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Intrusion detection model using naive Bayes and deep learning technique
Joint Authors
Tabash, Muhammad
Tawfiq, Bella
Abd Allah, Muhammad
Source
The International Arab Journal of Information Technology
Issue
Vol. 17, Issue 2 (31 Mar. 2020), pp.215-224, 10 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2020-03-31
Country of Publication
Jordan
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
The increase of security threats and hacking the computer networks are one of the most dangerous issues should treat in these days.
Intrusion Detection Systems (IDSs), are the most appropriate methods to prevent and detect the attacks of networks and computer systems.
This study presents several techniques to discover network anomalies using data mining tasks, Machine learning technology and dependence of artificial intelligence techniques.
In this research, the smart hybrid model was developed to explore any penetrations inside the network.
The model divides into two basic stages.
The first stage includes the Genetic Algorithm (GA) in selecting the characteristics with depends on a process of extracting, Discretize And dimensionality reduction through Proportional K-Interval Discretization (PKID) and Fisher Linear Discriminant Analysis (FLDA) on respectively.
At the end of the first stage combining Naïve Bayes classifier (NB) and Decision Table (DT) using NSL-KDD data set divided into two separate groups for training and testing.
The second stage completely depends on the first stage outputs (predicted class) and reclassified with multilayer perceptrons using Deep Learning4J (DL) and the use of algorithm Stochastic Gradient Descent (SGD).
In order to improve the performance in terms of the accuracy in classification of penetrations, raising the average of discovering and reducing the false alarms.
The comparison of the proposed model and conventional models show the superiority of the proposed model and the previous conventional hybrid models.
The result of the proposed model is 99.9325 of classification accuracy, the rate of detection is 99.9738 and 0.00093 of false alarms.
American Psychological Association (APA)
Tabash, Muhammad& Abd Allah, Muhammad& Tawfiq, Bella. 2020. Intrusion detection model using naive Bayes and deep learning technique. The International Arab Journal of Information Technology،Vol. 17, no. 2, pp.215-224.
https://search.emarefa.net/detail/BIM-954655
Modern Language Association (MLA)
Tabash, Muhammad…[et al.]. Intrusion detection model using naive Bayes and deep learning technique. The International Arab Journal of Information Technology Vol. 17, no. 2 (Mar. 2020), pp.215-224.
https://search.emarefa.net/detail/BIM-954655
American Medical Association (AMA)
Tabash, Muhammad& Abd Allah, Muhammad& Tawfiq, Bella. Intrusion detection model using naive Bayes and deep learning technique. The International Arab Journal of Information Technology. 2020. Vol. 17, no. 2, pp.215-224.
https://search.emarefa.net/detail/BIM-954655
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 222-224
Record ID
BIM-954655