2021
DOI: 10.1155/2021/3697536
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Network Threat Detection Based on Group CNN for Privacy Protection

Abstract: The Internet of Things (IoT) contains a large amount of data, which attracts various types of network attacks that lead to privacy leaks. With the upgrading of network attacks and the increase in network security data, traditional machine learning methods are no longer suitable for network threat detection. At the same time, data analysis techniques and deep learning algorithms have developed rapidly and have been successfully applied to a variety of tasks for privacy protection. Convolutional neural networks … Show more

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Cited by 9 publications
(3 citation statements)
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“…Among these DL models, CNN is considered a highly effective and efficient model. Generally due to its ability to reconstruct features and learn in-depth patterns from images [27]. Table 1 represents a summary of recent and prominent publications in the domain of image processing-based NIDS.…”
Section: Related Workmentioning
confidence: 99%
“…Among these DL models, CNN is considered a highly effective and efficient model. Generally due to its ability to reconstruct features and learn in-depth patterns from images [27]. Table 1 represents a summary of recent and prominent publications in the domain of image processing-based NIDS.…”
Section: Related Workmentioning
confidence: 99%
“…Literature [13] studied and analyzed the threats of IoT and used an artificial neural network-based defense method in order to combat these threats, carried out simulation experiments to validate the method and found that the accuracy of the method reaches 99.4%, which is capable of detecting DDOS/DOS. Literature [14] suggests using CNN models that are based on feature correlation to learn features and reconstruct security data. The proposed models have demonstrated better performance in simulation tests than other algorithmic models.…”
Section: Introductionmentioning
confidence: 99%
“…CNNs are gaining traction in several applications. These applications incorporate discerning menaces into a network [2]. Antecedent research has delved into how Graph Neural Ehsan Nowroozi is with the Centre for Secure Information Technologies (CSIT), Queen's University Belfast, Northern Ireland, United Kingdom (email: e.nowroozi@qub.ac.uk).…”
Section: Introductionmentioning
confidence: 99%