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-2023-0257

Malicious code in bioql PyPI...

EUVD-2024-0168

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EUVD-2024-0171

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EUVD-2023-0260

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EUVD-2023-0261

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EUVD-2023-0262

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EUVD-2023-0258

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EUVD-2024-0829

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EUVD-2023-0256

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EUVD-2024-0813

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EUVD-2024-0169

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A Lightweight Federated Learning Approach for Privacy-Preserving Botnet Detection in IoT

The rapid growth of the Internet of Things IoT has expanded opportunities for innovation but also increased exposure to botnet-driven cyberattacks. Conventional detection methods often struggle with scalability, privacy, and adaptability in resource-constrained IoT environments. To address these...

Federated Spatiotemporal Graph Learning for Passive Attack Detection in Smart Grids

Smart grids are exposed to passive eavesdropping, where attackers listen silently to communication links. Although no data is actively altered, such reconnaissance can reveal grid topology, consumption patterns, and operational behavior, creating a gateway to more severe targeted attacks. Detecti...

AntiFLipper: A Secure and Efficient Defense against Label-Flipping Attacks in Federated Learning

Federated learning FL enables privacy-preserving model training by keeping data decentralized. However, it remains vulnerable to label-flipping attacks, where malicious clients manipulate labels to poison the global model. Despite their simplicity, these attacks can severely degrade model...