Network intrusion detection based on deep learning model optimized with rule-based hybrid feature selection

Network Intrusion Detection System (NIDS) is often used to classify network traffic in an attempt to protect computer systems from various network attacks. A major component for building an efficient intrusion detection system is the preprocessing of network traffic and identification of essential f...

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Bibliographic Details
Published inInformation security journal. Vol. 29; no. 6; pp. 267 - 283
Main Authors Ayo, Femi Emmanuel, Folorunso, Sakinat Oluwabukonla, Abayomi-Alli, Adebayo A., Adekunle, Adebola Olayinka, Awotunde, Joseph Bamidele
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 01.11.2020
Taylor & Francis Ltd
Subjects
Accuracy
Algorithms
Classifiers
Communications traffic
Computer simulation
Deep learning
Experimentation
False alarms
Feature selection
Intrusion detection systems
Machine learning
Network security
NIDS
rule learning
Testing time
UNSW-NB15 data
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Summary:Network Intrusion Detection System (NIDS) is often used to classify network traffic in an attempt to protect computer systems from various network attacks. A major component for building an efficient intrusion detection system is the preprocessing of network traffic and identification of essential features which is essential for building robust classifier. In this study, a NIDS based on deep learning model optimized with rule-based hybrid feature selection is proposed. The architecture is divided into three phases namely: hybrid feature selection, rule evaluation and detection. Several search methods and attribute evaluators were combined for features selection to enhance experimentation and comparison. The results obtained showed that the number of selected features will not affect the detection accuracy of the feature selection algorithms, but directly proportional to the performance of the base classifier. Results from the performance comparison proved that the proposed method outperforms other related methods with reduction of false alarm rate, high accuracy rate, reduced  training and testing time of 1.2%, 98.8%, 7.17s and 3.11s, respectively. Finally, the simulation experiments on standard evaluation metrics showed that the proposed method is suitable for attack classification in NIDS.
ISSN:1939-3555
1939-3547
DOI:10.1080/19393555.2020.1767240