K. Kiriakidis, Brien Croteau, Tracie A. Severson, Erick J. Rodríguez-Seda, R. Robucci, Riadul Islam, Saad Rahman
{"title":"Degradable Tracking System based on Hardware Multi-Model Estimators","authors":"K. Kiriakidis, Brien Croteau, Tracie A. Severson, Erick J. Rodríguez-Seda, R. Robucci, Riadul Islam, Saad Rahman","doi":"10.1109/RWS55399.2022.9984042","DOIUrl":"https://doi.org/10.1109/RWS55399.2022.9984042","url":null,"abstract":"Sensing systems onboard unmanned vehicles operate in an environment of constrained computational resources. A cyber-attack may primarily aim to degrade these computing devices and, ultimately, incapacitate the sensing system itself. To prepare a prototype tracking system for degradation, this paper proposes distributed hardware implementation of a Multiple Model estimator on two FPGA units and, after an attack, adaptation of the estimator by leveraging Dynamic Partial Reconfiguration of the single surviving FPGA. The method ensures that the most likely models of the estimator are loaded on to the fabric of the surviving FPGA with minimal interruption.","PeriodicalId":170769,"journal":{"name":"2022 Resilience Week (RWS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128184501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. David McKinnon, Jodi Heintz-Obradovich, M. Legatt, Mark J Rice Pacific, C. Bonebrake, Arcadio Vielma, A. Bretas
{"title":"User-Focused Tools to Enhance IT/OT Cyber Resilience within the Power Grid","authors":"A. David McKinnon, Jodi Heintz-Obradovich, M. Legatt, Mark J Rice Pacific, C. Bonebrake, Arcadio Vielma, A. Bretas","doi":"10.1109/RWS55399.2022.9984026","DOIUrl":"https://doi.org/10.1109/RWS55399.2022.9984026","url":null,"abstract":"The power grid is undergoing several changes that are increasing its complexity as nexuses between electric-gas, transmission-distribution, and energy-communications continue to become increasingly critical. This system is heavily dependent on communication infrastructure and controls, and it relies on humans in operational technology (OT) and information technology (IT) roles to manage the increasing breadth, depth, and speed of data. Many technical challenges have presented themselves and will need to be addressed to provide reliable grid operations. With increased reliance on distributed controls and communication infrastructure, cybersecurity becomes an inherent requirement. When considering current cyber-physical security solutions for the power grid, one can notice a clear divide between information technology and operation technology networks. However, in real-life applications, these networks are interdependent. This work presents results of interviews with key utility cybersecurity personnel, analyzes the results, and makes recommendations towards solution of existing technical and operational challenges realized. Existing workflows are presented, wireframe interviews are discussed, and tool requirements are described. The existence of easy-to-implement solutions, based on existing energy management systems, highlight the potential for real-life applications.","PeriodicalId":170769,"journal":{"name":"2022 Resilience Week (RWS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123029756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Javier Moscoso-Cabrera, Alexis Burgos-Rivera, Michael Vázquez-Nieves, Fernando Lozano-Inca, Eduardo Ortiz-Rivera
{"title":"Proyecto Luz Verde UPRM","authors":"Javier Moscoso-Cabrera, Alexis Burgos-Rivera, Michael Vázquez-Nieves, Fernando Lozano-Inca, Eduardo Ortiz-Rivera","doi":"10.1109/rws55399.2022.9984021","DOIUrl":"https://doi.org/10.1109/rws55399.2022.9984021","url":null,"abstract":"A lot is said about solar photovoltaic (PV) energy systems but not much is taught. This becomes a problem when these systems can literally define the line between life and death for people when they most need it. Proyecto Luz Verde UPRM is an energy consumption awareness, renewable energy education and solar PV system design initiative addressing this situation. Since its inception in October 2019, it seeks to provide the university campus with a solar gazebo capable of serving as a space for establishing the conversations around energy consumption reduction while charging during day and night electronic devices such as cellphones, tablets, and computers for all members of the UPRM community. It began as a research experience for students eager in the IEEE Power and Energy Society (PES) to become engaged and informed about solar energy exploring the capabilities and limits PV systems impose before heading into their power engineering coursework. Since undertaking the challenge in 2019, the project development has led to awards in competitions and project proposals that have led to receiving funding for real life implementation in 2022. Its scope ranges from implementing a first prototype in the UPRM campus to extend in the future to communities outside of the campus all over Puerto Rico with education materials as well as the actual PV system structure, since the design including batteries can serve a specific purpose in the campus but consider particular resources and needs by community.","PeriodicalId":170769,"journal":{"name":"2022 Resilience Week (RWS)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128007780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mark D. Petersen, Alan B. Shaffer, Charles Prince, Gurminder Singh
{"title":"A Digital Twin System for Replaying Cyber Mission Data","authors":"Mark D. Petersen, Alan B. Shaffer, Charles Prince, Gurminder Singh","doi":"10.1109/RWS55399.2022.9984020","DOIUrl":"https://doi.org/10.1109/RWS55399.2022.9984020","url":null,"abstract":"The Persistent Cyber Training Environment (PCTE), developed by the Department of Defense, provides a single training environment for cyberspace operations. PCTE offers a closed network that supports individual training as well as mission rehearsal and post-operation analysis. However, it does not have the ability to replay near real-time events as training scenarios, nor to ingest other network events as scenarios. Replaying cyber mission data on a digital twin network in PCTE would enable more realistic near real-time operator training, as well as supporting development and testing for cyberspace operations. This requires extracting target network specifications from a cyber mission data set. This research developed a prototype tool to extract the network specifications necessary to instantiate a digital twin network within PCTE from cyber mission data. A key contribution of this work is the ability, upon sending recorded network data to the tool, to create a high-fidelity network, and scenario for training purposes. This contribution enables a significant decrease in the time from detection of a problem on a live network to the creation of relevant training scenarios that address the detected problem.","PeriodicalId":170769,"journal":{"name":"2022 Resilience Week (RWS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126530649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Application of Resilience Theory to Organizations Subject to Disinformation Campaigns","authors":"Amanda Wachtel, S. Caskey, T. Gunda, E. Keller","doi":"10.1109/RWS55399.2022.9984033","DOIUrl":"https://doi.org/10.1109/RWS55399.2022.9984033","url":null,"abstract":"Community, corporate, and government organizations are being targeted by disinformation attacks at an unprecedented rate. These attacks interrupt the ability of organizations to make high-consequence decisions and can lower their confidence in datasets and analytics. New interdisciplinary research approaches are being actively developed to expand resilience theory applications to organizations, and to determine the metrics and mitigations needed to increase resilience against disinformation. This paper presents initial ideas on adapting resilience methodologies for organizations and disinformation, highlighting key areas that require further exploration in this emerging field of research.","PeriodicalId":170769,"journal":{"name":"2022 Resilience Week (RWS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126282739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Efficient Interdependent Systems Recovery Modeling with DeepONets*","authors":"Somayajulu L. N. Dhulipala, Ryan Hruska","doi":"10.1109/RWS55399.2022.9984029","DOIUrl":"https://doi.org/10.1109/RWS55399.2022.9984029","url":null,"abstract":"Modeling the recovery of interdependent critical infrastructure is a key component of quantifying and optimizing societal resilience to disruptive events. However, simulating the recovery of large-scale interdependent systems under random disruptive events is computationally expensive. Therefore, we propose the application of Deep Operator Networks (DeepONets) in this paper to accelerate the recovery modeling of interdependent systems. DeepONets are ML architectures which identify mathematical operators from data. The form of governing equations DeepONets identify and the governing equation of interdependent systems recovery model are similar. Therefore, we hypothesize that DeepONets can efficiently model the interdependent systems recovery with little training data. We applied DeepONets to a simple case of four interdependent systems with sixteen states. DeepONets, overall, performed satisfactorily in predicting the recovery of these interdependent systems for out of training sample data when compared to reference results.","PeriodicalId":170769,"journal":{"name":"2022 Resilience Week (RWS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130991541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sam Yang, B. Vaagensmith, Deepika Patra, R. Hruska, T. Phillips
{"title":"Multi-fidelity power flow solver","authors":"Sam Yang, B. Vaagensmith, Deepika Patra, R. Hruska, T. Phillips","doi":"10.1109/RWS55399.2022.9984038","DOIUrl":"https://doi.org/10.1109/RWS55399.2022.9984038","url":null,"abstract":"We propose a multi-fidelity neural network (MFNN) tailored for rapid high-dimensional grid power flow simulations and contingency analysis with scarce high-fidelity contingency data. The proposed model comprises two networks—the first one trained on DC approximation as low-fidelity data and coupled to a high-fidelity neural network trained on both low- and high-fidelity power flow data. Each network features a latent module which parametrizes the model by a discrete grid topology vector for generalization (e.g., n power lines with k disconnections or contingencies, if any), and the targeted high-fidelity output is a weighted sum of linear and nonlinear functions. We tested the model on 14- and 118-bus test cases and evaluated its performance based on the n – k power flow prediction accuracy with respect to imbalanced contingency data and high-to-low-fidelity sample ratio. The results presented herein demonstrate MFNN's potential and its limits with up to two orders of magnitude faster and more accurate power flow solutions than DC approximation.","PeriodicalId":170769,"journal":{"name":"2022 Resilience Week (RWS)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116842779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}