Wiboonrat, Montri
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Item type:Publication, Cybersecurity for Industrial Control Systems(2024-01-01); ; Sriudomsilp, TheerapornCyber Vision plays a vital role in promoting a collaborative workflow that enhances synergy between IT and OT, ensuring secure operational production. Partnering with Cisco (Thailand), this study aims to bolster cybersecurity measures in the oil and gas (O&G) industry to protect against external cyber threats. Researchers plan to use Cyber Vision for protocol analysis, intrusion detection, vulnerability assessment, and behavioral analysis, providing valuable insights into the industry's security stance. Adhering to IEC 62443 and NIST 800-82 standards, Cyber Vision ensures the continuous, resilient, and safe operation of industrial processes by maintaining constant visibility into Industrial Control Systems (ICS). It comes pre-integrated with top-tier security information and event management (SIEM) and security orchestration, automation, and response (SOAR) platforms. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Cybersecurity in Industrial Control Systems: An integration of information technology and operational technology(2022-01-01)The Industrial Control Systems (ICS) consists of supervisory control and data acquisition (SCADA) systems, distributed control systems (DCS), safety instrumented systems (SIS), and other control system configurations such as programmable logic controllers (PLC). These systems are vulnerable to cyber-attacks. Security against cyber-attacks is becoming more crucial for the ICS industries. The cyber-attacks could be carried out simultaneously at many SCADA, DCS, and SIS systems. The researcher deployed NIST.SP.800-61r2, NIST.SP.800-82r2, IEC 62443 series, and Purdue Enterprise Reference Architecture (PERA) as a guide to implementing cybersecurity approaches to protect them from these threats. The ten industrial plants used cases such as chemical processing, power generation, oil and gas processing, and petroleum processing have been investigated subject to the four main types of vulnerability in cyber security; network; operating system; human; and process vulnerabilities. This research approach is intended to lead to innovative, game-changing capabilities for an incident response plan (IRP) according to cybersecurity Spectrum Security SL2 and SL4 models for the critical ICS. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Human Factors Psychology of Data Center Operations and Maintenance(2020-03-01)The human factor psychology are phenomenally adjust at identifying biases, errors, failures, defects, and deficiencies in how other people in the team think. The Institute Abnormal Incident Reports (AIRs) database reveals that approximately 70 percent of the reported data center outages are directly involved with human error. This paper has been investigated on 2 case studies, the case of data center failure of British Airways in May 2017, and data center assessment of two commercial banks in Bangkok Thailand. Data center outage according to operations and maintenance had been investigated subject to human factors much related to data center outages rather than systems failure. This paper proposes a personal certificated standard, an umbrella team structure, and data center infrastructure management (DCIM) to eliminate human error from data center operations and maintenance. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Cybersecurity of Industrial Automation and Control System (IACS) Networks in Biomass Power Plants(2023-01-01)During the COVID-19 pandemic, the Industrial Automation Control Systems (IACS) have to change procedures in day-by-day operations. Biomass power plants (BPP) cannot avoid this crisis as well. This research aims to solve the problems of BPP operations. The cybersecurity integration between Information Technology (IT) and Operations Technology (OT) was performed by combining of IEC 62443 series, NIST.800.82r2, ISA-95, and Purdue Model as guideline framework for cybersecurity of IACS networks as vulnerability protection. This research purposes two cybersecurity protection models called 'Spectrum Security'. Spectrum Security - SL2 was designed for minimum detection and protection for IACS. Spectrum Security - SL4 was designed for maximum detection and protection for IACS and fault tolerance operations availability of cybersecurity vulnerability. However, the consequence of Fault Tolerance is a double investment in cybersecurity detection and protection systems. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Condition Based Maintenance for Data Center Operations Management(2019-07-01)Power distribution system (PDS) of data center has designed for high reliability and fault tolerance by deploying a redundant topology. Optimal data center reliability requires 4 principals which consist of topology design by standard; reliable components or mean time between failures (MTBF); real-Time monitoring by data center infrastructure management (DCIM); and preventive maintenance through data center operations. However, data center needs more proper operations and maintenance approach to avoid planned and unplanned downtime. The best practice for data center operations management should be applied predictive maintenance or condition-based maintenance (CBM) to monitoring of critical components and systems. These processes will ensure that the system parameters will perform on their functions, and estimate remaining useful life of components for critical mission. Time-based maintenance (TBM) (preventive maintenance) has been applies for supplementary PDS components that define as not mission critical systems. This research proposes a model of preventive and predictive maintenance (PPM) for PDS of data center, which has been verified to improve maintenance efficiency and reduce operating costs of repair time and inventory. How to identify potential problems in early state before component and system failures are the key to prevent planed and unplanned downtime. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Energy Management in Data Centers from Design to Operations and Maintenance(2020-10-20)Data centers are the information factories of the digital evolution. Creating, storing, processing, distributing, and analyzing data all need energy. Therefore, data center industry consumes energy more than 2 percent of the global electricity consumption. Energy efficiency need to discuss at the outset of data center design. The root cause of oversizing data center design is the research question because this will affect investment or CAPEX and long-term operating costs or OPEX of data center as long as data center life cycle (DCLC). Data center measurement in power usage effectiveness (PUE) unit helps data center owners and consultants realized on relationship between oversizing data center design and total cost of ownership (TCO). The research results propose modular data center as a solution to handle uncertainty demand of IT equipment, scalability for growth as your need, flexibility in any size of infrastructure, fast deployment because of prefabricated design, and more efficiency by applying energy management platform called data center infrastructure management (DCIM). - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Data center investment vs. System reliability in power distribution systems(2019-05-01)Power failures are the major causes of data center downtime. Reliability analysis of power distribution systems (PDS) of data center evokes representing a system as collection of subsystems to characterize the reliability of each part of the whole system. Therefore, understanding of reliability at the component level is fundamental to develop accurate estimate of system reliability and prevent system failures. Moreover, realization in the component level help to estimate overall investment of each systems topology. To improve system reliability, it can perform through redundant topology, however redundant components and systems is increasing investment and complexity of data center system. Balancing between cost of downtime and cost of reliability can be differed from business to business. This research purpose optimal method analysis (OMA) to quantify the proper investment against the cost of data center downtime.