Anomaly detection in user access patterns using artificial neural networks is a novel way of combating the ever-present concern of computer network intrusion detection for many entities around the world. Anomaly detection is a technique in network security in which a profile is built around a user's normal daily actions. The data collected for these profiles can be as following: file access attempts; failed login attempts; file creations; file access failures; and countless others. This data is collected and used as training data for a neural network.
There are many types of neural networks, such as multi-layer feed-forward network; recurrent networks; support vector machines; and others. For our study, we implemented our own self¬ organizing map (SOM), which we found to not be as heavily researched as other neural network approaches. Using the KDD Cup 99 dataset, we compared our own SOM implementation against other neural network implementations and determine the effectiveness of such an approach.
","abstract_html":"<p>Anomaly detection in user access patterns using artificial neural networks is a novel way of combating the ever-present concern of computer network intrusion detection for many entities around the world. Anomaly detection is a technique in network security in which a profile is built around a user's normal daily actions. The data collected for these profiles can be as following: file access attempts; failed login attempts; file creations; file access failures; and countless others. This data is collected and used as training data for a neural network.</p> <p>There are many types of neural networks, such as multi-layer feed-forward network; recurrent networks; support vector machines; and others. For our study, we implemented our own self¬ organizing map (SOM), which we found to not be as heavily researched as other neural network approaches. Using the KDD Cup 99 dataset, we compared our own SOM implementation against other neural network implementations and determine the effectiveness of such an approach.</p>","abstract_has_math":false,"creators":["Parrachavez, Manuel R"],"institution":null,"degree_name":"Computer Science - Applied Computing Track","degree_level":"Thesis","degree_discipline":"TSYS School of Computer Science","degree_department":null,"school":null,"contributors":["Dr. Shamim Khan","Dr. John Barone","Dr. Jianhua Yang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-01-01T08:00:00Z","date_published":"2017-01-01T08:00:00Z","updated_at":"2026-07-24T01:44:55Z","subjects":["Neural Networks","Self-Organizing Map","Anomaly Detection","KDD Cup 99","Computer Sciences","Cybersecurity","OS and Networks"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://csuepress.columbusstate.edu/theses_dissertations/293","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Shamim Khan","Dr. John Barone","Dr. Jianhua Yang"]},{"key":"dc:creator","label":"Author","values":["Parrachavez, Manuel R"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2018-08-20T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["TSYS School of Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Computer Science - Applied Computing Track"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Neural Networks","Self-Organizing Map","Anomaly Detection","KDD Cup 99","Computer Sciences","Cybersecurity","OS and Networks"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://csuepress.columbusstate.edu/theses_dissertations/293"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Anomaly detection in user access patterns using artificial neural networks is a novel way of combating the ever-present concern of computer network intrusion detection for many entities around the world. Anomaly detection is a technique in network security in which a profile is built around a user's normal daily actions. The data collected for these profiles can be as following: file access attempts; failed login attempts; file creations; file access failures; and countless others. This data is collected and used as training data for a neural network.
There are many types of neural networks, such as multi-layer feed-forward network; recurrent networks; support vector machines; and others. For our study, we implemented our own self¬ organizing map (SOM), which we found to not be as heavily researched as other neural network approaches. Using the KDD Cup 99 dataset, we compared our own SOM implementation against other neural network implementations and determine the effectiveness of such an approach.
"]},{"key":"dc:title","label":"Title","values":["Using Self-Organizing Maps for Computer Network Intrusion Detection"]}]}],"canonical_facts":{"dc:contributor":["Dr. Shamim Khan","Dr. John Barone","Dr. Jianhua Yang"],"dc:creator":["Parrachavez, Manuel R"],"dc:date.available":["2018-08-20T07:00:00Z"],"dc:description.abstract":["Anomaly detection in user access patterns using artificial neural networks is a novel way of combating the ever-present concern of computer network intrusion detection for many entities around the world. Anomaly detection is a technique in network security in which a profile is built around a user's normal daily actions. The data collected for these profiles can be as following: file access attempts; failed login attempts; file creations; file access failures; and countless others. This data is collected and used as training data for a neural network.
There are many types of neural networks, such as multi-layer feed-forward network; recurrent networks; support vector machines; and others. For our study, we implemented our own self¬ organizing map (SOM), which we found to not be as heavily researched as other neural network approaches. Using the KDD Cup 99 dataset, we compared our own SOM implementation against other neural network implementations and determine the effectiveness of such an approach.
"],"dc:identifier":["https://csuepress.columbusstate.edu/theses_dissertations/293"],"dc:language":["English"],"dc:subject":["Neural Networks","Self-Organizing Map","Anomaly Detection","KDD Cup 99","Computer Sciences","Cybersecurity","OS and Networks"],"dc:title":["Using Self-Organizing Maps for Computer Network Intrusion Detection"],"thesis:degree_discipline":["TSYS School of Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Computer Science - Applied Computing Track"]},"updated_at":"2026-07-24T01:44:55Z"}