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Convolution Neural Network-Based Higher Accurate Intrusion Identification System for the Network Security and Communication
Joint Authors
Hong, Cheng
Khan, Sulaiman
Nazir, Shah
Gu, Zhiwei
Source
Security and Communication Networks
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-28
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
With the development of communication systems, information securities remain one of the main concerns for the last few years.
The smart devices are connected to communicate, process, compute, and monitor diverse real-time scenarios.
Intruders are trying to attack the network and capture the organization’s important information for its own benefits.
Intrusion detection is a way of identifying security violations and examining unwanted occurrences in a computer network.
Building an accurate and effective identification system for intrusion detection or malicious activities can secure the existing system for smooth and secure end-to-end communication.
In the proposed research work, a deep learning-based approach is followed for the accurate intrusion detection purposes to ensure the high security of the network.
A convolution neural network based approach is followed for the feature classification and malicious data identification purposes.
In the end, comparative results are generated after evaluating the performance of the proposed algorithm to other rival algorithms in the proposed field.
These comparative algorithms were FGSM, JSMA, C&W, and ENM.
After evaluating the performance of these algorithms and the proposed algorithm based on different threshold values ranging, Lp norms, and different parametric values for c, it was concluded that the proposed algorithm outperforms with small Lp values and high Kitsune scores.
These results reflect that the proposed research is promising toward the identification of attack on data packets, and it also reflects the applicability of the proposed algorithms in the network security field.
American Psychological Association (APA)
Gu, Zhiwei& Nazir, Shah& Hong, Cheng& Khan, Sulaiman. 2020. Convolution Neural Network-Based Higher Accurate Intrusion Identification System for the Network Security and Communication. Security and Communication Networks،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1208629
Modern Language Association (MLA)
Gu, Zhiwei…[et al.]. Convolution Neural Network-Based Higher Accurate Intrusion Identification System for the Network Security and Communication. Security and Communication Networks No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1208629
American Medical Association (AMA)
Gu, Zhiwei& Nazir, Shah& Hong, Cheng& Khan, Sulaiman. Convolution Neural Network-Based Higher Accurate Intrusion Identification System for the Network Security and Communication. Security and Communication Networks. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1208629
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208629