Performance Metrics of an Intrusion Detection System Through Window-Based Deep Learning Models

Authors

DOI:

https://doi.org/10.47852/bonviewJDSIS32021485

Keywords:

intrusion and prevention system, convolutional neural network, recurrent neural network, auto-encoders, performance metrics, data packets

Abstract

Intrusion and prevention technologies perform reliably in harsh conditions by fortifying many of the world's highest security sites with few defects in high performance. This paper aims to contribute by designing an intrusion/preventive system using a window-based convolutional neural network (CNN), an integrated recurrent neural network (RNN), and autoencoders (AutoE) to detect and test the performance of the intrusion detection system. The data packets were converted to images where the pixels were used as input. The CNN architecture shows a three-layer model with high predictive performance. The result shows high performance on CNN as compared to both RNN and AutoE; CNN seems to resist overfitting more than the rest of the models. The future perspective would be to test the model on other standard methods such as support vector machine (SVM) and dynamic control systems.

 

Received: 7 August 2023 | Revised: 13 October 2023 | Accepted: 30 October 2023

 

Conflicts of Interest

The author declares that she has no conflicts of interest to this work. 

 

Data Availability Statement

Data available on request from the corresponding author upon reasonable request.


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Published

2023-10-30

How to Cite

Isiaka, F. (2023). Performance Metrics of an Intrusion Detection System Through Window-Based Deep Learning Models. Journal of Data Science and Intelligent Systems. https://doi.org/10.47852/bonviewJDSIS32021485

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Section

Research Articles