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Our research work focuses on the detection of anomalous network traffic or intruders by means of deep learning methods from flow-based data.
As a new management plane of the SDN, once it is attacked, it will cause the entire network to face flaws. For this reason, this paper proposes a SDN control ...
Our research work focuses on the detection of anomalous network traffic or intruders by means of deep learning methods from flow-based data. We have utilized ...
This study designs and enhances a novel anomaly-based intrusion detection system (AIDS) for IoT networks.
Aug 20, 2024 · This study designs and enhances a novel anomaly-based intrusion detection system (AIDS) for IoT networks. Firstly, a Sparse Autoencoder (SAE) is ...
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This review has explored seven deep learning techniques practiced in IoT security, and the results showed their effectiveness in dealing with security ...
This paper presents a CNN-based approach for anomaly-based intrusion detection systems (IDS) that takes advantage of IoT's power.
Machine learning algorithms can use anomaly-based intrusion detection techniques to track active behavior and compare them with established intrusion footprints ...
Jul 9, 2024 · In deep learning-based intrusion and anomaly detection systems, deep networks learn from historical traffic data and classify data behavior into ...
Sep 29, 2023 · This paper presents a model for intrusion detection in the IoT based on edge computing. The model utilizes gated convolution to improve the performance of the ...