Search Results (104)

Existing Large Language Model cybersecurity evaluations rely on text-based plausibility scoring systems that fail to validate operational exploit viability. In this paper, we present the Operational Risk Framework (ORF), advancing beyond our prior MalcodeEval work through three (3) innovations: (1) ECDSA-P384 cryptographic execution validation providing non-repudiable proof-of-exploit, (2) MITRE ATT&CK-aligned tiered scoring with CVSS v4.0-derived severity weights, (3) and six-phase progressive validation tracking 217 Indicators of Compromise within isolated VM environments. The utility of this framework is demonstrated through detailed case studies that have revealed granular disparities in capabilities and multi-stage attack progression, often obscured by standard pass/fail binary metrics. This work contributes systematic LLM-to-CVSS mapping and open cryptographic protocols toward NIST AI RMF 2.0 development. Full article

As digitalization is widely used, Industrial Control Systems (ICSs) face severe cybersecurity challenges, where traditional defenses often lack real-time detection and immutable audit trails. Therefore, we propose a security alert platform that integrates blockchain, smart contracts, and homomorphic encryption. By leveraging the decentralized architecture of blockchain, the platform ensures the integrity and non-repudiation of operational logs. Concurrently, anomaly detection logic is embedded within smart contracts to enable an automated, real-time alerting mechanism. Furthermore, to preserve industrial data privacy, homomorphic encryption is employed, allowing the system to perform anomaly detection directly on encrypted data, thereby maintaining confidentiality throughout the data lifecycle. Preliminary analysis indicates that the proposed platform effectively enhances the resilience of ICS, strengthening both defense against unauthorized operations and post-incident forensic capabilities. Full article

We explore the problems encountered by today’s enterprises when using traditional human resource management systems and task tracking management systems to propose the use of blockchain technology as an innovative solution for internal human resource management and task tracking management. To ensure the security, transparency, non-repudiation, and traceability of the information submitted by various parties in the entire life cycle of task tracking management, we propose a task tracking management system based on blockchain technology. The system architecture integrates the key stakeholders in the entire value chain of task tracking management in human resource management, including internal employees, department managers, and human resource management departments. The architecture integrates the internal work tracking and management process of the enterprise through blockchain technology, ensuring data non-repudiation through digital signatures. Asymmetric encryption and decryption technology are employed to prevent data leakage and resist ransomware attacks. Based on the above features, it is highly suitable for enterprise introduction and use. Full article

Aiming at the challenge that traditional signature schemes struggle to simultaneously achieve efficiency, resistance to quantum attacks, and privacy protection, this paper proposes a lattice-based conditional privacy-preserving certificateless aggregate signature method (LCP-CAS). The scheme adopts an unordered aggregation algorithm to compress multiple signatures, in arbitrary order, into a single fixed-length aggregate signature, thereby achieving linear scalability in verification complexity. Its security is based on the hardness of the Ring Short Integer Solution (RSIS) problem, ensuring post-quantum resistance. By incorporating a conditional privacy-preserving mechanism, the scheme realizes device anonymity while supporting identity traceability, thus balancing privacy protection with regulatory requirements. Security analysis shows that the scheme meets the security requirements, including integrity, non-repudiation, conditional privacy preservation, and resistance to collusion attacks. Compared with existing related schemes, LCP-CAS achieves reduces aggregation and verification overhead while maintaining practicality in large-scale settings such as industrial IoT and device monitoring. Full article

Deepfake technology can produce highly realistic manipulated media which pose as significant cybersecurity threats, including fraud, misinformation, and privacy violations. This research proposes a deepfake prevention approach based on symmetric and asymmetric ciphers. Post-quantum asymmetric ciphers were utilized to perform digital signature operations, which offer essential security services, including integrity, authentication, and non-repudiation. Symmetric ciphers were also employed to provide confidentiality and authentication. Unlike classical ciphers that are vulnerable to quantum attacks, this study adopts quantum-resilient ciphers to offer long-term security. The proposed approach enables entities to digitally sign media content before public release on other platforms. End users can subsequently verify the authenticity of content using the public keys of the media creators. To identify the most efficient ciphers to perform cryptography operations required for deepfake prevention, the study explores the implementation of quantum-resilient symmetric and asymmetric ciphers standardized by NIST, including Dilithium, Falcon, SPHINCS+, and Ascon-80pq. Additionally, this research provides comprehensive comparisons between the various classical and post-quantum ciphers in both categories: symmetric and asymmetric. Experimental results revealed that Dilithium-5 and Falcon-512 algorithms outperform other post-quantum ciphers, with a time delay of 2.50 and 251 ms, respectively, for digital signature operations. The Falcon-512 algorithm also demonstrates superior resource efficiency, making it a cost-effective choice for digital signature operations. With respect to symmetric ciphers, Ascon-80pq achieved the lowest time consumption, taking just 0.015 ms to perform encryption and decryption operations. Also, it is a significant option for constrained devices, since it consumes fewer resources compared to standard symmetric ciphers, such as AES. Through comprehensive evaluations and comparisons of various symmetric and asymmetric ciphers, this study serves as a blueprint to identify the most efficient ciphers to perform the cryptography operations necessary for deepfake prevention. Full article

According to Fortune Business Insights, the market share of the Internet of Vehicless is expected to grow from USD 95.62 billion in 2021 to USD 369.61 billion in 2028, at a compound annual growth rate of 21.4%. However, the Internet of Vehicles system still faces several challenges, including regulation, scalability, data management, connectivity, interoperability, privacy, and security. To improve communication security within the Internet of Vehicle system, we have implemented a secure cryptographic algorithm called Optimized Certificateless Key-Encapsulated Encryption, resulting from a fusion of the key-insulated cryptosystem and the cryptographic key-encapsulated mechanism. The formal security analysis of our algorithm using the AVISPA version 1.1 software shows us that our protocol is safe. Informal analysis shows that our algorithm ensures authenticity, confidentiality, integrity, and non-repudiation and resists several other attacks. Our algorithm’s computational and communicational costs are slightly better than those at which it inherits the functionalities. Full article

Simultaneous ascending auctions find extensive applications in spectrum licensing and advertising space allocation. However, existing quantum sealed-bid auction protocols suffer from dual limitations: they cannot support multi-item simultaneous bidding scenarios, and their reliance on complex quantum resources along with requiring full quantum operational capabilities from bidders fails to accommodate practical constraints of quantum resource-limited users. To address these challenges, this paper proposes a multi-party semi-quantum simultaneous ascending auction protocol based on single-particle states. The protocol employs a trusted honest third party (HTP) responsible for quantum state generation, distribution, and security verification. Bidders determine their groups through quantum measurements and privately encode their bid vectors. Upon successful HTP authentication, each bidder obtains a unique identity code. During the bidding phase, HTP dynamically updates quantum sequences, allowing bidders to submit bids for multiple items by performing only simple unitary operations. HTP announces the highest bid for each item in real time and iteratively generates auction sequences until no new highest bid emerges, thereby achieving simultaneous ascending auctions for multiple items. It acts as a quantum-secured signaling layer, ensuring unconditional security for bid transmission and identity verification while maintaining classical auction logic. Quantum circuit simulations validate the protocol’s feasibility with current technology while satisfying critical security requirements, including anonymity, verifiability, non-repudiation, and privacy preservation. It provides a scalable semi-quantum auction solution for resource-constrained scenarios. Full article

The rapid expansion of digital payment channels has significantly widened the financial transaction attack surface, exposing ecosystems to sophisticated, polymorphic threat vectors. This study introduces an AI-powered cybersecurity mesh that unites Generative AI (GenAI), federated reinforcement learning, and zero-trust principles, with a forward-looking architecture designed for post-quantum readiness. The architecture ingests high-velocity telemetry, coordinates self-evolving agent collectives, and anchors model provenance in a permissioned blockchain to guarantee verifiability and non-repudiation. Empirical evaluations across two production-scale environments—a mobile wallet processing two million transactions per day and a high-throughput cross-border remittance rail—demonstrate a 95.1% threat-detection rate, a 62% reduction in false positives, and a 35.7% latency decrease compared to baseline systems. These results affirm the feasibility of a generative cybersecurity mesh as a scalable, future-proofed blueprint for next-generation payment security. Full article

We propose an entanglement-based quantum digital signature (QDS) protocol optimized for quantum networks. The protocol follows the Lamport-inspired QDS paradigm but eliminates QKD post-processing by signing and verifying with raw conclusive keys, thereby reducing latency and implementation complexity. We provide a finite-size security analysis of robustness, unforgeability, and non-repudiation. Under standard fiber-loss and detector models, simulations show a consistent signature rate advantage over a representative Lamport-inspired QDS baseline across metro-to-regional distances. The proposed protocol is practical for near-term deployment while preserving end-to-end, finite key security guarantees. Full article

Under the background of economic globalization and the rapid development of the knowledge economy, a large number of intellectual property achievements in China need to flow efficiently in order to give full play to their value; however, the traditional method of rights confirmation has problems, such as complicated operation, low efficiency, high cost, etc., and its practicability is limited. For this reason, this paper aims to construct an efficient intellectual property rights confirmation system by utilizing the characteristics of non-repudiation, non-tampering, traceability and distribution of the consortium chain. By designing smart contracts for user login and registration, rights confirmation, and transaction; combining with the Chameleon Signature algorithm to guarantee transaction security; and ensuring integration with the IPFS to improve the efficiency of file storage, this research develops an IPR confirmation system based on the consortium chain. This system was ultimately successfully deployed and tested, verifying that it operates with good efficiency and correctly realizes the expected functions. The findings show that the proposed system can effectively simplify the operation, provide reliable credentials, guarantee security and storage efficiency, and provide a feasible solution for intellectual property rights. Full article

The rapid integration of AI into IoT systems has outpaced the ability to explain and audit automated decisions, resulting in a serious transparency gap. We address this challenge by proposing a blockchain-based framework to create immutable audit trails of AI-driven IoT decisions. In our approach, each AI inference comprising key inputs, model ID, and output is logged to a permissioned blockchain ledger, ensuring that every decision is traceable and auditable. IoT devices and edge gateways submit cryptographically signed decision records via smart contracts, resulting in an immutable, timestamped log that is tamper-resistant. This decentralized approach guarantees non-repudiation and data integrity while balancing transparency with privacy (e.g., hashing personal data on-chain) to meet data protection norms. Our design aligns with emerging regulations, such as the EU AI Act’s logging mandate and GDPR’s transparency requirements. We demonstrate the framework’s applicability in two domains: healthcare IoT (logging diagnostic AI alerts for accountability) and industrial IoT (tracking autonomous control actions), showing its generalizability to high-stakes environments. Our contributions include the following: (1) a novel architecture for AI decision provenance in IoT, (2) a blockchain-based design to securely record AI decision-making processes, and (3) a simulation informed performance assessment based on projected metrics (throughput, latency, and storage) to assess the approach’s feasibility. By providing a reliable immutable audit trail for AI in IoT, our framework enhances transparency and trust in autonomous systems and offers a much-needed mechanism for auditable AI under increasing regulatory scrutiny. Full article

Innovation ecosystems often face challenges such as inadequate coordination, insufficient protection of intellectual property, limited access to quality expertise, and inefficient matchmaking between innovators and experts. This paper provides an in-depth design analysis of SPARK-IT, a novel business innovation platform specifically addressing these challenges. The platform leverages advanced AI to precisely match innovators with suitable mentors, supported by a distributed web scraper that constructs expert profiles from reliable sources (e.g., LinkedIn and BrainMap). Data privacy and security are prioritized through robust encryption that restricts sensitive content exclusively to innovators and mentors, preventing unauthorized access even by platform administrators. Additionally, documents are stored encrypted on decentralized storage, with their cryptographic hashes anchored on blockchain to ensure transparency, traceability, non-repudiation, and immutability. To incentivize active participation, SPARK-IT utilizes a dual-token approach comprising reward and reputation tokens. The reward tokens, SparkCoins, are wrapped stablecoins with tangible monetary value, enabling seamless internal transactions and external exchanges. Finally, the paper discusses key design challenges and critical architectural trade-offs and evaluates the socio-economic impacts of implementing this innovative solution. Full article

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. Full article

With the rapid development of quantum computers and quantum computing, Internet of Things (IoT) networks equipped with traditional cryptographic algorithms have become very weak against quantum attacks. This paper focuses on the privacy-preserving problem in IoT networks and proposes a certificateless ring signature (CLRS) scheme. This CLRS is constructed with lattice theories, which show promising advantages in resisting quantum attacks. Meanwhile, the certificateless mechanism reduces the key control ability of the key generation center (KGC) by adding personal secret keys to the private key generated by the system. Meanwhile, the ring signature mechanism protects users’ privacy information through a non-central control mechanism. Next, the security proof in a random oracle model is given, which shows that this CLRS scheme can obtain unforgeability and ensure the signer’s anonymity. Its security properties include non-repudiation, traceability, and post-quantum security. Then, the efficiency comparison and performance results show that this CLRS scheme is more efficient and practical than similar schemes. Moreover, this work presents an exploration of the post-quantum cryptographic algorithm and its application in IoT networks. Full article

This paper introduces a novel approach based on matrix multiplication in ${\mathbb{F}}_p^{n\times n}$, which enables methods for public key exchange, user authentication, digital signatures, blockchain integration, and homomorphic encryption. Unlike traditional algorithms that rely on integer factorization or discrete logarithms, our approach utilizes matrix factorization, rendering it resistant to current quantum cryptanalysis techniques. This method enhances confidentiality by ensuring secure communication and facilitating user authentication through public key validation. We have incorporated a method that allows a Certification Authority to certify the public keys. Furthermore, the incorporation of digital signatures ensures nonrepudiation, while the system functions as a blockchain technology to enhance transaction security. A key innovation of this approach is its capability to perform homomorphic encryption. Our approach has practical applications in artificial intelligence, robotics, and image processing. Full article