Temporal Signature Mining for Network Intrusion Detection Using TEMR

Mawla, Tanjila; Dutta, Sharmishtha; Rabbi, Md. Forhad

doi:10.1007/978-981-13-1501-5_57

Cited by 2 publications

(1 citation statement)

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“…Combined with the two-stage classification technology of adaptive support vector machine (SVM) classification, malicious sensor nodes are reported, which minimizes time consumption and improves network lifetime and scalability. Abraham A et al (2019) proposed an intrusion detection model based on the framework of double sparse convolution matrix [15]. e model uses the close correlation of nonnegative matrix decomposition to identify the hidden pattern of events and obtains a high detection accuracy.…”

Section: Related Workmentioning

confidence: 99%

Network Intrusion Detection Method Based on Improved CNN in Internet of Things Environment

Wang

Jin

2022

Mobile Information Systems

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In view of most existing intrusion detection technologies that cannot meet the actual needs of the Internet of Things and facing the problems of poor detection effect of complex network intrusion methods, a network intrusion detection method based on deep learning algorithm in the environment of the Internet of Things is proposed. Firstly, the Internet of Things intrusion detection model is constructed based on edge computing, in which the concept of gated convolution is introduced to improve the convolution neural network model. Data passes through convolution layer, pooling, dropout, full connection, and Softmax function to realize multiclassification. Finally, the Focal Loss function is used to modulate the training ratio of positive and negative samples to solve the problem of uneven distribution of sample data. The proposed algorithm is demonstrated experimentally based on KDD99 data set. The results show that the accuracy, precision, recall, and F1 values are 92.14%, 95.97%, 90.89%, and 90.03%, which are better than other comparison algorithms. The proposed method can better meet the needs of Internet of Things intrusion detection.

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Section: Related Workmentioning

confidence: 99%