Operations Center Research Articles

The SEE-IT Trial: emergency medical services Streaming Enabled Evaluation In Trauma: study protocol for an interventional feasibility randomised controlled trial

IntroductionAccurate and timely dispatch of emergency medical services (EMS) is vital due to limited resources and patients’ risk of mortality and morbidity increasing with time. Currently, most UK emergency operations...

Post-Pandemic Spatial Decisions with University Students’ Awareness of Covid-19 Measures in The Built Environment

Background: Pandemic coordination boards, science boards, and operation centers were established worldwide to prevent the pandemic’s spread. These centers have published guidelines, posters, and brochures for public transportation places such as COVID-19 Risk Assessment, Guide, hospitals, workplaces, and public transportation to inform the communities. However, studies have not been conducted on how much these measures are applied in real life and how much people are conscious. Aims: In this study, the student’s awareness of COVID-19 precautions will be evaluated through the WELL criteria by examining the guide titled “Strategies from the WELL Building Standard to support the fight against COVID-19”. Methods: The survey questions were applied to 100 university students from different cities and departments in Turkiye to collect the research data. The data obtained from the questionnaires were applied to the study’s sample group and were coded over the questionnaire results via SPSS statistical program. Results: It is seen that only 45% of students have information about “water quality,” and only half of the students do sports during the pandemic period. In this period, 77 students declared that they paid attention to the issue of nutrition. 53 out of 100 students experienced sleep disorders. Moreover, according to the survey results, 52 students received psychological support. Conclusion: The WELL standard, using closed areas and creating awareness of COVID-19, is an important solution for the built environment against worldwide pandemics. Users’ behaviors and spatial requirements of the built environment will contribute to developing project ideas for designers, architects, and engineers.

Testing SOAR tools in use

Investigations within Security Operation Centers (SOCs) are tedious as they rely on manual efforts to query diverse data sources, overlay related logs, correlate the data into information, and then document results in a ticketing system. Security Orchestration, Automation, and Response (SOAR) tools are a relatively new technology that promise, with appropriate configuration, to collect, filter, and display needed diverse information; automate many of the common tasks that unnecessarily require SOC analysts’ time; facilitate SOC collaboration; and, in doing so, improve both efficiency and consistency of SOCs. There has been no prior research to test SOAR tools in practice; hence, understanding and evaluation of their effect is nascent and needed. In this paper, we design and administer the first hands-on user study of SOAR tools, involving 24 participants and six commercial SOAR tools. Our contributions include the experimental design, itemizing six defining characteristics of SOAR tools, and a methodology for testing them. We describe configuration of a cyber range test environment, including network, user, and threat emulation; a full SOC tool suite; and creation of artifacts allowing multiple representative investigation scenarios to permit testing. We present the first research results on SOAR tools. Concisely, our findings are that: per-SOC SOAR configuration is extremely important; SOAR tools increase efficiency and reduce context switching, although with potentially decreased ticketing accuracy/completeness; user preference is slightly negatively correlated with their performance with the tool; internet dependence varies widely among SOAR tools; and balance of automation with assisting decision making is preferred by senior participants. We deliver a public user- and tool-anonymized and -obfuscated version of the data.

Investigating on the robustness of flow-based intrusion detection system against adversarial samples using Generative Adversarial Networks

Recently, Software Defined Networking (SDN) has emerged as the key technology in programming and orchestrating security policy in the security operations centers (SOCs) for heterogeneous networks. Typically, machine learning-based intrusion detection systems (ML-IDS) have been deployed and associated with SDN to leverage the features of a programmable network to defend against sophisticated cyberattacks in anomaly detection. Unfortunately, such ML-based IDSs are easily vulnerable to adversarial attacks due to the lack of diverse forms of malicious records in the training dataset. The missing data sample in the training phase can lead to a lower detection rate in real-world scenarios with adversarial settings. In this paper, we explore the ability of Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP), WGAN-GP with two timescale update rule (WGAN-GP TTUR), and AdvGAN in generating perturbed attack samples to bypass attack detectors. Then, this approach is used to continuously evaluate the robustness of ML-based IDSs and then upgrade them as a service in SDN. The experimental results on CICIDS2018 and InSDN datasets demonstrate that generated adversarial samples can be used to fool targeted IDS. Later, those created samples can supplement the original ones in retraining IDS to improve the resilience of the attack detector.

Demographic and Public Health Characteristics of COVID-19 Mortality Cases in Rivers State, Nigeria –A Retrospective Cohort Study

Introduction: COVID-19 has accounted for approximately six million deaths globally. Several risk factors have been identified. However, the population profile varies in different population groups. The study's aim is to describe the population profile of COVID-19 mortality in Rivers State, Nigeria using captured population-based health records. Methods: Using electronic State Health Records, secondary data analysis was conducted on recorded COVID-19 mortality. Data were obtained from the Public Health Emergency Operations Centre (PHEOC) at the State Ministry of Health, Rivers State. Data were accessed from the PHEOC database, and it included COVID-19 related mortality. Data were collected on demographics, pre-existing comorbidity, symptoms, facility managed, patient status, treatment outcome, and dates of related events. Cohort characteristics were described using means and proportions. Results: There were 191 COVID-19 deaths identified. The mean age was 57.08 years, of which 144 were male (75.4%). The 51–65-year age group had the highest mortality count (38.9%). Over 50% of the patients were hypertensive, and diabetes was the second most common comrbidity (28.8%). Running nose, cough, fever and breathing difficulties were the most reported COVID-19 symptoms. Conclusion: This study found that COVID-19 was responsible for a greater mortality increase in men and that the prevalence of hypertension and diabetes was higher in these individuals. Additionally, age and the presence of comorbidities may be associated with COVID-19 mortality. Future research in this area could further explain these findings.

Towards Optimal Triage and Mitigation of Context-Sensitive Cyber Vulnerabilities

Cyber vulnerabilities are security deficiencies in computer and network systems of organizations, which can be exploited by an adversary to cause significant damage. The technology and security personnel resources currently available in organizations to mitigate the vulnerabilities are highly inadequate. As a result, systems routinely remain unpatched, thus making them vulnerable to security breaches from the adversaries. The potential consequences of an exploited vulnerability depend upon the context as well as the severity of the vulnerability, which may differ among networks and organizations. Furthermore, security personnel tend to have varying levels of expertise and technical proficiencies associated with different computer and network devices. There exists a critical need to develop a resource-constrained approach for effectively identifying and mitigating important context-sensitive cyber vulnerabilities. In this article, we develop an advanced analytics and optimization framework to address this need and compare our approach with rule-based methods employed in real-world cybersecurity operations centers, as well as a vulnerability prioritization method from recent literature. First, we propose a machine learning-based vulnerability priority scoring system (VPSS) to calculate the priority scores for each of the vulnerabilities found in an organization’s network and quantify organizational context-based vulnerability exposure. Next, we propose a decision-support system, which consists of a two-step sequential optimization approach. The first model selects the high priority vulnerability instances from the dense report subject to resource constraints, and the second model then optimally allocates them to the security personnel with matching skill types for mitigation. Experiment results conducted using a real-world vulnerability data set show that our approach 1) outperforms both the rule-based methods and the vulnerability prioritization method from literature in prioritizing context-sensitive vulnerabilities, which are found across highly susceptible organizationally relevant host machines, and 2) maximizes the pairs of vulnerability instance type and the respective security analyst skill type for optimal mitigation.

Data Auditing for Intelligent Network Security Monitoring

Data auditing is a process to consistently keep the quality of data high, but this process is generally missing in network security monitoring. When network-based intrusion detection systems catch any suspicious packet, they generate alert messages that are further investigated by security analysts. An alert is generally assigned to only one analyst at best, and then the analyst determines whether the alert is true or false, called labeling. Therefore, different analysts may label very similar alerts with different labels. In this article, we introduce this problem of inconsistent labeling in network security monitoring and present a new automatic data auditing method to check if any human mistake has occurred for the labeling. Through our experiments on two data sets, a private one from a real security operations center and an open data set for reproducible experiments, we confirm that the new auditing method can catch incorrect labels, and the accuracy of a machine learning model on the data set can be enhanced through the label correction.

INTEGRAL mission - Automation of Transponder Swaps Scheduling and Schedule Validation

Human-made satellites allow to broaden the knowledge of the world by discovering new stars, and exoplanets, observing Earth, performing X-ray measurements and more. All satellites need to be controlled from Earth by the Flight Control Team (FCT) consisting of experts from a wide range of fields. The satellite INTEGRAL is controlled from the astronomy control room in the European Space Operations Centre (ESOC) together with XMM-Newton and Gaia. Automation allows a single person to control three spacecrafts simultaneously and helps to reduce operational costs. This paper describes the automation of transponder swaps scheduling and mission schedule validation using tools developed in the programming language Python. Both tools were carefully tested in the defined verification process and marked as ready to be used. The developed tools allow faster mission planning for the INTEGRAL satellite thus saving the human resources of the FCT.

Deep VULMAN: A deep reinforcement learning-enabled cyber vulnerability management framework

Cyber vulnerability management is a critical function of a cybersecurity operations center (CSOC) that helps protect organizations against cyber-attacks on their computer and network systems. Adversaries hold an asymmetric advantage over the CSOC, as the number of deficiencies in these systems is increasing at a significantly higher rate compared to the expansion rate of the security teams to mitigate them. The current approaches in cyber vulnerability management are deterministic and one-time decision-making methods, which do not consider future uncertainties when prioritizing and selecting vulnerabilities for mitigation. These approaches are also constrained by the sub-optimal distribution of resources, providing no flexibility to adjust their response to fluctuations in vulnerability arrivals. We propose a novel framework, Deep VULMAN, consisting of a deep reinforcement learning agent and an integer programming method to fill this gap in cyber vulnerability management process. Our sequential decision-making framework, first, determines the near-optimal amount of resources to be allocated for mitigation under uncertainty for a given system state, and then determines the optimal set of prioritized vulnerability instances for mitigation. Results show that our framework outperforms the current methods in prioritizing the selection of important organization-specific vulnerabilities, on both simulated and real-world vulnerability data, observed over a one-year period.

Centre for smart grid analysis with real time simulation platform

The Smart Grid Operation Centre (SGOC) is established on 1st April, 2021, as part of the purpose-built facilities of the Haking Wong Campus of the Hong Kong Institute of Vocational Education (IVE), a Vocational Training Council (VTC) member institution. With the generous contributions and steadfast support from CLP Power Hong Kong Limited (CLP Power), SGOC is served as simulation and training facility available to support all Smart Grid development in Hong Kong. The SGOC has installed the Real-Time Digital Simulator (RTDS) and Smart grid-related equipment, including the Distributed Energy Resources (DERs), protection relay, and smart meter that can be looped into the simulator, for its capability and interoperability on real-time studies, including modeling and resolving the potential issues in realtime before they may impact the delivery or operation of the project or the smart grid network. Facing the up-coming challenges with penetration of renewable energy, digitalization, and increasing of electric vehicles, the SGOC provides a feasible range of smart grid related services in respect of real-time studies on the operation of smart grid system, operator training, and operational support that enable derisk deployment and operation of the related smart grid parties, and more practical and tailored training which increases familiarity and confidence of operators. This paper describes the practicalities of specifying, testing, installation and interfacing the platform, with a realtime simulator, at the SGOC, for future operational training and support use, including some uses on faults during operation, diagnosing alarms, scheme updates/upgrades and network interactions and range of smart grid focused training courses will be available and content enhancement into course delivery. Through these wide-ranging installations and systems, trainers will learn to apply the latest industry technologies. This will enrich their knowledge of smart grids and relevant new technologies, thereby helping to promote smart, clean energy development. This paper is also included the Power Hardware- In-the-Loop (PHIL) platform and a case study introduction for the frequency capability for microgrid and Battery Energy Storage System (BESS) response.

Co-planning of hydrogen-based microgrids and fuel-cell bus operation centers under low-carbon and resilience considerations

The hydrogen-driven fuel-cell electric buses, which bear the advantages of emission free, high storage content and long mileage range, provide a promising choice for decarbonizing the public transportation system. Also, the operation centers for fuel-cell electric buses can be upgraded as the hydrogen-based microgrids to achieve higher economic benefits and resilience levels. To support the low-carbon and resilience-oriented planning of microgrid-aided bus centers, this paper presents a two-stage robust optimization formulation with integer corrective decisions. In particular, the first-stage problem is to optimize the microgrid siting decisions as well as the capacity configuration of renewable-powered electrolyzers, fuel-cell stacks and hydrogen refueling stations for maximizing the annualized benefits. Then, a min–max problem is introduced in the second-stage to improve the load survival value subject to the planning decisions and the worst-case contingency scenario. Also, various corrective actions, e.g., the emergency response of fuel-cell electric buses using vehicle-to-grid functionality, the energy scheduling of stationary equipment in hydrogen refueling stations and the adaptive network reconfiguration, are considered for resilience enhancement of the local power network. As the resulting robust optimization formulation is with a challenging mixed-integer recourse structure, the nested column-and-constraint generation algorithm is designed. Numerical results on exemplary power network and public transportation system validate the effectiveness of the proposed planning methodology, which has the potential to be profitable under normal operations while improving the load survivability under severe disastrous occasions. Moreover, our customized nested column-and-constraint generation shows a satisfactory solution performance, which facilitates prompt disaster relief actions of energy-transportation network.

Dynamic Multi-Compartment Vehicle Routing Problem for Smart Waste Collection

The rapid increase in urbanization results in an increase in the volume of municipal solid waste produced every day, causing overflow of the garbage cans and thus distorting the city’s appearance; for this and environmental reasons, smart cities involve the use of modern technologies for intelligent and efficient waste management. Smart bins in urban environments contain sensors that measure the status of containers in real-time and trigger wireless alarms if the container reaches a predetermined threshold, and then communicate the information to the operations center, which then sends vehicles to collect the waste from the selected stations in order to collect a significant waste amount and reduce transportation costs. In this article, we will address the issue of the Dynamic Multi-Compartmental Vehicle Routing Problem (DM-CVRP) for selective and intelligent waste collection. This problem is summarized as a linear mathematical programming model to define optimal dynamic routes to minimize the total cost, which are the transportation costs and the penalty costs caused by exceeding the bin capacity. The hybridized genetic algorithm (GA) is proposed to solve this problem, and the effectiveness of the proposed approach is verified by extensive numerical experiments on instances given by Valorsul, with some modifications to adapt these data to our problem. Then we were able to ensure the effectiveness of our approach based on the results in the static and dynamic cases, which are very encouraging.

V-SOC4AS: A Vehicle-SOC for Improving Automotive Security

Integrating embedded systems into next-generation vehicles is proliferating as they increase safety, efficiency, and driving comfort. These functionalities are provided by hundreds of electronic control units (ECUs) that communicate with each other using various protocols that, if not properly designed, may be vulnerable to local or remote attacks. The paper presents a vehicle-security operation center for improving automotive security (V-SOC4AS) to enhance the detection, response, and prevention of cyber-attacks in the automotive context. The goal is to monitor in real-time each subsystem of intra-vehicle communication, that is controller area network (CAN), local interconnect network (LIN), FlexRay, media oriented systems transport (MOST), and Ethernet. Therefore, to achieve this goal, security information and event management (SIEM) was used to monitor and detect malicious attacks in intra-vehicle and inter-vehicle communications: messages transmitted between vehicle ECUs; infotainment and telematics systems, which provide passengers with entertainment capabilities and information about the vehicle system; and vehicular ports, which allow vehicles to connect to diagnostic devices, upload content of various types. As a result, this allows the automation and improvement of threat detection and incident response processes. Furthermore, the V-SOC4AS allows the classification of the received message as malicious and non-malicious and acquisition of additional information about the type of attack. Thus, this reduces the detection time and provides more support for response activities. Experimental evaluation was conducted on two state-of-the-art attacks: denial of service (DoS) and fuzzing. An open-source dataset was used to simulate the vehicles. V-SOC4AS exploits security information and event management to analyze the packets sent by a vehicle using a rule-based mechanism. If the payload contains a CAN frame attack, it is notified to the SOC analysts.

In Situ Experimental Study of Cloud-Precipitation Interference by Low-Frequency Acoustic Waves

Since acoustic agglomeration is an effective pre-treatment technique for removing fine particles, it can be considered as a potential technology for applications in aerosol pollution control, industrial dust and mist removal, and cloud and precipitation interference. In this study, the cloud-precipitation interference effect was evaluated in situ based on a multi-dimensional multi-scale monitoring system. The variations in the spatial and temporal distribution of rainfall near the surface and the characteristics of precipitation droplets in the air were investigated. The results indicate that strong low-frequency acoustic waves had a significant impact on the macro-characteristics of rainfall clouds, the microphysical structure of rain droplets and near-surface precipitation, and various microwave parameters. In terms of physical structure, the precipitation cloud’s base height decreased significantly upon opening the acoustic device, while agglomeration and de-agglomeration of raindrops were in a dynamic equilibrium. When the sound generator was on, the particle concentration at a sampling attitude of 500−1700 m and the proportion of particles with diameters of 1–1.5 mm decreased significantly (by 1–5 ln [1/m3·mm]). In contrast, the particle concentration increased by 1–3 ln [1/m3·mm] at a sampling attitude below 400 m. Moreover, during acoustic interference, the reflectivity factor surged by 2.71 dBZ within 1200 m of the operation centre. Overall, the spatial and temporal distributions of rainfall rates and cumulative precipitation within 5 km of acoustic operation were uneven and influenced by local terrain and background winds.

Uganda's experience in establishing an electronic compendium for public health emergencies.

Uganda has implemented several interventions that have contributed to prevention, early detection, and effective response to Public Health Emergencies (PHEs). However, there are gaps in collecting and documenting data on the overall response to these PHEs. We set out to establish a comprehensive electronic database of PHEs that occurred in Uganda since 2000. We constituted a core development team, developed a data dictionary, and worked with Health Information Systems Program (HISP)-Uganda to develop and customize a compendium of PHEs using the electronic Integrated Disease Surveillance and Response (eIDSR) module on the District Health Information Software version 2 (DHIS2) platform. We reviewed literature for retrospective data on PHEs for the compendium. Working with the Uganda Public Health Emergency Operations Center (PHEOC), we prospectively updated the compendium with real-time data on reported PHEs. We developed a user's guide to support future data entry teams. An operational compendium was developed within the eIDSR module of the DHIS2 platform. The variables for PHEs data collection include those that identify the type, location, nature and time to response of each PHE. The compendium has been updated with retrospective PHE data and real-time prospective data collection is ongoing. Data within this compendium is being used to generate information that can guide future outbreak response and management. The compendium development highlights the importance of documenting outbreak detection and response data in a central location for future reference. This data provides an opportunity to evaluate and inform improvements in PHEs response.

Informatics infrastructure in a rural pediatric clinical trials network: Matching specific clinical research needs with best practices and industry guidelines

Children have historically been underrepresented in randomized controlled trials and multi-center studies. This is particularly true for children who reside in rural and underserved areas. Conducting multi-center trials in rural areas presents unique informatics challenges. These challenges call for increased attention towards informatics infrastructure and the need for development and application of sound informatics approaches to the collection, processing, and management of data for clinical studies. By modifying existing local infrastructure and utilizing open source tools, we have been able to successfully deploy a multi-site data coordinating and operations center. We report our implementation decisions for data collection and management for the IDeA States Pediatric Clinical Trial Network (ISPCTN) based on the functionality needed for the ISPCTN, our synthesis of the extant literature in data collection and management methodology, and Good Clinical Data Management Practices.

The National Cultural Resources Information Management System (NCRIMS)

ABSTRACTDespite making great strides over the past 50 years, cultural resources data management and synthesis continues to be elusive and nonstandardized, with each state and agency developing disparate systems that do not easily mesh. The Bureau of Land Management (BLM) has embarked on a national initiative by creating a National Cultural Resources Data Standard (NCRDS) that works to address many long-standing data organization issues. The NCRDS allows for the application of more rigorous data management principles that facilitate landscape-level planning and data modeling on BLM-administered lands across the western United States. The NCRDS and associated National Cultural Resources Information Management System (NCRIMS) contains normalized data from 11 western State Historic Preservation Offices (SHPOs) and BLM data stores. NCRIMS is a web-based application hosted by the BLM's National Operations Center (NOC) Enterprise Geographic Information System (EGIS). NCRIMS allows for high-level planning during local, regional, and multistate project analyses and undertakings, facilitating consideration of cultural heritage values early in the planning process versus late stages as has been traditional. This allows the BLM to more proactively, effectively, and efficiently answer data calls and inform agency decision-makers on possible impacts to cultural heritage resources by proposed or ongoing agency actions.

A Framework Design to Improve the Operation Capability of Cyber Security for Electric Power Dispatching and Control Systems based on the Concept of SOC

Benefiting from the vision of the Security Operation Center (SOC), this paper proposes a construction plan for a cyber security operation center for the electric power dispatching and control system. This solution integrates and analyzes various data such as ledger database, knowledge database, and historical alarm database in the power dispatching and control system, and realizes the functions of alarm handling, risk assessment, and emergency response for the existing security management platform. Through the risk evaluation experiment, it is verified that our scheme has the function of overall interacted active security defense, and can effectively improve the cyber security risk resistance of the power dispatching and control system.

APIRO: A Framework for Automated Security Tools API Recommendation

Security Orchestration, Automation, and Response (SOAR) platforms integrate and orchestrate a wide variety of security tools to accelerate the operational activities of Security Operation Center (SOC). Integration of security tools in a SOAR platform is mostly done manually using APIs, plugins, and scripts. SOC teams need to navigate through API calls of different security tools to find a suitable API to define or update an incident response action. Analyzing various types of API documentation with diverse API format and presentation structure involves significant challenges such as data availability, data heterogeneity, and semantic variation for automatic identification of security tool APIs specific to a particular task. Given these challenges can have negative impact on SOC team’s ability to handle security incident effectively and efficiently, we consider it important to devise suitable automated support solutions to address these challenges. We propose a novel learning-based framework for automated security tool API R ecommendation for security O rchestration, automation, and response, APIRO . To mitigate data availability constraint, APIRO enriches security tool API description by applying a wide variety of data augmentation techniques. To learn data heterogeneity of the security tools and semantic variation in API descriptions, APIRO consists of an API-specific word embedding model and a Convolutional Neural Network (CNN) model that are used for prediction of top three relevant APIs for a task. We experimentally demonstrate the effectiveness of APIRO in recommending APIs for different tasks using three security tools and 36 augmentation techniques. Our experimental results demonstrate the feasibility of APIRO for achieving 91.9% Top-1 Accuracy. Compared to the state-of-the-art baseline, APIRO is 26.93%, 23.03%, and 20.87% improved in terms of Top-1, Top-2, and Top-3 Accuracy and outperforms the baseline by 23.7% in terms of Mean Reciprocal Rank (MRR).

Outbreak investigation of cholera in a rural community, Rivers State Nigeria: an interventional epidemiological study

Cholera is a potentially life-threatening public health menace caused by a gram-negative rod-shaped bacterium known as Vibrio cholerae. This study was an epidemiological investigation of the cholera outbreak in Degema local government area (LGA), Rivers State, Nigeria. An interventional epidemiological study was conducted in a rural community between December 2021 to February 2022. All suspected cases were identified using the World Health Organization (WHO) case definition of acute watery diarrhoea. The standard outbreak investigation procedures (active case search, case management, water sanitation, hygiene) were implemented. The cholera outbreak observed two waves within an at-risk population of 373,071 in the affected LGA. Of the 35 line-listed suspected cases (male-20 versus female-15), the highest frequency was observed among those aged 0-5 years. The index case was reported on the 3rd of December in the 47th week of 2021 and abated by the 49th week, while the 2nd wave began in the 1st week of 2022 and abated by the 6th week. The stool samples result revealed; six positives for RDT, while four samples were positive by microscopy. All patients received home-based treatment except for two cases that were managed at the healthcare facility. The attack and case fatality rates were 0.01, and 8% respectively. The Rivers State public health emergency operation centre deployed standard epidemiological interventions for the containment of acute watery diarrhoea (cholera) aimed at reducing morbidity and mortality; however, vaccines were not part of the response. The deployment of vaccines and provision of potable water will improve response outcomes.