[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124895-en":3,"doc-seo-124895-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124895,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","IMPROVING CYBER RESILIENCE OF SHIPBOARD POWER SYSTEMS USING MACHINE LEARNING","Naval vessels increasingly deploy shipboard microgrids to reduce fuel consumption and support expanding technological needs, but these systems introduce distinct cybersecurity risks, including potential cyber intrusions. Preventing exploitation of network vulnerabilities requires immediate detection of system anomalies. This thesis explains how physical intrusions in shipboard components can appear in power data and how such manifestations can be detected and classified. A modified Simulink model generates realistic test data for a microgrid and loads, and a Python-modeled LSTM machine learning system produces predictive outputs to identify outliers linked to cyber threats and component failures, supporting fleet readiness.","Calhoun: The NPS Institutional Archive  \nDSpace Repository  \n\n| NPS Scholarship | Theses |\n| --- | --- |\n\n2024-03  \nIMPROVING CYBER RESILIENCE OF  \nSHIPBOARD POWER SYSTEMS USING MACHINE LEARNING  \nSmith, Paul F.  \nMonterey, CA; Naval Postgraduate School  \n[https://hdl.handle.net/10945/72762](https://hdl.handle.net/10945/72762)  \nThis 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.  \nDownloaded from NPS Archive: Calhoun  \nNAVAL POSTGRADUATE  \nSCHOOL MONTEREY, CALIFORNIA  \nTHESIS  \nIMPROVING CYBER RESILIENCE OF SHIPBOARD POWER SYSTEMS USING MACHINE LEARNING  \nby  \nPaul F. Smith  \nMarch 2024  \nThesis Advisor: Preetha Thulasiraman  \nCo-Advisor: Giovanna Oriti  \nApproved for public release. Distribution is unlimited.  \nTHIS PAGE INTENTIONALLY LEFT BLANK  \n\n| REPORT DOCUMENTATION PAGE |  |  |  |  | Form Approved OMB No. 0704-0188 |  |\n| --- | --- | --- | --- | --- | --- | --- |\n| Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington, DC, 20503. |  |  |  |  |  |  |\n| 1. AGENCY USE ONLY (Leave blank) |  | 2. REPORT DATE\u003Cbr>March 2024 |  | 3. REPORT TYPE AND DATES COVERED\u003Cbr>Master’s thesis |  |  |\n| 4. TITLE AND SUBTITLE\u003Cbr>IMPROVING CYBER RESILIENCE OF SHIPBOARD POWER SYSTEMS USING MACHINE LEARNING |  |  |  |  | 5. FUNDING NUMBERS |  |\n| 6. AUTHOR(S) Paul F. Smith |  |  |  |  |  |  |\n| 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)\u003Cbr>Naval Postgraduate School\u003Cbr>Monterey, CA 93943-5000 |  |  |  |  | 8. PERFORMING\u003Cbr>ORGANIZATION REPORT NUMBER |  |\n| 9. SPONSORING / MONITORING AGENCY NAME(S) AND\u003Cbr>ADDRESS(ES)\u003Cbr>N/A |  |  |  |  | 10. SPONSORING / MONITORING AGENCY REPORT NUMBER |  |\n| 11. SUPPLEMENTARY NOTES The views expressed in this thesis are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government. |  |  |  |  |  |  |\n| 12a. DISTRIBUTION / AVAILABILITY STATEMENT\u003Cbr>Approved for public release. Distribution is unlimited. |  |  |  |  | 12b. DISTRIBUTION CODE\u003Cbr>A |  |\n| 13. ABSTRACT (maximum 200 words)\u003Cbr>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 pre","cbCaitmdrG0kj3Ti","https://ap.wps.com/l/cbCaitmdrG0kj3Ti","pdf",3409586,1,75,"English","en",105,"# Abstract\n## Microgrid risks and the need for anomaly detection\n## Mapping intrusions to power-data manifestations\n## Simulation and test-data generation\n## LSTM-based anomaly detection and classification\n## Impact on operational readiness","[{\"question\":\"What problem does the thesis address for shipboard microgrids?\",\"answer\":\"It addresses the cybersecurity risk that shipboard microgrids introduce, requiring immediate detection of anomalies to prevent exploitation of network vulnerabilities.\"},{\"question\":\"How does the research detect intrusions using power data?\",\"answer\":\"It explains how physical intrusions into shipboard components manifest in power data, then detects and classifies those manifestations as outliers.\"},{\"question\":\"What methods and tools are used to build the detection system?\",\"answer\":\"The work uses a modified Simulink model to simulate the shipboard microgrid and loads for realistic test data, and a Python-modeled LSTM network to generate predictive data and detect outliers.\"}]","IMPROVING CYBER RESILIENCE OF SHIPBOARD POWER SYSTEMS USING MACHINE LEARNING | 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