In recent years, zero-knowledge proofs have made great strides in efficiency, on two fronts: minimizing the finite field size without sacrificing security and allowing more constructs in their intermediate representations, such as look-ups. These have lead to novel, efficiently aritmetizable, hash functions, which are the backbone of modern proof systems. We take advantage of these improvements and present a resource constrained hardware design for the Monolith hash function, targeted torwards embedded, edge IoT devices. The hardware implementation demonstrates a significant performance advantage over the software-only solution on the Zynq SoC, achieving a 22× speed-up while consuming only a few hundred milliwatts.
Cryptographic Implementations and Security
Cryptography and Residue Arithmetic
Physical Unclonable Functions (PUFs) and Hardware Security
The evolution of 5G and emerging 6G networks has introduced unprecedented opportunities for connectivity, but also expanded the attack surface for Distributed Denial of Service (DDoS) amplification attacks. Service-Based Architecture (SBA), network slicing, and massive IoT (mMTC) environments create new vectors for reflection and amplification, making conventional defenses inadequate. This paper proposes a novel layered defense framework that integrates edge filtering, AI-driven anomaly detection, slice isolation, cloud scrubbing, and quantum-safe cryptography to mitigate DDoS amplification attacks in 5G/6G environments. The framework is theoretically modeled through equations for amplification, mitigation efficiency, resilience, and defense cost, and evaluated experimentally using simulated signaling floods, IoT-driven amplification, slice-targeted floods, and hybrid attacks. Performance was measured using detection rate, false alarm rate, service availability, resilience score, and resource overhead. Two algorithms—pseudonymous authentication with zero-knowledge proof (ZKP) and layered mitigation orchestration—were implemented to operationalize the defense strategy. The results demonstrate that the proposed framework achieves a detection accuracy of 95–97%, reduces false positives to 2%, and maintains a service availability of over 85% under prolonged amplification attacks. It scales efficiently in scenarios with up to 10,000 simulated IoT devices, retaining 70–80% throughput, and maintains URLLC latency below 10 ms, outperforming baseline defenses (firewalls, scrubbing, and AI-only) and state-of-the-art defenses from the literature. These findings validate the framework as a scalable, efficient, and future-ready solution for mitigating amplification attacks in 5G/6G networks, with strong alignment with 3GPP, GSMA, and NIST post-quantum standards.
Open access
Advanced Malware Detection Techniques
Network Security and Intrusion Detection
Physical Unclonable Functions (PUFs) and Hardware Security
A. G. Ramakrishnan, Shubham Agarwal, Sharmila Kumari Selvanayagam, Kunwar P. Singh
As image generation models grow increasingly powerful and accessible, concerns around authenticity, ownership, and misuse of synthetic media have become critical. The ability to generate lifelike images indistinguishable from real ones introduces risks such as misinformation, deepfakes, and intellectual property violations. Traditional watermarking methods either degrade image quality, are easily removed, or require access to confidential model internals – making them unsuitable for secure and scalable deployment. We are the first to introduce ZK-WAGON, a novel system for watermarking image generation models using the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (ZK-SNARKs). Our approach enables verifiable proof of origin without exposing model weights, generation prompts, or any sensitive internal information. We propose Selective Layer ZK-Circuit Creation (SL-ZKCC), a method to selectively convert key layers of an image generation model into a circuit, reducing proof generation time significantly. Generated ZK-SNARK proofs are imperceptibly embedded into a generated image via Least Significant Bit (LSB) steganography. We demonstrate this system on both GAN and Diffusion models, providing a secure, model-agnostic pipeline for trustworthy AI image generation.
Open access
2 source records
Physical Unclonable Functions (PUFs) and Hardware Security
Adversarial Robustness in Machine Learning
Generative Adversarial Networks and Image Synthesis
With the development of science and technology, the Internet of Things (IoT) had been integrated into the daily life of people. That makes the security of IoT a necessity and gains more attention. The Device authentication is an important issue in the security of IoT. In this paper, we propose a device authentication scheme based on both Physically Unclonable Function (PUF) and zero-knowledge proof. The proposed mutual authentication scheme reduces the memory load on the server and provides both data integrity and confidentiality during the authenticating process. We verify the proposed device authentication algorithm on the IoT platform Raspberry Pi using SRAM-PUF. The experimental results reveal that the proposed device authentication scheme is novel for IoT. It can resist brute force attack, replay attack, man-in-the-middle attack, machine learning attacks, and etc.
Physical Unclonable Functions (PUFs) and Hardware Security
Nayantara Thomas, Liya Treesa Philip, Pampana Jahnavi, R. Sujatha · 5 authors
The increasing complexity and globalization of the semiconductor supply chain, particularly in Field-Programmable Gate Arrays (FPGAs), has introduced significant vulnerabilities including counterfeiting, unauthorized modifications, and malicious hardware insertions. To address these challenges, this project proposes a novel security framework that integrates Zero Trust Architecture (ZTA), blockchain technology, and Arbiter Physical Unclonable Functions (PUFs) to enhance the authenticity, traceability, and resilience of the FPGA supply chain. The framework employs PUFs to generate unique hardware identities for FPGA components, which are authenticated through smart contracts and immutably recorded on a permissioned blockchain. By adopting ZTA principles, the system ensures continuous verification of all entities and components across the supply chain. The proposed architecture supports real-time monitoring, automated anomaly detection, and role-based access control, creating a tamper-proof and transparent ledger of FPGA transactions. This approach significantly strengthens supply chain integrity, mitigates risks of counterfeit insertion, and establishes a secure foundation for deploying FPGAs in mission-critical applications such as defense, telecommunications, and AI systems.
Physical Unclonable Functions (PUFs) and Hardware Security
Cross-chain decentralized finance ( DeFi) applications enable the seamless transfer of assets and data across diverse blockchain networks, thereby enhancing liquidity and user flexibility. However, the distributed nature and inter-operability of these networks introduce significant security challenges, ranging from double-spending and smart contract vulnerabilities to mismatches in consensus mechanisms and privacy risks. In this survey, we introduce a novel review about the recent literature that explores these multifaceted challenges. We categorize the research based on attack vectors and the security requirements of cross-chain systems, discuss state-of-the-art solutions including advanced cryptographic techniques and rigorous auditing practices, and outline open problems that warrant further explorations. Our work provides a comprehensive analysis of the current security landscape in cross-chain DeFi and emphasizes future research directions.
Physical Unclonable Functions (PUFs) and Hardware Security
Nan Wang, Nan Wu, Xiangyu Hui, Jiafan Wang · 5 authors
As the demand for exercising the "right to be forgotten" grows, the need for verifiable machine unlearning has become increasingly evident to ensure both transparency and accountability. We present {\em zkUnlearner}, the first zero-knowledge framework for verifiable machine unlearning, specifically designed to support {\em multi-granularity} and {\em forgery-resistance}. First, we propose a general computational model that employs a {\em bit-masking} technique to enable the {\em selectivity} of existing zero-knowledge proofs of training for gradient descent algorithms. This innovation enables not only traditional {\em sample-level} unlearning but also more advanced {\em feature-level} and {\em class-level} unlearning. Our model can be translated to arithmetic circuits, ensuring compatibility with a broad range of zero-knowledge proof systems. Furthermore, our approach overcomes key limitations of existing methods in both efficiency and privacy. Second, forging attacks present a serious threat to the reliability of unlearning. Specifically, in Stochastic Gradient Descent optimization, gradients from unlearned data, or from minibatches containing it, can be forged using alternative data samples or minibatches that exclude it. We propose the first effective strategies to resist state-of-the-art forging attacks. Finally, we benchmark a zkSNARK-based instantiation of our framework and perform comprehensive performance evaluations to validate its practicality.
Vincent Duronio, Mohamed Firas Aguir, Hari Sai Kaja, Andrew J. Kalafut · 6 authors
The Internet of Health Things (IoHT) introduces new opportunities for remote patient monitoring but also exposes sensitive medical data to serious security risks. Traditional authentication mechanisms are often too resource-intensive for IoHT devices, which are constrained in terms of storage, computation, and energy. This paper presents a lightweight, decentralized authentication protocol tailored for IoHT environments, integrating blockchain technology, Physical Unclonable Functions (PUFs), and zero-knowledge proofs (zk-SNARKs). Our protocol employs the Proof of Elapsed Work and Luck (PoEWAL) consensus mechanism, enabling secure, energy-efficient participation in a private Hyperledger Fabric blockchain. The scheme ensures strong mutual authentication between healthcare providers and IoHT devices without exposing sensitive data. An informal analysis is done to demonstrate the protocol’s resilience against common attacks. This protocol offers a secure solution for enabling trustworthy healthcare communication in resource-limited IoHT networks.
Physical Unclonable Functions (PUFs) and Hardware Security
The rapid expansion of Internet of Things (IoT) deployments across smart environments introduces critical security challenges, particularly at the device identity and physical layers. Traditional cryptographic methods and Distributed Ledger Technologies (DLTs), while valuable, often fail to account for the constrained resources of IoT devices and their susceptibility to physical-layer attacks. This paper proposes a scalable, lightweight security framework that integrates Physical Unclonable Functions (PUFs), Zero-Knowledge Proofs (ZKPs), and a permissioned blockchain to establish end-to-end trust in distributed IoT ecosystems. PUFs act as hardware-rooted trust anchors, enabling secure key generation and unclonable device identity without relying on non-volatile memory. ZKPs facilitate mutual authentication by allowing devices to prove legitimacy without revealing any identifying information. A permissioned blockchain acts as a decentralised verification and audit layer, immutably recording authentication events and ensuring tamper resistance with controlled governance. The proposed architecture is designed to counteract physical tampering, spoofing, and identity forgery while remaining computationally viable for resource-constrained IoT devices. This work presents the foundation for a robust, privacy-preserving, and decentralised security model, bridging the gap between hardware-level assurance and scalable trust in future IoT deployments.
Physical Unclonable Functions (PUFs) and Hardware Security
Venkateswarlu Boddu, Malaya Dutta Borah, Naresh Babu M
This paper proposes a blockchain-based framework to monitor drugs and trigger alerts to safeguard stakeholders when drugs are reported to be faulty. The proposed system uses a decentralised approach, enhancing the overall transparency and security. The framework identifies counterfeit drugs and reports the issue across various stakeholders in the pharmaceutical supply chain, including regulatory departments, hospitals, and manufacturers. The alerts regarding contaminated or counterfeit drugs are ensured due to the integration of blockchain and distributed ledger. Furthermore, the recorded data are secure, traceable, and immutable. The results demonstrate that the framework successfully registers the departments and sends the alert across the departments associated with the blockchain. Also, the results confirm the feasibility of using blockchain to create a transparent and robust counterfeit drug alert network, providing a reliable solution for pharmaceutical safety management.
Pharmaceutical Quality and Counterfeiting
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
Yagmur Yigit, Mehmet Ali Erturk, Kerem Gursu, Berk Canberk
Digital twin (DT) technology is rapidly becoming essential for smart city ecosystems, enabling real-time synchronisation and autonomous decision-making across physical and digital domains. However, as DTs take active roles in control loops, securely binding them to their physical counterparts in dynamic and adversarial environments remains a significant challenge. Existing authentication solutions either rely on static trust models, require centralised authorities, or fail to provide live and verifiable physical-digital binding, making them unsuitable for latency-sensitive and distributed deployments. To address this gap, we introduce PRZK-Bind, a lightweight and decentralised authentication protocol that combines Schnorr-based zero-knowledge proofs with elliptic curve cryptography to establish secure, real-time correspondence between physical entities and DTs without relying on pre-shared secrets. Simulation results show that PRZK-Bind significantly improves performance, offering up to 4.5 times lower latency and 4 times reduced energy consumption compared to cryptography-heavy baselines, while maintaining false acceptance rates more than 10 times lower. These findings highlight its suitability for future smart city deployments requiring efficient, resilient, and trustworthy DT authentication.
The electronics sector depends on geographically dispersed, multi-tier supplier networks that generate enormous volumes of audit-relevant data every day. Conventional compliance approaches—relying on periodic manual audits, centralised repositories, and siloed information exchange—are ill-equipped to cope with real-time regulatory demands arising from frameworks such as the EU Conflict Minerals Regulation, RoHS Directive, REACH, and emerging digital-product-passport legislation. This paper proposes and evaluates a Distributed Ledger Architecture (DLA) built on Hyperledger Fabric that encodes supplier audit trails as cryptographically immutable on-chain records, automated via smart-contract chaincode, and enriched through Internet-of-Things (IoT) sensor oracles deployed at manufacturing nodes. A twelve-month simulation experiment spanning eighty supplier nodes across three continents demonstrates that the proposed system raises the supplier compliance rate from 75.5 % to 97.2 %, reduces audit-cycle time from weeks to sub-three-second confirmations, cuts per-supplier audit cost by 87 %, and reduces document falsification events by 96 %. The architecture is shown to scale linearly to 80 nodes while sustaining 2,890 transactions per second at a mean end-to-end latency of 148 ms—outperforming both Ethereum Proof-of-Authority and centralised database baselines across all throughput dimensions. The research contributes a formal DLA design schema, an empirical performance benchmark, and a policy mapping framework for multi-jurisdictional regulatory alignment
Blockchain Technology Applications and Security
Supply Chain Resilience and Risk Management
Physical Unclonable Functions (PUFs) and Hardware Security
Dan Ivanov, Tristan Freiberg, Shahabi, Shirin, Jonathan Gold · 5 authors
DSperse is a modular framework for distributed machine learning inference with strategic cryptographic verification. Operating within the emerging paradigm of distributed zero-knowledge machine learning, DSperse avoids the high cost and rigidity of full-model circuitization by enabling targeted verification of strategically chosen subcomputations. These verifiable segments, or "slices", may cover part or all of the inference pipeline, with global consistency enforced through audit, replication, or economic incentives. This architecture supports a pragmatic form of trust minimization, localizing zero-knowledge proofs to the components where they provide the greatest value. We evaluate DSperse using multiple proving systems and report empirical results on memory usage, runtime, and circuit behavior under sliced and unsliced configurations. By allowing proof boundaries to align flexibly with the model's logical structure, DSperse supports scalable, targeted verification strategies suited to diverse deployment needs.
Open access
2 source records
Adversarial Robustness in Machine Learning
Physical Unclonable Functions (PUFs) and Hardware Security
This paper presents a novel blockchain-based incentive system that protects farmers' privacy while promoting sustainable farming methods by using zero-knowledge proofs, or ZKPs. With this method, farmers can show that they've met their yield goals without giving away private production information. We implement the system as an Ethereum smart contract that uses cryptographic assurances to ensure correct reporting and distributes incentives based on verified crop yields. Our method addresses important problems in agricultural sustainability projects, like protecting privacy, the cost of verification, and making sure that rewards are shared fairly. The suggested answer affects sustainable agriculture policy, privacy-focused data exchange in supply lines, and the use of cryptographic methods in environmental governance.
Blockchain Technology Applications and Security
Smart Agriculture and AI
Physical Unclonable Functions (PUFs) and Hardware Security
The rise of blockchain has expanded the possibilities for asset representation, particularly through Non-Fungible Tokens (NFTs), which enable unique connections between digital and physical assets. Despite their potential, existing NFT systems face challenges such as high transaction costs and uncertainties related to the management and transfer of associated physical assets. This paper introduces a decentralized marketplace architecture designed to address these challenges, by making use of blockchain-based smart contracts, decentralized storage solutions, and trustless validation mechanisms. The proposed system separates ownership from possession, ensuring secure and transparent asset transfers. The architecture was then tested in a blockchain-based environment, demonstrating its economic viability. This work paves the way for decentralized asset management across various domains, such as rental services and remote logistics. By addressing existing challenges, the proposed architecture provides a usable and cost-effective solution that links digital and physical asset ecosystems while preserving the fundamental principles of blockchain technology.
Blockchain Technology Applications and Security
Cloud Data Security Solutions
Physical Unclonable Functions (PUFs) and Hardware Security
Custom tokens are fundamental in decentralized applications (dApps) operating on Ethereum and other Blockchain platforms. Ethereum, in particular, relies on the ERC-20 standard as a widely accepted token interface, facilitating seamless integration with numerous pre-existing dApps, user interface platforms, and popular web applications like exchange services. A notable security challenge within the ERC-20 framework is the “lost token problem”. This problem arises because users occasionally send tokens to the wrong addresses, and it has caused more than $27 million in damage. In this paper, we evaluate three existing solutions to this issue. Through the utilization of formal modeling, property specification, and the TLC model checker. Most importantly, we propose a novel double-layer solution to remedy the ERC-20 vulnerability. Our formal verification and experimental results indicate our approach encompasses the protection of the already deployed smart contracts, which is a critical aspect that has never been addressed in the existing mitigation techniques.
Open access
Security and Verification in Computing
Radiation Effects in Electronics
Physical Unclonable Functions (PUFs) and Hardware Security
Non-fungible tokens (NFT) have recently become a popular method of tokenizing \& commercializing personal artifacts. Designing NFTs requires selecting different blockchain-based consensus models, encryption techniques, and distribution mechanisms. Existing NFT design techniques use computationally complex encryption models like Elliptic Curve Cryptography (ECC), Advanced Encryption Standard (AES), etc., which restricts their general-purpose usability, limiting their scalability for real-time use cases. To overcome this drawback, while maintaining high security, this text proposes a design of a lightweight, restrictive non-fungible token based on Practically Unclonable Functions (PuFs) via image signature patterns. The proposed model initially collects context-specific information sets about the entity that needs tokenization and uses this information to generate restrictive hash sets. These hash sets are passed through a customized PuF model, which generates image-like hash signatures. The generated hash signatures are iteratively embedded into unique images, which are fused via a dual visual encryption-decryption process. The encryption process generates 2 image sets, for distribution among the buyer \& seller, while the decryption process aggregates these image sets to form a single file token. These tokens are passed through another encryption-decryption-based validation process while reselling operations. Due to use of PuFs and restrictive hash sets, the proposed model is capable of deployment for low-power IoT applications and can be scaled for general-purpose scenarios. The proposed model was tested on different NFT use cases, and showcased 10.4% lower processing delay, 8.3% lower energy consumption during selling, and 4.9% lower energy consumption during reselling processes. The tokens generated via this model were also tested under different attack types, and similar efficiency levels were observed under real-time scenarios.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
The cybersecurity threats targeting industrial control systems (ICS) are evolving with increasing sophistication. Addressing the detection blind spots in existing source code analysis techniques, this study reveals a dual security paradox arising from code sensitivity: privacy leakage risks caused by decompilation techniques and integrity verification deficiencies in reverse engineering. This paper investigates three critical challenges: (1) What are the component flow process and detection elements of ICS component source code? (2) How can high-performance and reliable tracing and traceability be provided for ICS component source code exceptions and routine detection? (3) How can privacy enhancement and trusted detection of ICS component source code with high sensitivity be achieved? This paper proposes a blockchain-integrated trusted detection framework for ICS (BCTD-ICS), delivering groundbreaking solutions: (1) Establishing a lifecycle circulation model that systematically maps component types, stakeholders, and detection parameters; (2) Developing a tripartite collaborative architecture (Blockchain-Identification Resolution Zero-knowledge proofs (ZKPs)), featuring a traceability mechanism with trusted identification codes (resolution efficiency: 40ms/105 queries) to eliminate decompilation-induced privacy risks; (3) Creating an industrial-oriented privacy enhancement system utilizing DBSCAN clustering for intelligent sampling (26% compression rate on BCN3D Moveo) and optimizing ZK-SNARK protocols through Shamir’s Secret Sharing, establishing a backdoor-resistant distributed parameter generation system (time delay increment < 100ms). Experimentally verified, our solution enables ICS component code detection supply-chain-wise without sensitive data leakage in real-world industries. This work establishes a novel trusted detection paradigm for ICS, advancing detection efficiency and credibility under strict privacy preservation requirements, meeting Industry 4.0 security demands.
Smart Grid Security and Resilience
Physical Unclonable Functions (PUFs) and Hardware Security
Smart contracts, self-executing programs deployed on blockchain networks, offer significant benefits in terms of transparency, efficiency, and trustless operations. However, their widespread adoption is hindered by security vulnerabilities that can lead to financial losses and operational failures. These vulnerabilities often stem from coding errors and logical flaws, such as input validation issues, race conditions, and value propagation problems. In this work, we propose a systematic analysis of common security risks in smart contracts, based on an in-depth literature review and the examination of several case studies. The results identify the most critical vulnerabilities and suggest best practices to mitigate these risks. For instance, the study highlights the importance of rigorous input validation and the use of formal models to verify contract logic. In short, this study provides valuable guidelines for smart contract developers and contributes to strengthening the security of blockchain applications. As a perspective for future work, avenues for research on the formalization of smart contracts and the automation of security testing will be explored.
Physical Unclonable Functions (PUFs) and Hardware Security
Artificial Intelligence (AI) is profoundly transforming cryptography by significantly enhancing cryptanalysis techniques and informing innovative cryptographic design approaches. This survey reviews recent advancements in applying deep learning methods to side-channel and differential fault analyses, demonstrating substantial improvements over traditional methods in attack efficiency, accuracy, and resilience. Additionally, it highlights breakthroughs such as neural differential cryptanalysis, which expand classical cryptanalytic boundaries. In cryptographic design, Generative Adversarial Networks (GANs) have successfully automated the creation of high-quality cryptographic primitives, particularly S-boxes. Furthermore, AI shows promise in post-quantum cryptography (PQC) by uncovering potential vulnerabilities and optimizing cryptographic parameters. Despite these advancements, challenges persist regarding data dependency, model generalization, and interpretability. Future research directions emphasize enhancing AI model explainability, creating standardized benchmarks, and integrating AI with emerging technologies such as quantum computing and zero-knowledge proofs.
Open access
Cryptographic Implementations and Security
Chaos-based Image/Signal Encryption
Physical Unclonable Functions (PUFs) and Hardware Security