Results 51 to 60 of about 417 (172)
Recent proposals by Mahmood et al., Braeken et al., and Chaudhry et al. aimed at establishing secure communication in AMI, asserting their methods fulfil the requisite security criteria. This paper, however, reveals that each of these proposals exhibits security vulnerabilities and lacks sufficient features for deployment.
Ahmad Rahdari, Bahareh Akhbari
wiley +1 more source
Delay‐Scheduled Adaptive Observer Control Strategy for Nonlinear Systems With Time‐Varying Delays
This paper proposes a delay‐scheduled adaptive observer controller for nonlinear systems with unknown time‐varying delays and bounded disturbances. By integrating an adaptive observer with an LMI‐based state‐feedback controller, the method ensures robust stability and H∞$H_\infty$ performance. Numerical simulations on delayed Lorenz and hyperchaotic Lü
Mohammad Ali Labbaf Khaniki +1 more
wiley +1 more source
A Novel Two-Level Protection Scheme against Hardware Trojans on a Reconfigurable CNN Accelerator
With the boom in artificial intelligence (AI), numerous reconfigurable convolution neural network (CNN) accelerators have emerged within both industry and academia, aiming to enhance AI computing capabilities.
Zichu Liu +3 more
doaj +1 more source
Securing Machine‐Type Communications: A Survey on Privacy Threats and Countermeasures
Machine‐type communication (MTC) is a fundamental enabler of the Internet of Things (IoT) and emerging 5G/6G networks, supporting massive deployments of heterogeneous and resource‐constrained devices. However, large‐scale data collection, persistent connectivity, and limited device capabilities introduce critical privacy challenges that are not ...
Amirhosein Imani +2 more
wiley +1 more source
Security Challenges and Solutions in Self‐Driving Vehicles: A Comprehensive Review
This article presents a comprehensive review of cybersecurity challenges and solutions in self‐driving vehicles, focusing on threats across vehicular communication, sensors, artificial intelligence (AI) systems, and data privacy. It categorises attacks based on autonomous vehicle architecture layers and highlights advanced countermeasures such as AI ...
Yaw Opoku Mensah Sekyere +2 more
wiley +1 more source
Cybersecurity Driven Quantum Digital Twin for Proactive Threat Reversal in Open RAN
This paper presents a novel cybersecurity‐driven quantum digital twin (CQDT) architecture for proactive defence in 6G Open RAN. By integrating quantum observables and reinforcement learning, the framework maintains high fidelity and suppresses entropy under adversarial CPTP noise. CQDT achieves real‐time adaptation with sub‐20 ms latency, meeting URLLC
Yassir Al‐Karawi +2 more
wiley +1 more source
Resisting Quantum Key Distribution Attacks Using Quantum Machine Learning
Quantum key distribution (QKD) promises secure communication but remains vulnerable to advanced quantum attacks. We propose a hybrid quantum long short‐term memory (QLSTM) model that combines quantum‐enhanced learning with classical deep learning to detect attacks such as photon‐number splitting, Trojan‐Horse and detector blinding.
Ali Al‐Kuwari +4 more
wiley +1 more source
Quantum Key Distribution Networks Design: Overview and Challenges
This paper explores the potential of using established QKD network design techniques in the context of quantum key distribution, which is based on the principles of quantum mechanics. ABSTRACT Quantum cryptography has increasingly attracted interest from both industry and academia for its potential in real‐world applications.
Pankaj Kumar +2 more
wiley +1 more source
Device‐Independent Quantum Key Distribution: Protocols, Quantum Games and Security
Device‐independent quantum key distribution (DIQKD) removes the need to trust internal device behaviour by certifying security through Bell‐inequality violations, thereby closing practical loopholes in conventional QKD. This paper systematically reviews DIQKD foundations (Bell tests and security definitions), protocol frameworks (CHSH‐based and ...
Syed M. Arslan +3 more
wiley +1 more source
AI‐Powered Defense: Leveraging Deep Learning for Effective Malware Detection
Traditional malware detection techniques frequently fail to detect and stop malicious activity in an era where cyber threats are becoming more complex. Any software that enters a computer system without the administrator’s consent is considered malicious software.
Nancy Awadallah Awad +1 more
wiley +1 more source

