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HYBRID FEATURE SELECTION IN NETWORK INTRUSION DETECTION USING DECISION TREE

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thesis
posted on 27.06.2020 by Chenxi Xiong
The intrusion detection system has been widely studied and deployed by researchers for providing better security to computer networks. The increasing of the attack volume and the dramatic advancement of the machine learning make the cooperation between the intrusion detection system and machine learning a hot topic and a promising solution for the cybersecurity. Machine learning usually involves the training process using huge amount of sample data. Since the huge input data may cause a negative effect on the training and detection performance of the machine learning model. Feature selection becomes a crucial technique to rule out the irrelevant and redundant features from the dataset. This study applied a feature selection approach that combines the advanced feature selection algorithms and attacks characteristic features to produce the optimal feature subset for the machine learning model in network intrusion detection. The optimal feature subset was created using the CSE-CIC-IDS2018 dataset, which is the most up-to-date benchmark dataset with comprehensive attack diversity and features. The result of the experiment was produced using machine learning models with decision tree classifier and analyzed with respect to the accuracy, precision, recall, and f1 score.

History

Degree Type

Master of Science

Department

Computer and Information Technology

Campus location

West Lafayette

Advisor/Supervisor/Committee Chair

Baijian Yang

Advisor/Supervisor/Committee co-chair

Robert C. Deadman

Additional Committee Member 2

Wenhai Sun

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