Satyal, Rashmi2022-05-252022-05-252021https://hdl.handle.net/10365/32584Intrusion 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.NDSU policy 190.6.2https://www.ndsu.edu/fileadmin/policy/190.pdfintrusion detectionANOVAautoencoderfeature selectionmachine learningThesis