280 matches found
Federated Anomaly Detection and Mitigation for EV Charging Forecasting under Cyberattacks
Electric Vehicle EV charging infrastructure faces escalating cybersecurity threats that can severely compromise operational efficiency and grid stability. Existing forecasting techniques are limited by the lack of combined robust anomaly mitigation solutions and data privacy preservation...
Trustworthy GenAI over 6G: Integrated Applications and Security Frameworks
The integration of generative artificial intelligence GenAI into 6G networks promises substantial performance gains while simultaneously exposing novel security vulnerabilities rooted in multimodal data processing and autonomous reasoning. This article presents a unified perspective on cross-doma...
Scalable Hierarchical AI-Blockchain Framework for Real-Time Anomaly Detection in Large-Scale Autonomous Vehicle Networks
The security of autonomous vehicle networks is facing major challenges, owing to the complexity of sensor integration, real-time performance demands, and distributed communication protocols that expose vast attack surfaces around both individual and network-wide safety. Existing security schemes...
Data Poisoning Vulnerabilities across Healthcare AI Architectures: A Security Threat Analysis
Healthcare AI systems face major vulnerabilities to data poisoning that current defenses and regulations cannot adequately address. We analyzed eight attack scenarios in four categories: architectural attacks on convolutional neural networks, large language models, and reinforcement learning...
Federated Cyber Defense: Privacy-Preserving Ransomware Detection across Distributed Systems
Detecting malware, especially ransomware, is essential to securing today's interconnected ecosystems, including cloud storage, enterprise file-sharing, and database services. Training high-performing artificial intelligence AI detectors requires diverse datasets, which are often distributed acros...
OptiFLIDS: Optimized Federated Learning for Energy-Efficient Intrusion Detection in IoT
In critical IoT environments, such as smart homes and industrial systems, effective Intrusion Detection Systems IDS are essential for ensuring security. However, developing robust IDS solutions remains a significant challenge. Traditional machine learning-based IDS models typically require large...
EUVD-2023-0259
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EUVD-2024-0171
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EUVD-2023-0261
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EUVD-2024-0169
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EUVD-2024-0829
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EUVD-2024-0813
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EUVD-2023-0257
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EUVD-2023-0260
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EUVD-2023-0262
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EUVD-2023-0255
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EUVD-2024-0168
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EUVD-2024-0170
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