Advancing Machine Learning Strategies for Power Consumption-Based IoT Botnet Detection. [PDF]
Wakili AA +4 more
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Robust and Lightweight Federated Learning for NB-IoT Security: A Blockchain-Verified CNN-RNN Approach. [PDF]
Özmen G, Yiltas-Kaplan D.
europepmc +1 more source
A Survey of Emerging DDoS Threats in New Power Systems. [PDF]
Luo F, Fan S, Shao G.
europepmc +1 more source
Correction: Okey et al. BoostedEnML: Efficient Technique for Detecting Cyberattacks in IoT Systems Using Boosted Ensemble Machine Learning. <i>Sensors</i> 2022, <i>22</i>, 7409. [PDF]
Okey OD +6 more
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FORT-IDS: a federated, optimized, robust and trustworthy intrusion detection system for IIoT security. [PDF]
Mazroa AA.
europepmc +1 more source
Scalable architecture for autonomous malware detection and defense in software-defined networks using federated learning approaches. [PDF]
Ranpara R +3 more
europepmc +1 more source
Enhanced SqueezeNet model for detecting IoT-Bot attacks: A comprehensive approach. [PDF]
Bojarajulu B, Tanwar S, Singh TP.
europepmc +1 more source
Botnet detection in internet of things using stacked ensemble learning model. [PDF]
Ali M +6 more
europepmc +1 more source
Hyperparameter optimization of XGBoost and hybrid CnnSVM for cyber threat detection using modified Harris hawks algorithm. [PDF]
Elwahsh H +7 more
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Systematic Evaluation of Machine Learning and Deep Learning Models for IoT Malware Detection Across Ransomware, Rootkit, Spyware, Trojan, Botnet, Worm, Virus, and Keylogger. [PDF]
Maghanaki M +3 more
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