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A Mean Convolutional Layer for Intrusion Detection System
Joint Authors
Mohammadpour, Leila
Ling, T.C.
Liew, C.S.
Aryanfar, Alihossein
Source
Security and Communication Networks
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-24
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Mohammadpour, Leila& Ling, T.C.& Liew, C.S.& Aryanfar, Alihossein. 2020. A Mean Convolutional Layer for Intrusion Detection System. Security and Communication Networks،Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1208889
Modern Language Association (MLA)
Mohammadpour, Leila…[et al.]. A Mean Convolutional Layer for Intrusion Detection System. Security and Communication Networks No. 2020 (2020), pp.1-16.
https://search.emarefa.net/detail/BIM-1208889
American Medical Association (AMA)
Mohammadpour, Leila& Ling, T.C.& Liew, C.S.& Aryanfar, Alihossein. A Mean Convolutional Layer for Intrusion Detection System. Security and Communication Networks. 2020. Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1208889
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208889