Computer Science Masters Theses

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Intrusion Detection With an Autoencoder and ANOVA Feature Selector
(North Dakota State University, 2021) Satyal, Rashmi
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.