Comparison deep learning method to traditional methods using for network intrusion detection

Recently, deep learning has gained prominence due to the potential it portends for machine learning. For this reason, deep learning techniques have been applied in many fields, such as recognizing some kinds of patterns or classification. Intrusion detection analyses got data from monitoring securit...

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Bibliographic Details
Published in2016 8th IEEE International Conference on Communication Software and Networks (ICCSN) pp. 581 - 585
Main Authors Bo Dong, Xue Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2016
Subjects
classifier machine learning
Communication networks
deep learning
Feature extraction
Hardware
Intrusion detection
Machine learning
network security
Software
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DOI10.1109/ICCSN.2016.7586590

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Summary:Recently, deep learning has gained prominence due to the potential it portends for machine learning. For this reason, deep learning techniques have been applied in many fields, such as recognizing some kinds of patterns or classification. Intrusion detection analyses got data from monitoring security events to get situation assessment of network. Lots of traditional machine learning method has been put forward to intrusion detection, but it is necessary to improvement the detection performance and accuracy. This paper discusses different methods which were used to classify network traffic. We decided to use different methods on open data set and did experiment with these methods to find out a best way to intrusion detection.
DOI:10.1109/ICCSN.2016.7586590