Temporal Convolutional Neural Network for Intrusion Detection

Author

Luis Javier Romo Jr., The University of Texas Rio Grande Valley

Date of Award

5-2021

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Dr. Lei Xu

Second Advisor

Dr. Dongchul Kim

Third Advisor

Dr. Emmet Tomai

Abstract

Intrusion detection is an important endeavor for large organizations who are constantly targeted by malicious actors. The nature of network traffic data creates many challenges for researchers that want to create an accurate and efficient system for detecting attacks on networks. Many machine learning algorithms have been developed to take on this task. In this paper, we will review some of these techniques, some data sets used to test these techniques, and an experiment where we developed an intrusion detection system that uses a convolution neural network that can perform sequence modeling. This convolutional neural network outperformed a long-shorted term neural network, an artificial neural network known for its exceptional performance on sequence modeling, on the same task.

Comments

Copyright 2021 Luis Javier Romo Jr. All Rights Reserved.

https://go.openathens.net/redirector/utrgv.edu?url=https://www.proquest.com/dissertations-theses/temporal-convolutional-neural-network-intrusion/docview/2711829134/se-2?accountid=7119

Recommended Citation

Romo, Luis Javier Jr., "Temporal Convolutional Neural Network for Intrusion Detection" (2021). Theses and Dissertations. 759.
https://scholarworks.utrgv.edu/etd/759