相关论文: Blockchain-Based and Fuzzy Logic-Enabled False Dat…

Blockchain smart contracts have given rise to a variety of interesting and compelling applications and emerged as a revolutionary force for the Internet. Quite a few practitioners have devoted themselves to developing tools for detecting…

Federated Learning (FL) has gained widespread popularity in recent years due to the fast booming of advanced machine learning and artificial intelligence along with emerging security and privacy threats. FL enables efficient model…

Unmanned Aerial Vehicles (UAVs) have significant potential for agile communication and relief coordination in post-disaster scenarios, particularly when ground infrastructure is compromised. However, efficiently coordinating and securing…

As artificial intelligence (AI) systems become increasingly integral to critical infrastructure and global operations, the need for a unified, trustworthy governance framework is more urgent that ever. This paper proposes a novel approach…

The internet of things (IoT) and other emerging ubiquitous technologies are supporting the rapid spread of smart systems, which has underlined the need for safe, open, and decentralized data storage solutions. With its inherent…

In a traditional cloud storage system, users benefit from the convenience it provides but also take the risk of certain security and privacy issues. To ensure confidentiality while maintaining data sharing capabilities, the…

Industrial processes rely on sensory data for decision-making processes, risk assessment, and performance evaluation. Extracting actionable insights from the collected data calls for an infrastructure that can ensure the dissemination of…

Innovative solutions addressing product anti-counterfeiting and record provenance have been deployed across today's internationally spanning supply chain networks. These product anti-counterfeiting solutions are developed and implemented…

Automated Driving Systems (ADSs) are being manufactured at an accelerated rate, leading to improvements in traffic safety, reduced energy consumption, pollution, and congestion. ADS relies on various data streams from onboard sensors,…

Fuzzing is a widely used technique for detecting vulnerabilities in smart contracts, which generates transaction sequences to explore the execution paths of smart contracts. However, existing fuzzers are falling short in detecting…

As blockchain smart contracts become more widespread and carry more valuable digital assets, they become an increasingly attractive target for attackers. Over the past few years, smart contracts have been subject to a plethora of…

Federated learning (FL), thanks in part to the emergence of the edge computing paradigm, is expected to enable true real-time applications in production environments. However, its original dependence on a central server for orchestration…

This study presents a blockchain-based voting system aimed at enhancing election security, transparency, and integrity. Traditional voting methods face growing risks of tampering, making it crucial to explore innovative solutions. Our…

The Internet of Things (IoT) connected by Software Defined Networking (SDN) promises to bring great benefits to cyber-physical systems. However, the increased attack surface offered by the growing number of connected vulnerable devices and…

Context: Exhaustive fuzzing of modern JavaScript engines is infeasible due to the vast number of program states and execution paths. Coverage-guided fuzzers waste effort on low-risk inputs, often ignoring vulnerability-triggering ones that…

Modern Internet of Things (IoT) applications generate enormous amounts of data, making data-driven machine learning essential for developing precise and reliable statistical models. However, data is often stored in silos, and strict…

Advancements in data-driven machine learning have emerged as a pivotal element in supporting automotive software systems (ASSs) engineering across various levels of the V-development process.…

For the modern world where data is becoming one of the most valuable assets, robust data privacy policies rooted in the fundamental infrastructure of networks and applications are becoming an even bigger necessity to secure sensitive user…

Federated learning (FL), as a distributed machine learning paradigm, promotes personal privacy by local data processing at each client. However, relying on a centralized server for model aggregation, standard FL is vulnerable to server…

Federated learning is an emerging privacy-preserving AI technique where clients (i.e., organisations or devices) train models locally and formulate a global model based on the local model updates without transferring local data externally.…