Proposed hybrid classifier to improve network intrusion detection system using data mining techniques
Joint Authors
Sharif, Sarah Muhammad
Hashim, Sukaynah Hasan
Source
Engineering and Technology Journal
Issue
Vol. 38, Issue 1B (31 Jan. 2020), pp.6-14, 9 p.
Publisher
Publication Date
2020-01-31
Country of Publication
Iraq
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
Network intrusion detection system (nids) is a software system which plays an important role to protect network system and can be used to monitor network activities to detect different kinds of attacks from normal behavior in network traffics.
a false alarm is one of the most identified problems in relation to the intrusion detection system which can be a limiting factor for the performance and accuracy of the intrusion detection system.
the proposed system involves mining techniques at two sequential levels, which are: at the first level naïve bayes algorithm is used to detect abnormal activity from normal behavior.
the second level is the multinomial logistic regression algorithm of which is used to classify abnormal activity into main four attack types in addition to a normal class.
to evaluate the proposed system, the kddcup99 dataset of the intrusion detection system was used and k-fold cross-validation was performed.
the experimental results show that the performance of the proposed system is improved with less false alarm rate.
American Psychological Association (APA)
Sharif, Sarah Muhammad& Hashim, Sukaynah Hasan. 2020. Proposed hybrid classifier to improve network intrusion detection system using data mining techniques. Engineering and Technology Journal،Vol. 38, no. 1B, pp.6-14.
https://search.emarefa.net/detail/BIM-1283380
Modern Language Association (MLA)
Sharif, Sarah Muhammad& Hashim, Sukaynah Hasan. Proposed hybrid classifier to improve network intrusion detection system using data mining techniques. Engineering and Technology Journal Vol. 38, no. 1B (2020), pp.6-14.
https://search.emarefa.net/detail/BIM-1283380
American Medical Association (AMA)
Sharif, Sarah Muhammad& Hashim, Sukaynah Hasan. Proposed hybrid classifier to improve network intrusion detection system using data mining techniques. Engineering and Technology Journal. 2020. Vol. 38, no. 1B, pp.6-14.
https://search.emarefa.net/detail/BIM-1283380
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 13-14
Record ID
BIM-1283380