A hybrid XGBoost–SVM ensemble framework for robust cyber-attack detection in the internet of medical things (IoMT)

Nature·Jan 31

✦ NabkaNews BriefAuto-summarized from multiple outlets · verify with the source

Researchers are exploring various machine learning and artificial intelligence approaches to detect cyber-attacks in the internet of medical things (IoMT) and other related networks. These approaches include hybrid ensemble frameworks, deep learning, and optimization-based methods to enhance detection capabilities. The specific techniques and frameworks being developed vary, with some focusing on anomaly-based detection, proactive ransomware detection, and explainable AI for collaborative healthcare cybersecurity.

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Hybrid optimization-based quantum-driven multi-relational graph attention networks for enhanced cyber attack detection in medical IoT networks

Nature·Apr 24

A deep learning/machine learning approach for anomaly based network intrusion detection

Frontiers·Sep 8

Towards advanced AI-based solutions for securing IoMT in smart health information systems

Nature·Jan 7

Ensemble machine learning for proactive android ransomware detection using network traffic

Nature·Feb 18

FalsEye: proactive detection of false data injection attacks in smart grids using IceCube-optimised ensemble learning

Nature·Mar 14

Efficient detection of intrusions in TON-IoT dataset using hybrid feature selection approach

Nature·Feb 7

FedMedSecure: federated few-shot learning with cross-attention mechanisms and explainable AI for collaborative healthcare cybersecurity

Nature·Nov 14

A hybrid ensemble deep learning framework with novel metaheuristic optimization for scalable malicious website detection

Nature·Dec 24

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