutions in regulated cyber security domains.
Victoria L. Lemieux, Rosa Gil, Faith Molosiwa, Qizheng Zhou · 8 authors
As archives turn to artificial intelligence to manage growing volumes of digital records, privacy risks inherent in current AI data practices raise critical concerns about data sovereignty and ethical accountability. This paper explores how privacy-enhancing technologies (PETs) and Web3 architectures can support archives to preserve control over sensitive content while still being able to make it available for access by researchers. We present Clio-X, a decentralized, privacy-first Web3 digital solution designed to embed PETs into archival workflows and support AI-enabled reference and access. Drawing on a user evaluation of a medium-fidelity prototype, the study reveals both interest in the potential of the solution and significant barriers to adoption related to trust, system opacity, economic concerns, and governance. Using Rogers' Diffusion of Innovation theory, we analyze the sociotechnical dimensions of these barriers and propose a path forward centered on participatory design and decentralized governance through a Clio-X Decentralized Autonomous Organization. By integrating technical safeguards with community-based oversight, Clio-X offers a novel model to ethically deploy AI in cultural heritage contexts.
The escalating complexity and frequency of malware attacks pose a significant challenge to conventional cybersecurity frameworks, particularly in scenarios demanding high data privacy and cross-organizational threat intelligence sharing. Traditional centralized machine learning models for malware detection often rely on aggregating data in a central server, thereby increasing the risk of data breaches and limiting the deployment of models in privacy-sensitive environments such as healthcare, finance, and critical infrastructure. To address these limitations, this study explores an integrated approach that combines Federated Learning (FL) with Explainable Artificial Intelligence (XAI) for enhancing malware detection while preserving user privacy and system confidentiality. Federated learning enables the collaborative training of robust malware classifiers across multiple decentralized nodes without sharing raw data, thus maintaining local data sovereignty and complying with data protection regulations. The proposed framework incorporates deep learning architectures such as convolutional neural networks (CNNs) trained in a federated environment using feature vectors extracted from malicious binaries and behavior logs. To ensure transparency and trust in model predictions, explainable AI techniques specifically SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are integrated, providing actionable insights into the model’s decision-making process. This study also presents a comprehensive evaluation using a benchmark malware dataset distributed across simulated client environments, measuring detection accuracy, communication overhead, privacy leakage, and interpretability performance. Results demonstrate that the FL-XAI approach achieves detection rates comparable to centralized models while ensuring data confidentiality and interpretability. The research contributes to the evolving field of privacy-preserving threat intelligence by offering a scalable and explainable framework suitable for real-time cybersecurity applications.
The fact that blockchain technology is decentralized, transparent, and immutable is transforming the face of such industries as finance, healthcare, and logistics. It is nonetheless, difficult in regulatory compliance, data privacy, and law enforcement, specifically blockchain forensics. Blockchain forensics is an activity of tracking transactions and members of illegal organizations like money laundering and cybercrime. Whereas the traceability of the blockchain technology with the transparency it possesses raises no more concerns on the legal issues, the pseudonymity of its participants, on the other hand, makes it quite difficult to identify them, respectively, creating issues within the scope of the data protection, as well as financial regulations. The paper will touch on the practice today of forensics, the regulation and morality of the balance that is there between privacy and criminal investigation. It ends with suggestions of a joint effort in creation of efficient legal frameworks to govern the same, and promotion of innovation.
Abhishek Bhattarai, Abdulhadi Sahin, Maryna Veksler, Ahmet Kurt · 7 authors
As cryptocurrencies have become increasingly used as an alternative to regular cash and credit card payments, the wallet solutions/apps that facilitate their use have also become increasingly popular. This has also intensified the involvement of these crypto wallet apps in criminal activities such as ransom requests, money laundering, and transactions on dark markets. From a digital forensics point of view, it is crucial to have tools and reliable approaches to detect these wallets on devices and extract their artifacts quickly with greater efficiency. However, with current research and trends, forensic investigators still need to manually extract these file artifacts, which delays the time-sensitive investigation findings. As mobile devices increasingly facilitate cryptocurrency transactions, there emerges a critical gap and need for automated evidence extraction to detect crucial artifacts preventing illicit activities. Therefore, in this paper, we present a comprehensive framework that incorporates various machine learning (ML), image processing, and natural language processing (NLP) approaches to enable fast and automated extraction/triage of crypto-related artifacts from Android and iOS devices. Specifically, our method can automatically detect which crypto wallet exists on the device, their artifacts (i.e., database/log files), along with the crypto-related images, web browsing data, and SMS conversations. For each type of data, we offer a specific ML technique, such as Support Vector Machine, Logistic Regression, and Neural Networks, to detect and classify these files. Our evaluation results show very high accuracy compared to alternative tools: our wallet classification model achieves 91% recall, crypto-related image classification achieves 75% accuracy, browsing data achieves 100% accuracy, and the SMS message model achieves 85% accuracy.
Morena Barboni, Guglielmo De Angelis, Andrea Morichetta, Andrea Polini
Blockchain technology is increasingly adopted in scenarios requiring trust and data integrity. On the Ethereum blockchain, the proxy pattern has become increasingly popular because it allows smart contract code to evolve while preserving stored data. However, a key challenge remains ensuring that such upgrades do not introduce breaking changes or cause disruptions to other contracts and off-chain systems. In this article, we introduce Catana , a framework that leverages historical transactions for Capture-Replay testing of proxy-based Upgradeable Smart Contracts (USCs). Catana assesses the potential impact of an upgrade by comparing the outcomes of replayed transactions with those from the previous version deployed on the main network. Additionally, it extracts and decodes contract state variables, providing deeper insights into how code changes affect the contract state, and helping developers mitigate issues before deployment. Experiments demonstrate that analyzing storage data accounts for the majority (about 86.5%) of detected disruptive upgrades. We also evaluate different policies for building replay test suites from historical transactions. Results identify a strategy that maximizes effectiveness while requiring a small number of replay test executions. Even a test suite containing just one transaction per each invoked method achieved good effectiveness (about 60%) in detecting disruptive upgrades.
Jesus Gama-Rodnguez, Ekam Puri Nieto, Juan Francisco Martínez Gil, Agustín Marín Frutos
Exploration in the crucial role of cyber threat intelligence (CTI) sharing and lifecycle security in IoT ecosystems. It examines how the ERATOSTHENES project leverages distributed ledger technology (DLT) and an inter-ledger approach to facilitate secure and privacy-preserving CTI exchange across different domains. The chapter also discusses the use of Manufacturer Usage Description (MUD) files, including the proposed Threat MUD extension, to manage security configurations and mitigation actions throughout the device lifecycle. Additionally, it highlights the integration of these components to achieve a system that dynamically responds to cybersecurity incidents, ensuring the ongoing protection of devices and domains.
Blockchain technology offers a powerful solution for securing and managing legal and academic documents by providing decentralization, transparency, and immutability. This research presents a blockchain-based framework for tamper-proof legal document and credential management aimed at improving the reliability and efficiency of traditional verification systems. In the proposed architecture, applicants submit their credentials, which are authenticated by issuing institutions and stored in the InterPlanetary File System (IPFS) for decentralized file management, while only cryptographic hashes are recorded on the blockchain to ensure integrity, reduce storage costs, and improve scalability. The system supports multiple consensus mechanisms, including Proof of Work, Proof of Stake, and Practical Byzantine Fault Tolerance, and experimental results demonstrate that Proof of Stake achieves the best balance between security and transaction efficiency. A prototype implementation achieved a transaction throughput of up to 1,000 transactions per second with an average confirmation time of 5 seconds, significantly reducing verification time and manual processing compared to conventional methods. Comparative analysis confirms that the proposed solution enhances security, minimizes fraud, and improves cost-effectiveness. The framework provides a strong foundation for future extensions such as cross-chain interoperability, AI-driven fraud detection, and mobile-based verification platforms
Since Diffie and Hellman's pioneering work on asymmetric cryptography in 1976, digital signature technology has evolved through three phases—theoretical foundation, standardization, and diversified innovation—emerging as a cornerstone of trust in digital societies. Theoretically, foundational frameworks were established by RSA, DSA, and Schnorr algorithms. Standardization efforts, including NIST DSS, ISO/IEC series, and national systems (e.g., China's SM2/SM9, Russia's GOST), fostered a multipolar ecosystem. Extended-attribution technologies (blind, group, and ring signatures) addressed privacy and scenario-specific demands. Current challenges, such as quantum computing threats and privacy-regulation trade-offs, drive advancements in post-quantum cryptography (lattice-based signatures, hash-based XMSS) and privacy-enhancing mechanisms (verifiably encrypted signatures, homomorphic signatures), guided by ISO/IEC redactable standards and NIST's post-quantum initiative. Moving forward, digital signatures will deepen capabilities in provable security, quantum resistance, and adaptive policy control, underpinning trust architectures for emerging ecosystems like Web3 and the metaverse.
Open access
Cryptography and Data Security
Digital and Cyber Forensics
Advanced Steganography and Watermarking Techniques
Verification of the integrity of deep learning inference is crucial for understanding whether a model is being applied correctly. However, such verification typically requires access to model weights and (potentially sensitive or private) training data. So-called Zero-knowledge Succinct Non-Interactive Arguments of Knowledge (ZK-SNARKs) would appear to provide the capability to verify model inference without access to such sensitive data. However, applying ZK-SNARKs to modern neural networks, such as transformers and large vision models, introduces significant computational overhead. We present TeleSparse, a ZK-friendly post-processing mechanisms to produce practical solutions to this problem. TeleSparse tackles two fundamental challenges inherent in applying ZK-SNARKs to modern neural networks: (1) Reducing circuit constraints: Over-parameterized models result in numerous constraints for ZK-SNARK verification, driving up memory and proof generation costs. We address this by applying sparsification to neural network models, enhancing proof efficiency without compromising accuracy or security. (2) Minimizing the size of lookup tables required for non-linear functions, by optimizing activation ranges through neural teleportation, a novel adaptation for narrowing activation functions' range. TeleSparse reduces prover memory usage by 67% and proof generation time by 46% on the same model, with an accuracy trade-off of approximately 1%. We implement our framework using the Halo2 proving system and demonstrate its effectiveness across multiple architectures (Vision-transformer, ResNet, MobileNet) and datasets (ImageNet,CIFAR-10,CIFAR-100). This work opens new directions for ZK-friendly model design, moving toward scalable, resource-efficient verifiable deep learning.
Kai Ma, Ningyu He, Jintao Huang, B. X. Zhang · 6 authors
Cybersquatting refers to the practice where attackers register a domain name similar to a legitimate one to confuse users for illegal gains. With the growth of the Non-Fungible Token (NFT) ecosystem, there are indications that cybersquatting tactics have evolved from targeting domain names to NFTs. This paper presents the first in-depth measurement study of NFT cybersquatting. By analyzing over 220K NFT collections with over 150M NFT tokens, we have identified 8,019 cybersquatting NFT collections targeting 654 popular NFT projects. Through systematic analysis, we discover and characterize seven distinct squatting tactics employed by scammers. We further conduct a comprehensive measurement study of these cybersquatting NFT collections, examining their metadata, associated digital asset content, and social media status. Our analysis reveals that these NFT cybersquatting activities have resulted in a significant financial impact, with over 670K victims affected by these scams, leading to a total financial exploitation of $59.26 million. Our findings demonstrate the urgency to identify and prevent NFT squatting abuses.
Alexander Plotkin, E. Kuznetsov, K. Starodubov, Yuri Gromov
The paper considers the solution of the problem of ensuring the resistance of key infrastructure in distributed registry systems to some destructive influences with the help of multi-factor authentication. Potentially possible destructive influences depending on defects in authentication systems, in particular on network nodes, are presented. Possible methods of providing resistance against such destructive influences are analyzed.
Log files are essential assets for IT engineers engaged in the security of server and computer systems. They provide crucial information for identifying malicious events, conducting cybersecurity incident analyses, performing audits, system maintenance, and ensuring compliance with security regulations. Nevertheless, there is still the possibility of deliberate data manipulation by own personnel, especially with regard to system access and configuration changes, where error tracking or debugging traces are vital. To address tampering of log files, this work proposes a solution to ensure data integrity, immutability, and non-repudiation through different blockchain-based public registry systems. This approach offers an additional layer of security through a decentralized, tamper-resistant ledger. To this end, this manuscript aims to provide a solid guideline for creating secure log storage systems. For this purpose, methodologies and experiments using two different blockchains are presented to demonstrate their effectiveness in various contexts, such as transactions with and without metadata. The findings suggest that Solana’s response times make it well suited for environments with moderately critical records requiring certification. In contrast, Cardano shows higher response times, thus making it suitable for less frequent events with metadata that requires legitimacy.
Open access
Blockchain Technology Applications and Security
Digital and Cyber Forensics
Advanced Steganography and Watermarking Techniques
Oshoke Samson Igonor, Muhammad Bilal Amin, Saurabh K. Garg†
Blockchain technology has risen in recent years from its initial application in finance to gain prominence across diverse sectors, including digital forensics. The possible application of blockchain technology to digital forensics is now becoming increasingly explored with many researchers now looking into the unique inherent properties that blockchain possesses to address the inherent challenges in this sector such as evidence tampering, the lack of transparency, and inadmissibility in court. Despite the increasing interest in integrating blockchain technology into the field of digital forensics and its domains, no systematic literature review currently exists to provide a holistic perspective on this integration. It is a challenge to find a comprehensive resource that examines how blockchain is being applied to enhance the digital forensics process. This paper provides a systematic literature review to explore the application of blockchain technology in digital forensics, focusing on its potential to address these challenges and enhance forensic methodologies. Through a rigorous review process, this paper examines selected studies to identify diverse frameworks, methodologies, and blockchain-driven enhancements applied to digital forensic investigations. The discussion highlights how blockchain properties such as immutability, transparency, and automation have been leveraged to improve evidence management and forensic workflows. Furthermore, this paper explores the common applications of blockchain-based forensic solutions across various domains and phases while addressing the associated limitations and challenges. Open issues and future research directions, including unexplored domains and operational gaps, are also discussed. This study provides valuable insights for researchers, investigators, and policymakers by offering a comprehensive overview of the state of the art in blockchain-based digital forensics, summarizing key contributions and limitations, and identifying pathways for advancing the field.
In smart contract fuzz testing, it is crucial to consider the inter-dependencies between the contract functions. To effectively test the business logic of a contract, its functions must be invoked in a meaningful order. In this paper, we propose techniques that utilize static analysis on Ethereum bytecode to tackle this challenge. When compared with the current state-of-the-art, our approach takes Solidity compiler’s variable packing optimization into account and allows more precise analysis of the data-flows between functions. In addition, we devise a novel test case initialization algorithm for fuzz testing, which minimizes the redundancy in the generated seed set. Our algorithm reduces test cases that share similar function call patterns and leads to more effective testing of the contract code during the fuzz testing. Experimental results show that the proposed techniques improve the effectiveness of smart contract fuzz testing for vulnerability detection. Specifically, our techniques enabled the fuzz testing tool to trigger the target bugs in the benchmark 3.0 times faster on average.
Leandro Loffi, Gerson Luiz Camillo, Cristiano Antonio de Souza, Carla Merkle Westphall · 5 authors
Digital evidence plays an increasingly crucial role in judicial proceedings due to the exponential growth in the creation, storage, and transmission of digital data. However, its inherent volatility and susceptibility to tampering necessitate robust mechanisms to ensure integrity and authenticity, making an effective chain of custody (CoC) a fundamental requirement. While state-of-the-art reviews identify various aspects, it is necessary to include the use of Self-Sovereign Identity (SSI) systems within the scope of research. To address this challenge, this article conducts a systematic review of the literature on the use of blockchain and SSI in managing the chain of custody of digital evidence. The review began with 9,178 studies, which, after a rigorous process applying inclusion and exclusion criteria, resulted in 39 studies directly related to the research topic. The study maps and reviews techniques, tools, methods, approaches, and security components for managing the chain of custody of digital evidence. The findings confirm the widespread adoption of blockchain for preserving digital evidence while indicating that SSI remains an emerging and underexplored concept in forensic applications. The results highlight the need for further research on off-chain storage mechanisms, privacy-preserving techniques such as Zero-Knowledge Proofs (ZKPs) to enhance security, auditability, and interoperability when combined with Verifiable Credentials (VCs). By mapping the current state of research, this study provides valuable insights into CoC, Blockchain, and SSI in forensic-based proposals, identifying research gaps, limitations, and opportunities for developing more robust and scalable evidence management systems.
The rise of blockchain and Digital Ledger Technology (DLT) has gained wide traction. Instead of relying on a traditional centralized data authority, a blockchain system consists of digitally entangled block data shared across a distributed network. The specially designed chain data structure and its consensus mechanism protect blockchain data from being tampered by unauthorized adversaries. However, implementing a full-fledged blockchain system to protect a database can be technically cumbersome. In this work, we introduce an in-database design, named chain table, to protect data integrity without the need for a blockchain system. It features a succinct design without significant technology barriers or storage overhead. To realize rigorous data security, we also propose a set of data writing principles for the chain table. We prove that the chain table, together with the data writing principles, will guarantee flexible data integrity, named table-level data integrity (TDI).
Mr. Amar More, Mr . Karan More, Mr . Nikhil Neavse, Mr. Prasanna Deokar · 6 authors
This project introduces a decentralized file storage system that leverages blockchain technology to create a secure, immutable, and tamper-resistant platform for file sharing. By storing files within blocks on a blockchain, the system ensures that once data is uploaded, it cannot be altered or deleted, making it ideal for applications where data integrity is critical. Users interact with the platform through a web interface, allowing them to upload, download, and share files across a peer-to-peer network. The blockchain structure used in this project employs a Proof of Work (PoW) consensus mechanism, requiring peers (miners) to solve cryptographic puzzles to validate blocks and add them to the chain. Two different PoW methods are used: one generates nonces at random, while the other increases the nonce value one after the other. By comparing the effectiveness and security of different methods, the project finds that random nonce generation outperforms them at higher difficulty levels, providing quicker block validation and more robust defense against possible assaults. On the other hand, the incremental approach is less secure over time because it is simpler to foresee. The project also covers the advantages of on-chain storage, which involves storing files directly inside blockchain blocks. This approach offers better security but comes at the expense of more processing power. Furthermore, it investigates alternatives such as off-chain blockchain architectures for more effective file storage in subsequent iterations and Proof of Stake (PoS) for lowering resource use