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References from Towards decentralized cybersecurity: A novel privacy-preserving federated learning approach for botnet attack detection. Local targets link to admitted publications; unresolved targets remain external evidence.
Examination of traditional botnet detection on IoT-based bots
10.3390/s24031027 · 2024 · External reference
A novel hybrid feature selection and ensemble-based machine learning approach for botnet detection
10.1038/s41598-023-48230-1 · 2023 · External reference
Classifying IoT botnet attacks with Kolmogorov-Arnold networks: a comparative analysis of architectural variations
10.1109/access.2025.3528940 · 2025 · External reference
An ensemble-based machine learning approach for botnet-based DDoS attack detection
2023 · External reference
Enhancing DDoS attack detection with hybrid feature selection and ensemble-based classifier: a promising solution for robust cybersecurity
2024 · External reference
A decentralized approach to threat intelligence using federated learning in privacy-preserving cyber security
10.52783/jes.658 · 2023 · External reference
A novel feature selection-driven ensemble learning approach for accurate botnet attack detection
10.1016/j.aej.2025.01.042 · 2025 · External reference
Privacy-preserving federated learning cyber-threat detection for intelligent transport systems with blockchain-based security
10.1111/exsy.13103 · 2023 · External reference
Advancing IoT security with an innovative machine learning paradigm for botnet attack detection
2025 · External reference
IoT security fortification: enhancing cyber threat detection through feature selection and advanced machine learning
2024 · External reference
Deep learning and ensemble methods for anomaly detection in ICS security
2025 · External reference
Double DQN method for botnet traffic detection system
10.32604/cmc.2024.042216 · 2024 · External reference
Interpretable machine learning for IoT security: feature selection and explainability in botnet intrusion detection using extra trees classifier
2024 · External reference
An integrated framework for privacy-preserving based anomaly detection for cyber-physical systems
10.1109/tsusc.2019.2906657 · 2021 · External reference
A comparative study of machine learning algorithms for intrusion detection in IoT networks
2023 · External reference
Intelligent detection of distributed denial of service attacks: a supervised machine learning and ensemble approach
2023 · External reference
Performance evaluation of machine learning classifiers for predicting denial-of-service attack in Internet of Things
2024 · External reference
Enhancing IoT security: effective botnet attack detection through machine learning
10.1016/j.procs.2024.08.058 · 2024 · External reference
Botnet attacks detection in IoT environment using machine learning techniques
10.5267/j.ijdns.2023.7.021 · 2023 · External reference
Comparison of convolutional neural networks, long short-term memory networks, and recurrent neural networks for intrusion detection system
2024 · External reference
Botnets attack detection using bio-inspired deep learning techniques in Internet of medical things (IoMT)
10.1002/spy2.493 · 2025 · External reference
Memory-efficient deep learning for botnet attack detection in IoT networks
10.3390/electronics10091104 · 2021 · External reference
An optimized approach to deep learning for botnet detection and classification for cybersecurity in Internet of Things environment
2024 · External reference
Improved botnet attack detection using principal component analysis and ensemble voting algorithm
2021 · External reference
Machine learning and metaheuristic optimization algorithms for feature selection and botnet attack detection
10.1007/s10115-024-02322-0 · 2025 · External reference
Hybrid bagging and boosting with SHAP based feature selection for enhanced predictive modeling in intrusion detection systems
10.1038/s41598-024-81151-1 · 2024 · External reference
Enabling efficient and low-effort decentralized federated learning with the EdgeFL framework
10.1016/j.infsof.2024.107600 · 2025 · External reference
Federated deep learning for zero-day botnet attack detection in IoT-edge devices
10.1109/jiot.2021.3100755 · 2021 · External reference
IDAC: federated learning-based intrusion detection using autonomously extracted anomalies in IoT
10.3390/s24103218 · 2024 · External reference
Enhancing intrusion detection in IoT networks through federated learning
10.1109/access.2024.3495702 · 2024 · External reference
Unresolved reference
2023 · External reference
Federated quantum-based privacy-preserving threat detection model for consumer Internet of Things
10.1109/tce.2024.3377550 · 2024 · External reference
Privacy-preserving blockchain-based federated learning for IoT devices
10.1109/jiot.2020.3017377 · 2021 · External reference
Personalized federated learning-based intrusion detection system: Poisoning attack and defense
10.1016/j.future.2023.10.005 · 2024 · External reference
Exploring homomorphic encryption and differential privacy techniques towards secure federated learning paradigm
10.3390/fi15090310 · 2023 · External reference
Integrating explainable AI with federated learning for next-generation IoT: a comprehensive review and prospective insights
10.1016/j.cosrev.2024.100697 · 2025 · External reference
MedBIoT: generation of an IoT botnet dataset in a medium-sized IoT network
2020 · External reference
I-MPaFS: enhancing EDoS attack detection in cloud computing through a data-driven approach
10.1186/s13677-024-00699-5 · 2024 · External reference
Unresolved reference
2020 · External reference
Comparing machine learning and deep learning for IoT botnet detection
2021 · External reference
Using MedBIoT dataset to build effective machine learning-based IoT botnet detection systems
2020 · External reference
IoT botnet attack detection using deep autoencoder and artificial neural networks
2023 · External reference
In-depth feature selection for the statistical machine learning-based botnet detection in IoT networks
10.1109/access.2022.3204001 · 2022 · External reference
Lightweight Internet of Things botnet detection using one-class classification
10.3390/s22103646 · 2022 · External reference
