Comparative analysis of reduced feature set based ML models for IDS

Cyber-attacks targeting organisations and individuals are getting worse day by day. With the technological improve-ments, the principles used to launch attacks are also varied. Intrusion Detection System (IDS) is a network solution to detect attacks and anomalies in the network. Leveraging Machine L...

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Bibliographic Details
Main Authors: Jishiya, P. A., Swaraj, K. P., James, Ajay
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Anomalies
Artificial neural networks
Cybersecurity
Deep learning
Intrusion detection systems
Machine learning
Online Access:Get full text
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Summary:Cyber-attacks targeting organisations and individuals are getting worse day by day. With the technological improve-ments, the principles used to launch attacks are also varied. Intrusion Detection System (IDS) is a network solution to detect attacks and anomalies in the network. Leveraging Machine Learning for developing an IDS can make it more powerful. This work aims at evaluating the performance of machine learning models for Intrusion Detection System built based on CART Algorithm, ID3Decision tree algorithm, Random forest algorithm with Convolutional Neural Network. The model is built using KDD Cup99 Dataset. In this work we perform Linear Correlation and Mutual Information feature selection algorithms for ML algorithms and for Convolutional Neural Network algorithm being a deep learning technique feature selection happens automatically. Analysing various parameters including F1-score, ID3 has shown better results.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0138520