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A mean convolutional layer for intrusion detection system

The significant development of Internet applications over the past 10 years has resulted in the rising necessity for the information network to be secured. An intrusion detection system is a fundamental network infrastructure defense that must be able to adapt to the ever-evolving threat landscape a...

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Main Authors: Mohammadpour, Leila, Ling, Teck Chaw, Liew, Chee Sun, Aryanfar, Alihossein
Format: Article
Published: Wiley 2020
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.um.edu.my/36325/
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spelling my.um.eprints.363252023-10-06T01:56:08Z http://eprints.um.edu.my/36325/ A mean convolutional layer for intrusion detection system Mohammadpour, Leila Ling, Teck Chaw Liew, Chee Sun Aryanfar, Alihossein QA75 Electronic computers. Computer science The significant development of Internet applications over the past 10 years has resulted in the rising necessity for the information network to be secured. An intrusion detection system is a fundamental network infrastructure defense that must be able to adapt to the ever-evolving threat landscape and identify new attacks that have low false alarm. Researchers have developed several supervised as well as unsupervised methods from the data mining and machine learning disciplines so that anomalies can be detected reliably. As an aspect of machine learning, deep learning uses a neuron-like structure to learn tasks. A successful deep learning technique method is convolution neural network (CNN); however, it is presently not suitable to detect anomalies. It is easier to identify expected contents within the input flow in CNNs, whereas there are minor differences in the abnormalities compared to the normal content. This suggests that a particular method is required for identifying such minor changes. It is expected that CNNs would learn the features that form the characteristic of the content of an image (flow) rather than variations that are unrelated to the content. Hence, this study recommends a new CNN architecture type known as mean convolution layer (CNN-MCL) that was developed for learning the anomalies' content features and then identifying the particular abnormality. The recommended CNN-MCL helps in designing a strong network intrusion detection system that includes an innovative form of convolutional layer that can teach low-level abnormal characteristics. It was observed that assessing the proposed model on the CICIDS2017 dataset led to favorable results in terms of real-world application regarding detecting anomalies that are highly accurate and have low false-alarm rate as opposed to other best models. Wiley 2020-10 Article PeerReviewed Mohammadpour, Leila and Ling, Teck Chaw and Liew, Chee Sun and Aryanfar, Alihossein (2020) A mean convolutional layer for intrusion detection system. Security and Communication Networks, 2020. ISSN 1939-0122, DOI https://doi.org/10.1155/2020/8891185 <https://doi.org/10.1155/2020/8891185>. 10.1155/2020/8891185
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Mohammadpour, Leila
Ling, Teck Chaw
Liew, Chee Sun
Aryanfar, Alihossein
A mean convolutional layer for intrusion detection system
description The significant development of Internet applications over the past 10 years has resulted in the rising necessity for the information network to be secured. An intrusion detection system is a fundamental network infrastructure defense that must be able to adapt to the ever-evolving threat landscape and identify new attacks that have low false alarm. Researchers have developed several supervised as well as unsupervised methods from the data mining and machine learning disciplines so that anomalies can be detected reliably. As an aspect of machine learning, deep learning uses a neuron-like structure to learn tasks. A successful deep learning technique method is convolution neural network (CNN); however, it is presently not suitable to detect anomalies. It is easier to identify expected contents within the input flow in CNNs, whereas there are minor differences in the abnormalities compared to the normal content. This suggests that a particular method is required for identifying such minor changes. It is expected that CNNs would learn the features that form the characteristic of the content of an image (flow) rather than variations that are unrelated to the content. Hence, this study recommends a new CNN architecture type known as mean convolution layer (CNN-MCL) that was developed for learning the anomalies' content features and then identifying the particular abnormality. The recommended CNN-MCL helps in designing a strong network intrusion detection system that includes an innovative form of convolutional layer that can teach low-level abnormal characteristics. It was observed that assessing the proposed model on the CICIDS2017 dataset led to favorable results in terms of real-world application regarding detecting anomalies that are highly accurate and have low false-alarm rate as opposed to other best models.
format Article
author Mohammadpour, Leila
Ling, Teck Chaw
Liew, Chee Sun
Aryanfar, Alihossein
author_facet Mohammadpour, Leila
Ling, Teck Chaw
Liew, Chee Sun
Aryanfar, Alihossein
author_sort Mohammadpour, Leila
title A mean convolutional layer for intrusion detection system
title_short A mean convolutional layer for intrusion detection system
title_full A mean convolutional layer for intrusion detection system
title_fullStr A mean convolutional layer for intrusion detection system
title_full_unstemmed A mean convolutional layer for intrusion detection system
title_sort mean convolutional layer for intrusion detection system
publisher Wiley
publishDate 2020
url http://eprints.um.edu.my/36325/
_version_ 1781704495973007360
score 13.222552

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