Sandeep Kumar Mathariya, Sai Priyanka Tirumalasetty, Ajit More, P. Dinesh Kumar · 6 authors
As more and more forensic investigations use blockchain technology to preserve digital data, we will need to build systems that are both contextually optimal for investigations and impossible to break into. Most current blockchain forensic frameworks have a monolithic approach to consensus and static assessment models. This makes them not ideal for the ever-changing forensic context of different event sensitivity, legality, and auditability needs. There are currently barriers to the successful application in high-stakes forensic environments. This work presents the Forensic-Driven Blockchain Evaluation and Simulation Architecture (ForBESA), an extensive simulation-based evaluation framework designed to compare Proof-of-Stake (PoS), Directed Acyclic Graph (DAG), and Byzantine Fault Tolerant (BFT) blockchains against forensic key performance indicators (KPIs) to address existing deficiencies. This framework has five new modules. The Context-Aware KPI Weighting Engine (CAKWE) first makes dynamic KPI weight vector creation by using forensic event metadata and a decision tree classifier. Second, the Temporal Provenance DAG Tracker (TPDT) makes richer DAGs by adding investigator metadata and transaction timings. This makes it easier to find traces in the future. Third, the Hybrid Ledger Simulation Module will simulate how evidence moves between Hyperledger, IOTA, and Ethereum 2.0 using different KPIs that take forensic factors into account. Fourth, the Performance-Forensic Tradeoff Analyzer (PFTA) employs Pareto analysis and utility-based optimization to figure out if a design is good enough by weighing the pros and cons of performance and forensic depth. Finally, the Chain-of-Custody Cryptographic Verifier (C3V) combines smart contracts and zero-knowledge proofs to make sure that the evidence is safe and can be used in court. The experiments hardly show that the forensic efficacy has improved, that trace reconstruction is accurate to 98.1 %, and that tampering is detected 100% of the time. This study presents the inaugural paradigm for forensic-aware blockchain evaluation. The system enables ongoing digital investigations that are precise, legally compliant, and contextually aware.
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.
The ever-growing dependency on blockchain technology to secure e-evidence in a forensic investigation will involve building architecture that is intrinsically tamper-proof and at the same time optimized contextually for investigative purposes. Most of the extant blockchain forensic frameworks incorporate static evaluation models and a monolithic approach to consensus, rendering them ill-suited to the dynamic forensic context of various event sensitivity, legality requirements, and auditability demands. Existing barriers to practical deployment in high-stakes forensic environments have been created. To begin addressing these gaps, this work presents Forensic-Driven Blockchain Evaluation and Simulation Architecture (ForBESA), which provides a complete simulation-based evaluation framework to com- pare Byzantine Fault Tolerant (BFT), Directed Acyclic Graph (DAG), and Proof-of- Stake (PoS) blockchains against forensic key performance indicators (KPIs). This framework consists of five novel modules. First, the Context-Aware KPI Weighting Engine (CAKWE) develops dynamic generation of KPI weight vectors using forensic incident metadata through the use of a decision tree classifier. Second, the Temporal Provenance DAG Tracker (TPDT) constructs enriched DAGs embedding trans- action timelines and investigator metadata to enhance traces’ future availability. Third, evidence routing across Hyperledger, IOTA, and Ethereum 2.0 will be simulated within the Hybrid Ledger Simulation Module under individual KPIs weighted with forensic considerations. Fourth, the Performance-Forensic Tradeoff Analyzer (PFTA) employs a utilitybased optimization and Pareto analysis to identify architecture suitability based on forensic depth versus performance trade-offs. Finally, the Chain-of-Custody Cryptographic Verifier (C3V) ensures evidence integrity and legal admissibility using smart contracts and zero- knowledge proofs. The improved forensic effectiveness and trace reconstruction up to 98.1% accuracy and 100% tamper detection are scantly recorded in the experiments. This study creates the first model of its kind regarding forensic-aware blockchain evaluation. The system provides precise, legally compliant, and context-responsive digital investigations in process.
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.
With the continuous development of blockchain technology, massive off-chain data is mapped on the blockchain, ensuring the authenticity and privacy of on-chain data and off-chain data is a significant challenge. To solve this question, many studies use oracle to provide secure and reliable data for blockchain applications. Existing oracle schemes can protect the privacy of single-source data and prove the authenticity of private data sources to the third party. However, when handling multi-source data, these schemes require multiple executions to process and verify all data. We propose an optimized oracle scheme based on the “TLS-MPC” framework to improve efficiency. Firstly, we optimize the handshake process by dividing the$\mathrm{n}$servers into$\mathrm{t}$clusters and use the session ticket to reduce the number of MPC executions during the three-party handshake. As a result, most servers within each cluster run a fast three-party handshake by session ticket. Secondly, the prover runs two-party computation with the verifier to generate the queries and sends them to each data source to get the multi-source data. Then we design a constructable zero-knowledge proof system. Prover will inputs the multi-source data into the system to generate the proof value with a joint computation circuit. Finally, verifier will check the result sent from prover after the zero-knowledge proof is completed. Comparing with the DECO, our scheme is more efficient.
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.
The presented work is devoted to the issues of the feasibility of studying the semantics of the cryptocurrency ecosystem in order to determine the ways of further development of forensic techniques used in the investigation of crimes committed using digital assets. The intensive spread of the use of cryptocurrencies and their ecosystems requires a consistent and uniform understanding of terminology, which will ensure a single vector for identifying and suppressing socially dangerous acts. Attention is focused on the need to study both technological and legal aspects, which contributes to the integrity of understanding the specifics of such objects, as well as the implementation of procedural actions to identify, record and seize electronic digital traces. As a result of the study, the author comes to the conclusion about the practical need to study it from the technological and legal aspects in order to develop a uniform and unified understanding of its specific terminology in the framework of developing new, as well as improving existing methods for investigating crimes committed using cryptocurrencies.
G. Saranya, Velayudham Narendhran, D Karthi, Anand G. Mehul · 5 authors
Securing forensic evidence integrity and security is fundamental to avoid tampering, loss, and illegal access. Classic centralized evidence management systems are easy to manipulate and breach. The current paper advocates for a blockchain-based forensic evidence management system that uses the Ethereum ERC-721 token standard to tokenize each bit of evidence as a non-fungible token (NFT) to promote immutable ownership and verifiable access.The system uses wallet-based authentication and role-based access control (RBAC) to limit evidence handling to authorized users. Key metadata, such as timestamps, digital hashes, and access logs, are recorded immutably on the blockchain. Smart contracts perform verification, logging, and auditing automatically, minimizing human errors and improving operational efficiency. A hybrid storage model stores ownership records on-chain while keeping large forensic files off-chain.
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.
Blockchain gaming platforms based on Non-Fungible Tokens (NFTs) have vastly grown and provide players with ownership to trade and monetize in-game assets. However, the integrity of NFT transactions in Proof-of-Stake (PoS) systems are prone to security threats due to the possible insertion of malicious blocks. Detection of these blocks is required to keep trust in the ecosystem. The proposed machine learning-based solution included detection of malicious blocks in PoS with focus on the performance of an LSTM Model. Following extensive testing with 3 models of LSTM, 1D CNN and GRU, the model achieved an accuracy of 86.9% making it most effective in regards to early identification of fraudulent transactions. This method augments the security of NFT gaming transactions and thus offers support against several malicious acts occurring in blockchain based ecosystems.
As cyber-crimes have become more complex network forensics has become an essential element of cybersecurity investigations. However, conventional forensic techniques are confronted with challenges such as data privacy, integrity, and secure authentication of evidence. This paper suggests a privacy-preserving AI-augmented forensic framework that uses Zero-Knowledge Proofs (ZKP) for authenticating forensics securely and blockchain for tamper-evident forensic storage. The intended framework employs AI and ML strategies for real-time intrusion detection real-time intrusion detection, anomaly recognition, and cyber-attack attribution, radically enhancing forensic efficacy and investigative productivity. Experimental evidence obtained with the UNSW-NB15 dataset provides evidence that the AI model offers a detection rate of 97.5% accompanied by precision as high as 96.8% and a recall of as much as 98.2% to ensure good cyber threat classification. Moreover, the verification process of ZKP takes only 1.2 milliseconds, allowing for fast forensic validation with data confidentiality being maintained. The blockchain-based logging system for forensics has an overhead of merely 0.35 MB per transaction, allowing tamper-proof and scalable storage of forensic data. The findings confirm that integrating AI, ZKP, and blockchain improves forensic trustworthiness at the cost of reduced data exposure. This work adds to developing privacy-protecting forensic techniques and offers a secure, scalable solution for contemporary cybercrime investigations.
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.