Credit Rating Processes Applied to Critical Infrastructure Cyber Risk Assessment.
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Nearly 120 years ago, the great “San Francisco” earthquake of 1906 provided a stark and sobering view of the havoc that can be caused by the sudden and violent movement of Earth’s tectonic plates. According to USGS, the rupture along the San Andreas fault extended 296 miles (447 kilometers) and shook so violently that the motion could be felt as far north as Oregon and east into Nevada. The estimated 7.9-magnitude quake and subsequent fires decimated the major metropolis and surrounding areas: buildings turned to ruins, hundreds of thousands of people left homeless, and a death toll exceeding 3,000. Today, as evidenced by the catastrophic 7.8-magnitude earthquake that struck Turkey in February of 2023, large earthquakes still present a significant potential danger to life and economic security as researchers work to develop ways to better understand earthquake phenomena and quantify associated risks.
This case study report documents the City of Reno's efforts to quantify the value of resilience and the entrepreneurial ways through which the City approached the questions of what role solar and storage could play in their resilience efforts and how to value and pay for solar-plus-storage systems. It documents how and why the City of Reno came to the specific conclusion to integrate solar and storage into their Public Safety Center retrofit project and apply for Federal Emergency Management Agency (FEMA) Building Resilient Infrastructure and Communities (BRIC) funding. This report provides an overview for other local governments to draw inspiration from when considering how to prioritize community values and pursue multi-stakeholder dialogue around resilient energy solutions. The City of Reno's experience can serve as a starting point for other communities interested in valuing the resilience benefits of solar and storage.
Whitepaper that covers transmission line ratings (SLRs, AARs, DLRs), FERC Order 881, and NERC CIP considerations for DLRs, covering Dynamic Line Rating (DLR) Systems & Associated Cyber Security Considerations.
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The President’s Council of Advisors on Science and Technology (PCAST) is the sole body of advisors from outside the federal government charged with making science, technology, and innovation policy recommendations to the President and the White House. Established by Executive Order, it is an independent Federal Advisory Committee comprised of distinguished individuals from industry, academia, and non-profit organizations with a range of perspectives and expertise. On February 27, 2024, PCAST sent a report to the President with recommendations to bolster the resilience and adaptability of the nation’s cyber-physical infrastructure resources.
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Artificial intelligence (AI), and more specifically large language models (LLMs) have the potential for use in penetration testing (“pentesting”) against devices, networks, and computer systems in information technology (IT). We explore the possibility of extending pentesting from IT systems to operational technology (OT) systems, which are more obscure than IT systems in their protocols and design. A challenge therefore exists when applying pretrained LLMs to OT systems as corpora are likely to underrepresent OT systems in comparison to other more prevalent systems. We evaluate augmentations of LLMs with various methods, especially retrieval augmented generation (RAG), to improve performance of the LLMs in the OT domain. In addition to pentesting, some of the testing of these OT devices may include commissioning to ensure that the newly installed devices work correctly. Our framework may also be applied in such cases.
This report outlines the current state of manufacturing weaknesses introduced by the complexities of modern environments, including cloud services and Internet of Things (IoT) devices, with particular attention paid to the unique vulnerabilities encountered by SMMs. It also highlights CyManII’s strategic initiatives and collaborative solutions to mitigate these risks and strengthen the cybersecurity posture of the manufacturing ecosystem. Utilizing data from 2025 to inform forward-looking mitigation strategies, this report provides manufacturers with a clear understanding of both current and emerging cybersecurity threats, as well as practical opportunities to strengthen their cyber ecosystems. The following sections detail key vulnerabilities and threat vectors, along with actionable mitigation strategies, many of which have been developed or piloted through CyManII-led efforts. A thorough understanding of these risks and mitigation strategies is essential for manufacturers seeking to strengthen the security and resilience of their manufacturing operations.
Communication networks in power systems are a major part of the smart grid paradigm. It enables and facilitates the automation of power grid operation as well as self-healing in contingencies. Such dependencies on communication networks, though, create a roam for cyber-threats. An adversary can launch an attack on the communication network, which in turn reflects on power grid operation. Attacks could be in the form of false data injection into system measurements, flooding the communication channels with unnecessary data, or intercepting messages. Using machine learning-based processing on data gathered from communication networks and the power grid is a promising solution for detecting cyber threats. In this paper, a co-simulation of cyber-security for cross-layer strategy is presented. The advantage of such a framework is the augmentation of valuable data that enhances the detection as well as identification of anomalies in the operation of the power grid. The framework is implemented on the IEEE 118-bus system. The system is constructed in Mininet to simulate a communication network and obtain data for analysis. A distributed three controller software-defined networking (SDN) framework is proposed that utilizes the Open Network Operating System (ONOS) cluster. According to the findings of our suggested architecture, it outperforms a single SDN controller framework by a factor of more than ten times the throughput. This provides for a higher flow of data throughout the network while decreasing congestion caused by a single controller’s processing restrictions. Furthermore, our CECD-AS approach outperforms state-of-the-art physics and machine learning-based techniques in terms of attack classification. The performance of the framework is investigated under various types of communication attacks.
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