Intrusion Detection With an Autoencoder and ANOVA Feature Selector
| dc.contributor.author | Satyal, Rashmi | |
| dc.description.abstract | 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. | en_US |
| dc.publisher | North Dakota State University | en_US |
| dc.rights | NDSU policy 190.6.2 | en_US |
| dc.title | Intrusion Detection With an Autoencoder and ANOVA Feature Selector | en_US |
| dc.type | Thesis | en_US |
| dc.date.accessioned | 2022-05-25T20:41:49Z | |
| dc.date.available | 2022-05-25T20:41:49Z | |
| dc.date.issued | 2021 | |
| dc.identifier.uri | https://hdl.handle.net/10365/32584 | |
| dc.subject | intrusion detection | en_US |
| dc.subject | ANOVA | en_US |
| dc.subject | autoencoder | en_US |
| dc.subject | feature selection | en_US |
| dc.subject | machine learning | en_US |
| dc.rights.uri | https://www.ndsu.edu/fileadmin/policy/190.pdf | en_US |
| ndsu.degree | Master of Science (MS) | en_US |
| ndsu.college | Engineering | en_US |
| ndsu.department | Computer Science | en_US |
| ndsu.program | Computer Science | en_US |
| ndsu.advisor | Nygard, Kendall |