Pub Date : 2018-08-01DOI: 10.1109/RWEEK.2018.8473503
Aditya Sundararajan, Longfei Wei, Tanwir Khan, A. Sarwat, Deepal Rodrigo
The Operation and Information Technology support personnel at utility command and control centers constantly detect suspicious events and/or extreme conditions across the smart grid. Already overwhelmed by routine mandatory tasks like guidelines compliance and patching that if ignored could incur penalties, they have little time to understand the large volumes of event logs generated by intrusion detection systems, firewalls, and other security tools. The cognitive gap between these powerful automated tools and the human mind reduces the situation awareness, thereby increasing the likelihood of sub-optimal decisions that could be advantageous to well-evolved attackers. This paper proposes a tri-modular framework which shifts low-performance processing speed and data contextualization to intelligent learning algorithms that provide humans only with actionable information, thereby bridging the cognitive gap. The framework has three modules including Data Module (DM): Kafka, Spark, and R to ingest streams of heterogeneous data; Classification Module (CM): a Long Short-Term Memory (LSTM) model to classify processed data; and Action Module (AM): naturalistic and rational models for time-critical and non-time-critical decision-making, respectively. This paper focuses on the design and development of the modules, and demonstrates proof-of-concept of DM using partially synthesized streams of real smart grid network security data.
{"title":"A Tri-Modular Framework to Minimize Smart Grid Cyber-Attack Cognitive Gap in Utility Control Centers","authors":"Aditya Sundararajan, Longfei Wei, Tanwir Khan, A. Sarwat, Deepal Rodrigo","doi":"10.1109/RWEEK.2018.8473503","DOIUrl":"https://doi.org/10.1109/RWEEK.2018.8473503","url":null,"abstract":"The Operation and Information Technology support personnel at utility command and control centers constantly detect suspicious events and/or extreme conditions across the smart grid. Already overwhelmed by routine mandatory tasks like guidelines compliance and patching that if ignored could incur penalties, they have little time to understand the large volumes of event logs generated by intrusion detection systems, firewalls, and other security tools. The cognitive gap between these powerful automated tools and the human mind reduces the situation awareness, thereby increasing the likelihood of sub-optimal decisions that could be advantageous to well-evolved attackers. This paper proposes a tri-modular framework which shifts low-performance processing speed and data contextualization to intelligent learning algorithms that provide humans only with actionable information, thereby bridging the cognitive gap. The framework has three modules including Data Module (DM): Kafka, Spark, and R to ingest streams of heterogeneous data; Classification Module (CM): a Long Short-Term Memory (LSTM) model to classify processed data; and Action Module (AM): naturalistic and rational models for time-critical and non-time-critical decision-making, respectively. This paper focuses on the design and development of the modules, and demonstrates proof-of-concept of DM using partially synthesized streams of real smart grid network security data.","PeriodicalId":206638,"journal":{"name":"2018 Resilience Week (RWS)","volume":"282 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124508553","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}
Pub Date : 2018-08-01DOI: 10.1109/RWEEK.2018.8473470
K. Savchenko, H. Medema, R. Boring
At 8:07 a.m. on January 13, 2018, the Hawaii Emergency Management Agency transmitted a false ballistic missile alert via cellphone, television, and radio throughout the state of Hawaii. Stating, “BALLISTIC MISSILE THREAT INBOUND TO HAWAII. SEEK IMMEDIATE SHELTER. THIS IS NOT A DRILL,” the alert created widespread panic. Finally, after 38 minutes, a second message was issued, assuring the public that the alert was false. Initial speculation attributed the occurrence to human error in which the emergency officer inadvertently selected the option to elicit an actual alert rather than the mock drill alert. The emergency worker believed the attack to be real due to what he perceived to be a mistake in the means by which the drill was initiated during a shift change. The employee reported that he did not hear the word “exercise” repeated during the drill. Fellow coworkers reported that they had clearly heard the word during the drill. Investigations opened by the Federal Communications Commission, Hawaii House of Representatives, and Hawaii Department of Defense uncovered a critical lack of training and training records management, as well as poor and inconsistent work procedures and processes within both the Hawaii Emergency Management Agency and the Federal Emergency Management Agency. Additionally, the investigation highlighted a decade of consistent performance issues for work carried out by the emergency officer. This paper will examine the Hawaii Missile False Alarm Incident in greater detail with a focus on the contributing human factors. Specifically, this review presents the many aspects of mutual awareness that were present and addresses how each type plays a critical role in the cooperation and team-specific behaviors carried out within both the crew dynamic and the operations between the two emergency management agencies and their employees.
{"title":"Trouble in Paradise: Mutual Awareness, Teamwork, and Hawaii False Ballistic Missile Alert","authors":"K. Savchenko, H. Medema, R. Boring","doi":"10.1109/RWEEK.2018.8473470","DOIUrl":"https://doi.org/10.1109/RWEEK.2018.8473470","url":null,"abstract":"At 8:07 a.m. on January 13, 2018, the Hawaii Emergency Management Agency transmitted a false ballistic missile alert via cellphone, television, and radio throughout the state of Hawaii. Stating, “BALLISTIC MISSILE THREAT INBOUND TO HAWAII. SEEK IMMEDIATE SHELTER. THIS IS NOT A DRILL,” the alert created widespread panic. Finally, after 38 minutes, a second message was issued, assuring the public that the alert was false. Initial speculation attributed the occurrence to human error in which the emergency officer inadvertently selected the option to elicit an actual alert rather than the mock drill alert. The emergency worker believed the attack to be real due to what he perceived to be a mistake in the means by which the drill was initiated during a shift change. The employee reported that he did not hear the word “exercise” repeated during the drill. Fellow coworkers reported that they had clearly heard the word during the drill. Investigations opened by the Federal Communications Commission, Hawaii House of Representatives, and Hawaii Department of Defense uncovered a critical lack of training and training records management, as well as poor and inconsistent work procedures and processes within both the Hawaii Emergency Management Agency and the Federal Emergency Management Agency. Additionally, the investigation highlighted a decade of consistent performance issues for work carried out by the emergency officer. This paper will examine the Hawaii Missile False Alarm Incident in greater detail with a focus on the contributing human factors. Specifically, this review presents the many aspects of mutual awareness that were present and addresses how each type plays a critical role in the cooperation and team-specific behaviors carried out within both the crew dynamic and the operations between the two emergency management agencies and their employees.","PeriodicalId":206638,"journal":{"name":"2018 Resilience Week (RWS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124509288","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}
Pub Date : 2018-08-01DOI: 10.1109/RWEEK.2018.8473533
C. B. Jones, C. Carter, Zachary Thomas
The communications infrastructure for building automation systems was not originally designed to be resilient, and is susceptible to network attacks. Adversaries can exploit out-of-date legacy systems, insecure open protocols, exposure to the public internet, and outdated firmware to cause harm. To improve the defense strategies, significant efforts to provide defense through network detection have been conducted. However, the existing solutions require human intervention, such as analyst or an incident responder to investigate breaches and mitigate possible damages or data loss. Instead, this paper proposes an automated, device-level solution that can be deployed on a single board computer to effectively detect, and provide response strategies that deflect malicious signals and remediate infected devices when network-based cyber-attacks are successful. The solution monitors critical control networks, analyzes packet data, and actively detects and responds to attacks using an unsupervised artificial neural network.
{"title":"Intrusion Detection & Response using an Unsupervised Artificial Neural Network on a Single Board Computer for Building Control Resilience","authors":"C. B. Jones, C. Carter, Zachary Thomas","doi":"10.1109/RWEEK.2018.8473533","DOIUrl":"https://doi.org/10.1109/RWEEK.2018.8473533","url":null,"abstract":"The communications infrastructure for building automation systems was not originally designed to be resilient, and is susceptible to network attacks. Adversaries can exploit out-of-date legacy systems, insecure open protocols, exposure to the public internet, and outdated firmware to cause harm. To improve the defense strategies, significant efforts to provide defense through network detection have been conducted. However, the existing solutions require human intervention, such as analyst or an incident responder to investigate breaches and mitigate possible damages or data loss. Instead, this paper proposes an automated, device-level solution that can be deployed on a single board computer to effectively detect, and provide response strategies that deflect malicious signals and remediate infected devices when network-based cyber-attacks are successful. The solution monitors critical control networks, analyzes packet data, and actively detects and responds to attacks using an unsupervised artificial neural network.","PeriodicalId":206638,"journal":{"name":"2018 Resilience Week (RWS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131673434","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}
Pub Date : 2018-08-01DOI: 10.1109/RWEEK.2018.8473515
Joseph L. Loof, T. Pratt, Eric M. Jesse
Unsupervised classification methods based on polarization features are considered for the problem of associating frequency-hopped pulses according to their transmission source. The problem is considered in frequency-selective propagation channels, where discrimination cannot be obtained simply through signal amplitude information, and where angle-of-arrival methods are challenged by multipath. Source discrimination is based on polarization-frequency behavior, leveraging polarization mode dispersion (PMD) associated with each propagation channel. The passive receiver employs a dual-polarized antenna to collect orthogonally-polarized complex baseband signals, measure PMD responses, and compare the responses with a dynamic library of responses to identify signals that likely originated from the same source. The PMD responses are updated upon reception of each pulse and the number of unique sources may also be estimated.
{"title":"Unsupervised Classification of Frequency Hopped Signals in Frequency-Selective Channels","authors":"Joseph L. Loof, T. Pratt, Eric M. Jesse","doi":"10.1109/RWEEK.2018.8473515","DOIUrl":"https://doi.org/10.1109/RWEEK.2018.8473515","url":null,"abstract":"Unsupervised classification methods based on polarization features are considered for the problem of associating frequency-hopped pulses according to their transmission source. The problem is considered in frequency-selective propagation channels, where discrimination cannot be obtained simply through signal amplitude information, and where angle-of-arrival methods are challenged by multipath. Source discrimination is based on polarization-frequency behavior, leveraging polarization mode dispersion (PMD) associated with each propagation channel. The passive receiver employs a dual-polarized antenna to collect orthogonally-polarized complex baseband signals, measure PMD responses, and compare the responses with a dynamic library of responses to identify signals that likely originated from the same source. The PMD responses are updated upon reception of each pulse and the number of unique sources may also be estimated.","PeriodicalId":206638,"journal":{"name":"2018 Resilience Week (RWS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125252607","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}
Pub Date : 2018-08-01DOI: 10.1109/RWEEK.2018.8473545
G. Weaver, T. Klett, T. Holcomb
This paper presents ongoing research to understand the interplay between the structure and behavior of critical infrastructure systems. The National Infrastructure Protection Plan (NIPP), Presidential Policy Directive 21 (PPD 21), and other documents underscore the need to conduct risk assessments on critical infrastructure and discover high-risk assets. Complicating this problem however, is that interdependencies that aren’t explicitly modeled as part of the system can be important and greatly impact the system being studied. For example, the reported BadBIOS attack illustrates that through unexpected interactions (e.g. inaudible sounds), systems thought to be air-gapped may communicate with one another. Moreover, the impact of a disruption may be defined relative to some spatial or temporal scale and this may not always be explicit in a risk assessment. By combining graph complexity metrics with simulation, we hope to efficiently identify critical assets as well as understand more about the relationship between structure and function with critical infrastructure systems. This paper presents preliminary results from the electrical power grid as part of our ongoing research effort.
{"title":"Structure and Function of Interconnected Critical Infrastructures","authors":"G. Weaver, T. Klett, T. Holcomb","doi":"10.1109/RWEEK.2018.8473545","DOIUrl":"https://doi.org/10.1109/RWEEK.2018.8473545","url":null,"abstract":"This paper presents ongoing research to understand the interplay between the structure and behavior of critical infrastructure systems. The National Infrastructure Protection Plan (NIPP), Presidential Policy Directive 21 (PPD 21), and other documents underscore the need to conduct risk assessments on critical infrastructure and discover high-risk assets. Complicating this problem however, is that interdependencies that aren’t explicitly modeled as part of the system can be important and greatly impact the system being studied. For example, the reported BadBIOS attack illustrates that through unexpected interactions (e.g. inaudible sounds), systems thought to be air-gapped may communicate with one another. Moreover, the impact of a disruption may be defined relative to some spatial or temporal scale and this may not always be explicit in a risk assessment. By combining graph complexity metrics with simulation, we hope to efficiently identify critical assets as well as understand more about the relationship between structure and function with critical infrastructure systems. This paper presents preliminary results from the electrical power grid as part of our ongoing research effort.","PeriodicalId":206638,"journal":{"name":"2018 Resilience Week (RWS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133614636","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}
Pub Date : 2018-08-01DOI: 10.1109/RWEEK.2018.8473511
Sharif Ullah, S. Shetty, Amin Hassanzadeh
Cyber resiliency of Energy Delivery Systems (EDS) is critical for secure and resilient cyber infrastructure. Defense-in-depth architecture forces attackers to conduct lateral propagation until the target is compromised. Researchers developed techniques based on graph spectral matrices to model lateral propagation. However, these techniques ignore host criticality which is critical in EDS. In this paper, we model attacker’s opportunity by developing three criticality metrics for each host along the path to the target. The first metric refers the opportunity of attackers before they penetrate the infrastructure. The second metric measure the opportunity a host provides by allowing attackers to propagate through the network. Along with vulnerability we also take into account the attributes of hosts and links within each path. Then, we derive third criticality metric to reflect the information flow dependency from each host to target. Finally, we provide system design for instantiating the proposed metrics for real network scenarios in EDS. We present simulation results which illustrates the effectiveness of the metrics for efficient defense deployment in EDS cyber infrastructure.
{"title":"Towards Modeling Attacker’s Opportunity for Improving Cyber Resilience in Energy Delivery Systems","authors":"Sharif Ullah, S. Shetty, Amin Hassanzadeh","doi":"10.1109/RWEEK.2018.8473511","DOIUrl":"https://doi.org/10.1109/RWEEK.2018.8473511","url":null,"abstract":"Cyber resiliency of Energy Delivery Systems (EDS) is critical for secure and resilient cyber infrastructure. Defense-in-depth architecture forces attackers to conduct lateral propagation until the target is compromised. Researchers developed techniques based on graph spectral matrices to model lateral propagation. However, these techniques ignore host criticality which is critical in EDS. In this paper, we model attacker’s opportunity by developing three criticality metrics for each host along the path to the target. The first metric refers the opportunity of attackers before they penetrate the infrastructure. The second metric measure the opportunity a host provides by allowing attackers to propagate through the network. Along with vulnerability we also take into account the attributes of hosts and links within each path. Then, we derive third criticality metric to reflect the information flow dependency from each host to target. Finally, we provide system design for instantiating the proposed metrics for real network scenarios in EDS. We present simulation results which illustrates the effectiveness of the metrics for efficient defense deployment in EDS cyber infrastructure.","PeriodicalId":206638,"journal":{"name":"2018 Resilience Week (RWS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132271247","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}
Pub Date : 2018-08-01DOI: 10.1109/RWEEK.2018.8473464
Abdulaziz Alqahtani, D. Tipper, Katrina Kelly-Pitou
Determining the best location and configuration of microgrids to support post-disaster critical infrastructure operation in smart cities is a significant issue for policymakers and electric system planners. In this paper, we propose a two-phase approach to locating microgrids to increase critical infrastructure resilience. In the first phase, we consider how to prioritize critical infrastructure components in smart cities using either a normalized combination of characteristics or a ranked list of lists method. Numerical results show that the two methods give similar results. In the second phase, we propose to combine prioritization information across multiple infrastructures geographically to identify potential locations for microgrids. A simple heuristic for location of a microgrid is proposed and compared with an optimization model that seeks to find the location that minimizes a weighted combination of prioritization information and cost.
{"title":"Locating Microgrids to Improve Smart City Resilience","authors":"Abdulaziz Alqahtani, D. Tipper, Katrina Kelly-Pitou","doi":"10.1109/RWEEK.2018.8473464","DOIUrl":"https://doi.org/10.1109/RWEEK.2018.8473464","url":null,"abstract":"Determining the best location and configuration of microgrids to support post-disaster critical infrastructure operation in smart cities is a significant issue for policymakers and electric system planners. In this paper, we propose a two-phase approach to locating microgrids to increase critical infrastructure resilience. In the first phase, we consider how to prioritize critical infrastructure components in smart cities using either a normalized combination of characteristics or a ranked list of lists method. Numerical results show that the two methods give similar results. In the second phase, we propose to combine prioritization information across multiple infrastructures geographically to identify potential locations for microgrids. A simple heuristic for location of a microgrid is proposed and compared with an optimization model that seeks to find the location that minimizes a weighted combination of prioritization information and cost.","PeriodicalId":206638,"journal":{"name":"2018 Resilience Week (RWS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114850063","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}
Pub Date : 2018-08-01DOI: 10.1109/RWEEK.2018.8473514
Vivek Kumar Singh, Altay Ozen, M. Govindarasu
Future smart grid capabilities provide assurance to expand the advanced information and communication technologies to evolve into densely interconnected cyber physical system. Remedial Action Scheme (RAS), widely used for wide-area protection, relies on the interconnected networks and data sharing devices, which are exposed to the multitude of vulnerabilities. This paper presents our proposed approach to developing multi-agent based RAS scheme against the system-aware stealthy cyber-attacks. Specifically, we propose the two-level hierarchical architecture which consists of distributed local RAS controllers (RAScs) as local agents, operating at different zones/ areas, which are constantly monitored by an overseer, the central agent. The local controllers receive local and randomly changing outside zonal measurements and cyclically forwards to the overseer. The overseer identifies the corrupted controller using the anomaly detection algorithm which processes the measurements coming from the local controllers, compute measurement errors using local and outside zonal measurements, perform validation checks, and finally detect anomalies based on the two-step verification. Next, as a proof of concept, we have implemented and validated the proposed methodology in cyber physical environment at Iowa State’s PowerCyber testbed. We have also implemented the coordinated attack vectors which involve corrupting the local controller and later performing stealthy attacks on the system’s generator. We have evaluated its performance during the online testing in terms of detection rate and Iatency. The experimental results show that it is efficient in detecting different classes of attacks, including ramp and pulse attacks.
{"title":"A Hierarchical Multi-Agent Based Anomaly Detection for Wide-Area Protection in Smart Grid","authors":"Vivek Kumar Singh, Altay Ozen, M. Govindarasu","doi":"10.1109/RWEEK.2018.8473514","DOIUrl":"https://doi.org/10.1109/RWEEK.2018.8473514","url":null,"abstract":"Future smart grid capabilities provide assurance to expand the advanced information and communication technologies to evolve into densely interconnected cyber physical system. Remedial Action Scheme (RAS), widely used for wide-area protection, relies on the interconnected networks and data sharing devices, which are exposed to the multitude of vulnerabilities. This paper presents our proposed approach to developing multi-agent based RAS scheme against the system-aware stealthy cyber-attacks. Specifically, we propose the two-level hierarchical architecture which consists of distributed local RAS controllers (RAScs) as local agents, operating at different zones/ areas, which are constantly monitored by an overseer, the central agent. The local controllers receive local and randomly changing outside zonal measurements and cyclically forwards to the overseer. The overseer identifies the corrupted controller using the anomaly detection algorithm which processes the measurements coming from the local controllers, compute measurement errors using local and outside zonal measurements, perform validation checks, and finally detect anomalies based on the two-step verification. Next, as a proof of concept, we have implemented and validated the proposed methodology in cyber physical environment at Iowa State’s PowerCyber testbed. We have also implemented the coordinated attack vectors which involve corrupting the local controller and later performing stealthy attacks on the system’s generator. We have evaluated its performance during the online testing in terms of detection rate and Iatency. The experimental results show that it is efficient in detecting different classes of attacks, including ramp and pulse attacks.","PeriodicalId":206638,"journal":{"name":"2018 Resilience Week (RWS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128430859","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}
Pub Date : 2018-08-01DOI: 10.1109/RWEEK.2018.8473499
Sheuwen Chuang, Chia-Hsin Cheng, Hsiao-Chun Chen, Ching-An Lee, David D. Woods
The Formosa Fun Coast Dust Explosion (FFCDE) occurred on 27 June 2015. It is the largest man-made disaster in Taiwan’s history. The paper explores how participating actors dealt with the communication challenges to rescue 499 burn victims from the disaster scene and provide resuscitation and life support for mass burn casualties in hospitals following the FFCDE. Data collection was via review of government reports and journal publications as well as in-depth individual interviews with 36 key participants in this event. Technological communication issues and human related communication issues were identified. The analysis reveals that the remodel of local incident command post at the disaster scene improved the difficulties caused by inadequate communications, and hospital staff’ resilience to adapt was based on anticipation in the face of uncertainty and on coordination across roles and units to keep pace with the time varying demands.
{"title":"Coping with communication challenges after the Formosa Fun Coast Dust Explosion","authors":"Sheuwen Chuang, Chia-Hsin Cheng, Hsiao-Chun Chen, Ching-An Lee, David D. Woods","doi":"10.1109/RWEEK.2018.8473499","DOIUrl":"https://doi.org/10.1109/RWEEK.2018.8473499","url":null,"abstract":"The Formosa Fun Coast Dust Explosion (FFCDE) occurred on 27 June 2015. It is the largest man-made disaster in Taiwan’s history. The paper explores how participating actors dealt with the communication challenges to rescue 499 burn victims from the disaster scene and provide resuscitation and life support for mass burn casualties in hospitals following the FFCDE. Data collection was via review of government reports and journal publications as well as in-depth individual interviews with 36 key participants in this event. Technological communication issues and human related communication issues were identified. The analysis reveals that the remodel of local incident command post at the disaster scene improved the difficulties caused by inadequate communications, and hospital staff’ resilience to adapt was based on anticipation in the face of uncertainty and on coordination across roles and units to keep pace with the time varying demands.","PeriodicalId":206638,"journal":{"name":"2018 Resilience Week (RWS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114542031","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}
Pub Date : 2018-08-01DOI: 10.1109/RWEEK.2018.8473561
David A. Grimm, Mustafa Demir, Jamie C. Gorman, Nancy J. Cooke
In this longitudinal study, we examined the performance of Human Autonomy Teams (HATs) in the context of a Remotely Piloted Aircraft System (RPAS) to determine team resilience of HATs under three types of degraded conditions – an automation failure, an autonomy failure, and a malicious cyber-attack. In this study, two human team members interacted with a “synthetic” agent who was actually a well-trained experimenter. First, we identified high- and low-performing teams by considering team performance score and overcoming number of failures across 10 40-minute missions. We calculated the amount of system level entropy (extracted from human and technological signals) over the course of the missions to track the amount of system reorganization in response to failures. We hypothesized that resilient teams would be more effective at reorganizing system level behavior, as observed through entropy. To explore team resilience, we examined how long it took these two teams to overcome the failures, as well as the amount of system reorganization each team displayed throughout the failure. Our findings from this exploratory analysis indicate that the high-performing team displayed more flexibility and adaptivity under degraded conditions than the low-performing team. This also underlines that effective systems level reorganization is needed in order to be adaptive and resilient in a dynamic task environment.
{"title":"Systems Level Evaluation of Resilience in Human-Autonomy Teaming under Degraded Conditions","authors":"David A. Grimm, Mustafa Demir, Jamie C. Gorman, Nancy J. Cooke","doi":"10.1109/RWEEK.2018.8473561","DOIUrl":"https://doi.org/10.1109/RWEEK.2018.8473561","url":null,"abstract":"In this longitudinal study, we examined the performance of Human Autonomy Teams (HATs) in the context of a Remotely Piloted Aircraft System (RPAS) to determine team resilience of HATs under three types of degraded conditions – an automation failure, an autonomy failure, and a malicious cyber-attack. In this study, two human team members interacted with a “synthetic” agent who was actually a well-trained experimenter. First, we identified high- and low-performing teams by considering team performance score and overcoming number of failures across 10 40-minute missions. We calculated the amount of system level entropy (extracted from human and technological signals) over the course of the missions to track the amount of system reorganization in response to failures. We hypothesized that resilient teams would be more effective at reorganizing system level behavior, as observed through entropy. To explore team resilience, we examined how long it took these two teams to overcome the failures, as well as the amount of system reorganization each team displayed throughout the failure. Our findings from this exploratory analysis indicate that the high-performing team displayed more flexibility and adaptivity under degraded conditions than the low-performing team. This also underlines that effective systems level reorganization is needed in order to be adaptive and resilient in a dynamic task environment.","PeriodicalId":206638,"journal":{"name":"2018 Resilience Week (RWS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122554224","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}
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