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Anomaly detection by using hybrid method
Other Title(s)
كشف المتطفلين باستخدام طريقة هجينة
Author
Abd al-Khaliq, Muhammad Husayn Ghalib
Source
al-Qadisiyah Journal for Computer Science and Mathematics
Issue
Vol. 9, Issue 1 (30 Jun. 2017), pp.99-107, 9 p.
Publisher
University of al-Qadisiyah College of computer Science and Information Technology
Publication Date
2017-06-30
Country of Publication
Iraq
No. of Pages
9
Main Subjects
Mathematics
Information Technology and Computer Science
Abstract EN
In this paper a new approach has been designed for Intrusion Detection System (IDS).
The detection will be for misuse and anomalies for training and testing data detecting the normal users or attacks users.
The method used in this research is a hybrid method from supervised learning and text recognition field for (IDS).
Random Forest algorithm used as a supervised learning method to choose the features and k-Nearest Neighbours is a text recognition algorithm used to detect and classify of the legitimate and illegitimate attack types.
The experimental results have shown that the most accurate results is that obtained by using the proposed method and proved that the proposed method can classify the unknown attacks.
The results obtained by using benchmark dataset which are: KDD Cup 1999 dataset.
American Psychological Association (APA)
Abd al-Khaliq, Muhammad Husayn Ghalib. 2017. Anomaly detection by using hybrid method. al-Qadisiyah Journal for Computer Science and Mathematics،Vol. 9, no. 1, pp.99-107.
https://search.emarefa.net/detail/BIM-787376
Modern Language Association (MLA)
Abd al-Khaliq, Muhammad Husayn Ghalib. Anomaly detection by using hybrid method. al-Qadisiyah Journal for Computer Science and Mathematics Vol. 9, no. 1 (2017), pp.99-107.
https://search.emarefa.net/detail/BIM-787376
American Medical Association (AMA)
Abd al-Khaliq, Muhammad Husayn Ghalib. Anomaly detection by using hybrid method. al-Qadisiyah Journal for Computer Science and Mathematics. 2017. Vol. 9, no. 1, pp.99-107.
https://search.emarefa.net/detail/BIM-787376
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 105-106
Record ID
BIM-787376