A lightweight neural classifier for intrusion detection
Joint Authors
Guezzaz, Azidine
Asimi, Ahmad
Asimi, Yunus
Tbatw, Zakariyya
Sidqi, Yasin
Source
General Letters in Mathematics
Issue
Vol. 2, Issue 2 (30 Apr. 2017), pp.57-66, 10 p.
Publisher
Refaad Center for Studies and Research
Publication Date
2017-04-30
Country of Publication
Jordan
No. of Pages
10
Main Subjects
Engineering & Technology Sciences (Multidisciplinary)
Abstract EN
Intrusion detection and prevention is a set of techniques that try to detect attacks as they occur or after the attacks took place.
There are two recent and useful approaches to detect intrusions: misuse and anomaly.
They collect network trac activities from some points on the network or computer system and then use them to secure the network using one or both of the available detection methods.
The IDPS su er major vulnerabilities with large generation of false positives and negatives.
The anomaly detection aims to specify behavior detection problems that require modeling of pro le preliminary.
This paper describes a new approach of intrusion detection based on speci ed pro le built from training basis using a database that contains normal activities collected within monitored network.
The modeling of pro le represents a real challenge for network administrators and computer security researchers.
Our main goal is in the rst hand, to present an application of multilayer perceptron to make a monitored system, in the second hand, to build a classi er for trac events.
A supervised algorithm is suggested and used in training.
The recognition phase aims to validate the new classi er.
Our classi er is able to distinct between normal activity and intrusion.
We describe in details our novel detection approach and we validate the proposed solutions.
We demonstrated that this novel approach is robust, exible and gives useful performances using multilayer perceptron.
American Psychological Association (APA)
Guezzaz, Azidine& Asimi, Ahmad& Asimi, Yunus& Tbatw, Zakariyya& Sidqi, Yasin. 2017. A lightweight neural classifier for intrusion detection. General Letters in Mathematics،Vol. 2, no. 2, pp.57-66.
https://search.emarefa.net/detail/BIM-937833
Modern Language Association (MLA)
Guezzaz, Azidine…[et al.]. A lightweight neural classifier for intrusion detection. General Letters in Mathematics Vol. 2, no. 2 (Apr. 2017), pp.57-66.
https://search.emarefa.net/detail/BIM-937833
American Medical Association (AMA)
Guezzaz, Azidine& Asimi, Ahmad& Asimi, Yunus& Tbatw, Zakariyya& Sidqi, Yasin. A lightweight neural classifier for intrusion detection. General Letters in Mathematics. 2017. Vol. 2, no. 2, pp.57-66.
https://search.emarefa.net/detail/BIM-937833
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 65-66
Record ID
BIM-937833