{"id":{"repo_id":"nps","oai_identifier":"oai:calhoun.nps.edu:10945/72762"},"canonical_url":"https://search.dev.ndltd.org/etd/nps/oai:calhoun.nps.edu:10945/72762","repository":{"repo_id":"nps","name":"Naval Postgraduate School","base_url":"https://calhoun.nps.edu/server/oai/request"},"display":{"title":"IMPROVING CYBER RESILIENCE OF SHIPBOARD POWER SYSTEMS USING MACHINE LEARNING","abstract":"Naval vessels are increasingly implementing their own shipboard microgrids to reduce fuel consumption and to meet growing technological requirements. This improved technology comes with benefits but also creates unique risks, including exposure to cyber intrusions. To prevent exploitation of these network vulnerabilities, it is imperative that system anomalies are immediately detected. This research aims to explain how physical intrusions into shipboard components can manifest in power data and how these manifestations can be effectively detected and classified. This research uses a modified Simulink model to simulate a shipboard microgrid and various loads to create realistic test data while adhering to the DOD Interface Standard (MIL-STD-1399). This research then uses a long short term memory (LSTM) network machine learning algorithm, modeled in Python, to create a system for detecting anomalies in a shipboard microgrid. The model generates predictive data, and by comparing the predictive data to current trends, it detects outliers that result from cyber threats, as well as system component failures. This research is critical for improving the operational readiness of the fleet due to both the application of predicting component failures, and the primary objective of protecting ships from cyber threats.","abstract_html":"Naval vessels are increasingly implementing their own shipboard microgrids to reduce fuel consumption and to meet growing technological requirements. This improved technology comes with benefits but also creates unique risks, including exposure to cyber intrusions. To prevent exploitation of these network vulnerabilities, it is imperative that system anomalies are immediately detected. This research aims to explain how physical intrusions into shipboard components can manifest in power data and how these manifestations can be effectively detected and classified. This research uses a modified Simulink model to simulate a shipboard microgrid and various loads to create realistic test data while adhering to the DOD Interface Standard (MIL-STD-1399). This research then uses a long short term memory (LSTM) network machine learning algorithm, modeled in Python, to create a system for detecting anomalies in a shipboard microgrid. The model generates predictive data, and by comparing the predictive data to current trends, it detects outliers that result from cyber threats, as well as system component failures. This research is critical for improving the operational readiness of the fleet due to both the application of predicting component failures, and the primary objective of protecting ships from cyber threats.","abstract_has_math":false,"creators":["Smith, Paul F."],"institution":"Monterey, CA; Naval Postgraduate School","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Electrical and Computer Engineering (ECE)","school":null,"contributors":[],"advisors":["Oriti, Giovanna","Thulasiraman, Preetha"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-03","date_published":"2024-03","updated_at":"2026-07-27T20:26:27Z","subjects":[],"languages":[],"rights":["This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10945/72762","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Oriti, Giovanna","Thulasiraman, Preetha"]},{"key":"dc:contributor.department","label":"Department","values":["Electrical and Computer Engineering (ECE)"]},{"key":"dc:creator","label":"Author","values":["Smith, Paul F."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-04-23T20:13:58Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-04-23T20:13:58Z"]},{"key":"dc:date.issued","label":"Date","values":["2024-03"]},{"key":"dc:publisher","label":"Institution","values":["Monterey, CA; Naval Postgraduate School"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10945/72762"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Naval vessels are increasingly implementing their own shipboard microgrids to reduce fuel consumption and to meet growing technological requirements. This improved technology comes with benefits but also creates unique risks, including exposure to cyber intrusions. To prevent exploitation of these network vulnerabilities, it is imperative that system anomalies are immediately detected. This research aims to explain how physical intrusions into shipboard components can manifest in power data and how these manifestations can be effectively detected and classified. This research uses a modified Simulink model to simulate a shipboard microgrid and various loads to create realistic test data while adhering to the DOD Interface Standard (MIL-STD-1399). This research then uses a long short term memory (LSTM) network machine learning algorithm, modeled in Python, to create a system for detecting anomalies in a shipboard microgrid. The model generates predictive data, and by comparing the predictive data to current trends, it detects outliers that result from cyber threats, as well as system component failures. This research is critical for improving the operational readiness of the fleet due to both the application of predicting component failures, and the primary objective of protecting ships from cyber threats."]},{"key":"dc:title","label":"Title","values":["IMPROVING CYBER RESILIENCE OF SHIPBOARD POWER SYSTEMS USING MACHINE LEARNING"]}]}],"canonical_facts":{"dc:contributor.advisor":["Oriti, Giovanna","Thulasiraman, Preetha"],"dc:contributor.department":["Electrical and Computer Engineering (ECE)"],"dc:creator":["Smith, Paul F."],"dc:date.accessioned":["2024-04-23T20:13:58Z"],"dc:date.available":["2024-04-23T20:13:58Z"],"dc:date.issued":["2024-03"],"dc:description.abstract":["Naval vessels are increasingly implementing their own shipboard microgrids to reduce fuel consumption and to meet growing technological requirements. This improved technology comes with benefits but also creates unique risks, including exposure to cyber intrusions. To prevent exploitation of these network vulnerabilities, it is imperative that system anomalies are immediately detected. This research aims to explain how physical intrusions into shipboard components can manifest in power data and how these manifestations can be effectively detected and classified. This research uses a modified Simulink model to simulate a shipboard microgrid and various loads to create realistic test data while adhering to the DOD Interface Standard (MIL-STD-1399). This research then uses a long short term memory (LSTM) network machine learning algorithm, modeled in Python, to create a system for detecting anomalies in a shipboard microgrid. The model generates predictive data, and by comparing the predictive data to current trends, it detects outliers that result from cyber threats, as well as system component failures. This research is critical for improving the operational readiness of the fleet due to both the application of predicting component failures, and the primary objective of protecting ships from cyber threats."],"dc:identifier.uri":["https://hdl.handle.net/10945/72762"],"dc:publisher":["Monterey, CA; Naval Postgraduate School"],"dc:rights":["This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States."],"dc:title":["IMPROVING CYBER RESILIENCE OF SHIPBOARD POWER SYSTEMS USING MACHINE LEARNING"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T20:26:27Z"}