Machine learning-based detection and mitigation of cyberattacks in adaptive cruise control systems

Nature·Oct 17

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Researchers are exploring the use of machine learning to detect and mitigate cyberattacks in various systems, including adaptive cruise control and smart grids. Multiple frameworks and mechanisms have been proposed, incorporating techniques such as artificial intelligence, federated learning, and explainable machine learning. These approaches aim to improve anomaly detection, attack forensics, and incident response in different environments, including 5G-SDN and IoT networks.

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Artificial intelligence driven multi agent framework for adaptive cyber attack simulation and automated incident response in cyber range environments

Nature·Apr 7

Prediction-based attack detection and mitigation mechanism in power system

Nature·Mar 12

Design of an AI-driven secure 5G-SDN framework with federated reinforcement learning for anomaly detection, mitigation, and attack forensics

Frontiers·Jan 19

Detection of disturbances and cyber-attacks in smart grids using explainable machine learning | Scientific Reports

Nature·Feb 19

An efficient federated learning based defense mechanism for software defined network cyber threats through machine learning models

Nature·Nov 21

SecuFL-IoT: an adaptive privacy-preserving federated learning framework for anomaly detection in smart industrial networks

Nature·Jan 29

Privacy preserving blockchain integrated explainable artificial intelligence with two tier optimization for cyber threat detection and mitigation in the internet of things

Nature·Oct 21

CyberDetect MLP a big data enabled optimized deep learning framework for scalable cyberattack detection in IoT environments

Nature·Nov 19

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