scholar.google.com › citations
Thus, this paper proposes a Machine Learning-driven methodology for multiclass classification of cyber-attacks in IoT networks and investigates the robustness ...
This paper presents an ML-based methodology for the multiclass classification of malicious network traffic in IoT networks and evaluated it under several well- ...
A Machine Learning-driven methodology for multiclass classification of cyber-attacks in IoT networks and investigates the robustness of the Machine and Deep ...
Pantelakis et al. [32] proposed an ML-based multiclass classification methodology for cyber-attack detection in IoT networks.
People also ask
What is an example of an adversarial attack in machine learning?
What are the classification of adversarial attacks?
Which of the following are adversarial attacks on machine learning systems?
What are adversarial attacks on intrusion detection systems?
The main goal of this research is to propose the Overlapping Label Recovery (OLR) framework to mitigate the effects of label-flipping attacks in Deep-Learning- ...
In this paper, we perform a comprehensive analysis, includ- ing 4 ML algorithms and 3 neural networks (NNs), and pro- pose a pipeline which analyzes the ...
Aug 9, 2024 · This article examines the severity of adversarial attacks and accentuates the importance of designing secure and robust ML models in the IoT context.
Re- garding cyber-attacks targeting IoT systems, realistic adversarial examples must be valid traffic capable of being transmitted through a communication ...
Jul 9, 2024 · The proposed method demonstrates high accuracy in real-time intrusion detection of IoT attacks, outperforming existing approaches.
Anomaly detection using deep convolutional generative ...
www.sciencedirect.com › article › abs › pii
In this paper, we propose a weighted stacked ensemble model combining deep convolutional generative adversarial and bidirectional long short-term memory ...