Adaptive Anomaly Detection Framework Model Objects in Cyberspace
Joint Authors
Aldhyani, Theyazn H. H
Al-Yaari, Mohammed
Alkahtani, Hasan
Source
Applied Bionics and Biomechanics
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-10
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Telecommunication has registered strong and rapid growth in the past decade.
Accordingly, the monitoring of computers and networks is too complicated for network administrators.
Hence, network security represents one of the biggest serious challenges that can be faced by network security communities.
Taking into consideration the fact that e-banking, e-commerce, and business data will be shared on the computer network, these data may face a threat from intrusion.
The purpose of this research is to propose a methodology that will lead to a high level and sustainable protection against cyberattacks.
In particular, an adaptive anomaly detection framework model was developed using deep and machine learning algorithms to manage automatically-configured application-level firewalls.
The standard network datasets were used to evaluate the proposed model which is designed for improving the cybersecurity system.
The deep learning based on Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) and machine learning algorithms namely Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) algorithms were implemented to classify the Denial-of-Service attack (DoS) and Distributed Denial-of-Service (DDoS) attacks.
The information gain method was applied to select the relevant features from the network dataset.
These network features were significant to improve the classification algorithm.
The system was used to classify DoS and DDoS attacks in four stand datasets namely KDD cup 199, NSL-KDD, ISCX, and ICI-ID2017.
The empirical results indicate that the deep learning based on the LSTM-RNN algorithm has obtained the highest accuracy.
The proposed system based on the LSTM-RNN algorithm produced the highest testing accuracy rate of 99.51% and 99.91% with respect to KDD Cup’99, NSL-KDD, ISCX, and ICI-Id2017 datasets, respectively.
A comparative result analysis between the machine learning algorithms, namely SVM and KNN, and the deep learning algorithms based on the LSTM-RNN model is presented.
Finally, it is concluded that the LSTM-RNN model is efficient and effective to improve the cybersecurity system for detecting anomaly-based cybersecurity.
American Psychological Association (APA)
Alkahtani, Hasan& Aldhyani, Theyazn H. H& Al-Yaari, Mohammed. 2020. Adaptive Anomaly Detection Framework Model Objects in Cyberspace. Applied Bionics and Biomechanics،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1120137
Modern Language Association (MLA)
Alkahtani, Hasan…[et al.]. Adaptive Anomaly Detection Framework Model Objects in Cyberspace. Applied Bionics and Biomechanics No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1120137
American Medical Association (AMA)
Alkahtani, Hasan& Aldhyani, Theyazn H. H& Al-Yaari, Mohammed. Adaptive Anomaly Detection Framework Model Objects in Cyberspace. Applied Bionics and Biomechanics. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1120137
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1120137