FI-PCA for IoT Network Intrusion Detection

Intrusion detection systems (IDS) protect networks by continuously monitoring data flow and taking immediate action when anomalies are detected. However, due to redundancy and significant network data correlation, classical IDS have shortcomings such as poor detection rates and high computational co...

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Bibliographic Details
Published in2022 International Symposium on Networks, Computers and Communications (ISNCC) pp. 1 - 6
Main Authors Abdulkareem, Sulyman Age, Foh, Chuan Heng, Carrez, Francois, Moessner, Klaus
Format Conference Proceeding
LanguageEnglish
Published IEEE 19.07.2022
Subjects
Correlation
Ensemble Learning Classifier
Feature extraction
FI-PCA IoT
Machine Learning
Measurement
Network intrusion detection
PCA
Regression tree analysis
Termination of employment
Training
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Summary:Intrusion detection systems (IDS) protect networks by continuously monitoring data flow and taking immediate action when anomalies are detected. However, due to redundancy and significant network data correlation, classical IDS have shortcomings such as poor detection rates and high computational complexity. This paper proposes a novel feature selection and extraction technique (FI-PCA). Feature Importance (FI) and Principal Component Analysis (PCA) are used to preprocess the network dataset (PCA). FI identifies the most important features in the data, while PCA is used to reduce dimensionality and denoise the data. In order to detect anomalies, we employ three single classifiers: Decision Tree (DT), Naive Bayes and Logistic Regression. Preliminary results, however, show that these classifiers have achieved average classification metric scores. On this basis, we use the Stack Ensemble Learning Classifier (ELC) method of combining single classifiers to improve the classifier's performance further. Experimental results on varied feature dimensions of an IoT (Bot-IoT) dataset indicate that our proposed technique combined with the Stack ELC can maintain the same level of classification performance for reduced dataset features. A comparison of our result with state-of-the-art classifiers' classification performance shows that our classifier is superior in terms of accuracy and detection rate. At the same time, a remarkable decrease is recorded for both training and test time.
DOI:10.1109/ISNCC55209.2022.9851723