NDLI: Network anomaly detection using deep learning techniques

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Network anomaly detection using deep learning techniques

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Mohammad Kazim Hooshmand Doreswamy Hosahalli
Abstract	Abstract Convolutional neural networks (CNNs) are the specific architecture of feed‐forward artificial neural networks. It is the de‐facto standard for various operations in machine learning and computer vision. To transform this performance towards the task of network anomaly detection in cyber‐security, this study proposes a model using one‐dimensional CNN architecture. The authors' approach divides network traffic data into transmission control protocol (TCP), user datagram protocol (UDP), and OTHER protocol categories in the first phase, then each category is treated independently. Before training the model, feature selection is performed using the Chi‐square technique, and then, over‐sampling is conducted using the synthetic minority over‐sampling technique to tackle a class imbalance problem. The authors' method yields the weighted average f‐score 0.85, 0.97, 0.86, and 0.78 for TCP, UDP, OTHER, and ALL categories, respectively. The model is tested on the UNSW‐NB15 dataset.
Related Links	https://doi.org/10.1049/cit2.12078
DOI	10.1049/cit2.12078
Journal	CAAI Transactions on Intelligence Technology
Issue Number	2
Volume Number	7
e-ISSN	24682322
Language	English
Publisher	Wiley
Publisher Date	2022-01-01
Publisher Place	United Kingdom
Access Restriction	Open
Subject Keyword	Computational Linguistics. Natural Language Processing Computer Software Telecommunication Traffic Computer Vision Feedforward Neural Nets Cellular Neural Nets Transport Protocols Neural Nets Electronic computers. Computer science Science Computer software
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Human-Computer Interaction Artificial Intelligence Information Systems Computer Vision and Pattern Recognition

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