Comparison of Machine Learning Techniques For Intrusion Detection System
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Date
2018-10
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Abstract
The rapid growth in the ubiquity and sophistication of Information Communication
Technology (ICT) and the emergence of new networking paradigms such as Cloud
Computing (CC), and Internet of Things (IoT) have made vital changes in the globe.
Computer network security is one of the most critical issue as attackers are also evolving
dynamically. There should be a mechanism that fill the security vulnerability. One of the
promising technique to ensure computer network security is the use of hybrid machine
learning (ML) techniques which automate the process of intrusion detection in computer
networks.
In this research, six hybrid ML models were developed based on the Knowledge
Discovery in Database (KDD) process model. The dataset used in this study has been
taken from University of New Brunswick Institute (Canada Institute of Cyber Security).
After selecting the dataset, preprocessing techniques such as filling missing records,
reduce dimension, selecting the most relevant features, and finally normalize the dataset
input using features scaling are performed. The hybrid ML models for intrusion detection
systems (IDS) are implemented using Python programming language.
In this work, a total of 274208 dataset records are used for the ML models evaluation. Out
of this, 191945 datasets are used for training and a separate 82263 records are used as a
testing set. The decision tree (DT) and neural network (NN) algorithms as supervised and
K-means algorithm as unsupervised algorithms are applied in both without feature
selection and with feature selection.
The principal component analysis and decision tree (PCA-DT) model showed the best
results in all performance parameters. The model has a prediction accuracy of 99.89% and the lowest false positive rate of 0.027%. Results confirm the effectiveness of our proposed
methods.
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Keywords
Intrusion Detection, Machine learning algorithms, Computer Security