Network Intrusion Detection Leveraging Machine Learning and Feature Selection

Handling superfluous and insignificant features in high-dimension data sets incidents led to a long-term demand for system anomaly detection. Ignoring such elements with spectral instruction not speeds up the analysis process but again facilitates classifiers to make accurate selections during attac...

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Bibliographic Details
Main Authors: Ali, Arshid, Shaukat, Shahtaj, Tayyab, Muhammad, Khan, Muazzam A, Khan, Jan Sher, Arshad, Ahmad, Jawad
Format: Conference Proceeding
Language:English
Subjects:
attack perception stage
CFS
Classifier subset evaluation
Correlation
Correlation-Based Feature (CFS)
correlation-based feature selection
Data models
Dimensionality reduction
Feature extraction
feature selection
heterogeneous data
high-dimension data set incidents
huge-scale data
IBK
IDS
instance-based learning algorithm
Instance-Based Learning algorithm (IBK)
Intrusion detection
intrusion detection system
Intrusion Detection System (IDS)
learning (artificial intelligence)
Machine learning algorithms
MLP
multilayer perceptron
Multilayer Perceptron (MLP)
multilayer perceptrons
naïve Bayes classifier techniques
network intrusion detection
Neural networks
pattern classification
receiver operating curve
ROC
security of data
spectral instruction
superfluous features
system anomaly detection
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Summary:Handling superfluous and insignificant features in high-dimension data sets incidents led to a long-term demand for system anomaly detection. Ignoring such elements with spectral instruction not speeds up the analysis process but again facilitates classifiers to make accurate selections during attack perception stage, when wrestling with huge-scale and heterogeneous data. In this paper, for dimensionality reduction of data, we use Correlation-based Feature Selection (CFS) and Naïve Bayes (NB) classifier techniques. The proposed Intrusion Detection System (IDS) classifies attacks using a Multilayer Perceptron (MLP) and Instance-Based Learning algorithm (IBK). The accuracy of the introduced IDS is 99.87% and 99.82% with only 5 and 3 features out of 78 features for IBK. Other metrics such as precision, Recall, F-measure, and Receiver Operating Curve (ROC) also confirm the principal performance of IBK compared to MLP.
ISSN:1949-4106
DOI:10.1109/HONET50430.2020.9322813