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Neural Network-Based Voting System with High Capacity and Low Computation for Intrusion Detection in SIEMIDS Systems
Joint Authors
Moukafih, Nabil
Orhanou, Ghizlane
El Hajji, Said
Source
Security and Communication Networks
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-16
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Information Technology and Computer Science
Abstract EN
Integrating intelligence into intrusion detection tools has received much attention in the last years.
The goal is to improve the detection capability within SIEM and IDS systems in order to cope with the increasing number of attacks using sophisticated and complex methods to infiltrate systems.
Current SIEM and IDS systems have many processes involved, which work together to collect, analyze, detect, and send notification of failures in real time.
Event normalization, for example, requires significant processing power to handle network events.
So, adding heavy deep learning models will invoke additional resources for the SIEM or IDS tool.
This paper presents a majority system based on reliability approach that combines simple feedforward neural networks, as weak learners, and produces high detection capability with low computation resources.
The experimental results show that the model is very suitable for modeling a classification model with high accuracy and that its performance is superior to that of complex resource-intensive deep learning models.
American Psychological Association (APA)
Moukafih, Nabil& Orhanou, Ghizlane& El Hajji, Said. 2020. Neural Network-Based Voting System with High Capacity and Low Computation for Intrusion Detection in SIEMIDS Systems. Security and Communication Networks،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1208393
Modern Language Association (MLA)
Moukafih, Nabil…[et al.]. Neural Network-Based Voting System with High Capacity and Low Computation for Intrusion Detection in SIEMIDS Systems. Security and Communication Networks No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1208393
American Medical Association (AMA)
Moukafih, Nabil& Orhanou, Ghizlane& El Hajji, Said. Neural Network-Based Voting System with High Capacity and Low Computation for Intrusion Detection in SIEMIDS Systems. Security and Communication Networks. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1208393
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208393