Algorithm Perception When Using Threat Intelligence in Vulnerability Risk Assessment. [PDF]
van Gerwen S +3 more
europepmc +1 more source
Who Are The Greatest Cyber Attack Threats To The United States?: UNH Unveils Cyber Threat Calculator At National Defense Department Conference [PDF]
Wright, Lori
core +1 more source
This paper presents a programmable metasurface‐based Meta Key Distribution (MKD) system for secure, protocol‐independent key exchange in indoor wireless settings. By embedding entropy into the wireless channel, it enables lightweight, compatible cryptographic key generation.
Xinyu Li +7 more
wiley +1 more source
Latent topic-driven cyber intelligence model for tactics, techniques, and procedures (TTPs) detection using hybrid framework and Birch-inspired optimisation. [PDF]
Alanazi MM, Wahab AWA, Idris MYI.
europepmc +1 more source
Digital Agriculture: Past, Present, and Future
Digital agriculture integrates Internet of Things, artificial intelligence, and blockchain to enhance efficiency and sustainability in farming. This review outlines its evolution, current applications, and future directions, highlighting both technological advances and key challenges for global implementation.
Xiaoding Wang +3 more
wiley +1 more source
Integrating AI in security information and event management for real time cyber defense. [PDF]
Khan S +4 more
europepmc +1 more source
Meta Reinforcement Learning for Automated Cyber Defence
ABSTRACT Reinforcement learning (RL) solutions have shown considerable promise for automating the defense of networks to cyber attacks. However, a limitation to their real world deployment is the sample efficiency and generalizability of RL agents. This means that even small changes to attack types require a new agent to be trained from scratch.
Andrew Thomas, Nick Tillyer
wiley +1 more source
Cybersecurity governance in the healthcare sector during digital transformation: an integrated model and hybrid analytical approach. [PDF]
Alharbi A, Alkhalifah A.
europepmc +1 more source
Multi‐Agent Reinforcement Learning for Cyber Defence Transferability and Scalability
A method for using mutli‐agent reinforcement learning that allows for zero shot transfer across network setups. Diagrams show the local observation construction, training and agent mapping process. The results for novel 15 and 30 node networks show effective transfer and improved scaling performance. ABSTRACT Reinforcement learning (RL) has shown to be
Andrew Thomas +2 more
wiley +1 more source

