Tags → #internet of things

Research highlights the urgent need for enhanced cybersecurity measures, including adaptive security protocols and AI-driven solutions, as the telecommunications industry prepares for the transition from 5G to 6G.

Recent research introduces FlowID, a multi-view correlation-aware framework that enhances network traffic detection capabilities using advanced methodologies like graph neural networks and semi-supervised learning.

Recent research on decentralized federated learning (DFL) has shown advancements in real-time anomaly detection for smart grid systems, utilizing a gossip-based protocol to enhance communication efficiency and resilience against cyberattacks.

Recent research has developed deep learning models to enhance security in Low Power Wide Area Networks (LoRa) by effectively classifying legitimate and rogue devices, addressing vulnerabilities in Internet of Things (IoT) applications.

Recent research has identified significant cybersecurity vulnerabilities in military LoRaWAN networks, particularly related to sniffing and replay attacks, highlighting the need for improved security measures.

A recent tutorial addresses the security challenges faced by Internet of Things (IoT) devices in healthcare, proposing adaptive solutions such as the ASCON lightweight cryptographic cipher to enhance protection and performance.

Smart home devices offer convenience through automation and control but also pose significant cybersecurity and privacy risks that require careful management and mitigation strategies.

The article discusses the security vulnerabilities of IoT devices using the HTTP protocol and introduces ChatHTTPFuzz, a novel method that utilizes large language models to improve vulnerability detection in these devices.

A recent study addresses security challenges in Internet of Things (IoT) environments, proposing a blockchain-based data-sharing scheme designed to enhance efficiency and security while reducing computational overhead.