Date of Award
2021
Publisher
North Dakota State University
Document Type
Thesis
Degree Awarded
Master of Science (MS)
Program
Computer Science
Department
Computer Science
College
Engineering
Faculty Advisor
Nygard, Kendall
Description
Intrusion detection systems are systems that aim at identifying malicious activities or violation of policies in a network. The problem of high dimensionality in intrusion detection systems is a barrier in processing data and analyzing network traffic. This work aims at tackling problems associated with high data dimensionality using a feature selection technique based on one way ANOVA F-test before the classification process. It also involves study of autoencoder as a classification technique for network data as opposed to the traditional use of autoencoders in image data. Experiments have been conducted using the popular NSL-KDD dataset and the results of those experiments are compared with existing literature.
Recommended Citation
Satyal, Rashmi, "Intrusion Detection With an Autoencoder and ANOVA Feature Selector" (2021). Computer Science. 413.
https://digitalcommons.ndsu.edu/computer-sci/413
Rights
NDSU policy 190.6.2
Rights Link
https://www.ndsu.edu/fileadmin/policy/190.pdf
Handle Identifier
https://hdl.handle.net/10365/32584