Control and Data Plane Security of High Performance Networks
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INL HPC capabilities were utilized by individuals at universities, industry, and government laboratories for a wide range of research and engineering activities between October 1, 2020 and September 30, 2021. The HPC systems themselves experienced minimal downtime over the course of the fiscal year. Most downtime was planned to apply critical security patches to system software. The most serious of the unplanned downtime events occurred due to maintenance on the Kohler backup generator in July. Users were kept notified of all events, both planned and unplanned, via 36 total email notifications in FY-21. The INL HPC systems continue to provide high availability for collaboration and innovation in nuclear energy systems research.
This report highlights overall design consideration regarding automation and database collection and extrapolation prototype for Farmpod, LLC. There are potentially many combinations of software, hardware, and networking which suffices for the requirements. For this report, we suggest a Raspberry Pi 2 Model B for the hardware. We suggest the Mako application server for the user interface and RPIO python modules for automated software processes. At the present moment, security is not a major concern, but should be addressed during the initial design phase. The software stack consists of two primary components: the automated processes and application server to implement a user interface.
The BioWatch Sample Management System is software developed by Los Alamos National Laboratory to support the Department of Homeland Security BioWatch program. Field Operators use the software to keep track of samples and to provide information on the sample management process to other users and software programs within BioWatch. The information tracked about a sample includes the date and time the filter media is assembled in a filter holder, deployed to the field, placed in and removed from a collector, and received from the field, and identifies who performs each task. Field Operators can also use the software to prepare and print forms to document the chain-of-custody transfer of the samples. The BioWatch SMS Field Operator Guide provides easy-to-understand procedures for each task a Field Operator performs. This guide gives basic information about using the computer and other equipment, instructions about checking and editing data, and provides reference information, including appendices of error messages and solutions, words used interchangeably, and safety warnings plus a glossary and an index.
With cyber-attacks on the rise, information is one of the first lines of defense. Now more than ever, security experts and developers need efficient ways to know if their software may be outdated or vulnerable to emerging threats. By generating a Software Bill of Material based on STIX2/JSON bundles, security experts and developers can ensure that they know where to find what can go wrong with their systems before the hackers do. As part of the Infrastructure Expression project, Idaho National Laboratory (INL) is currently working on ways that can aid in the development of efficient SBOMs using Structured Threat Information Expression (STIX). Doing so can provide an efficient first line of defense for detection and monitoring that can be easily used by cyber personnel from beginners to highly skilled experts.
High-level synthesis (HLS) research generally focuses on transferring "software engineering virtues" (e.g., modularity, abstraction, extensibility, etc.) to hardware development with the ultimate goal of making hardware development as agile as software development. And recent HLS research has focused on transferring ideas and techniques from high assurance software formal methods to hardware development. Just as it has introduced software engineering virtues, we believe HLS can also become a vector for adapting software formal methods to the challenge of high assurance security in hardware. This paper introduces the Device Calculus and its mechanization in the Agda proof checking system. The Device Calculus is a starting point for exploring the formal methods and security of high-level synthesis flows. We illustrate the Device Calculus with a number of examples of formally verifiable security templates-i.e., functions in the Device Calculus that express common security structures at a high-level of abstraction.
Modern society is dependent on software systems of remarkable scope and complexity. Yet methods for assuring their security and functionality have not kept pace. The result is persistent compromises and failures despite best efforts. Cybersecurity methods must work together for situational awareness, attack prevention and detection, threat attribution, minimization of consequences, and attack recovery. Because defective software cannot be secure, assurance technologies must play a central role in cybersecurity approaches. There is increasing recognition of the need for rigorous methods for cybersecurity and software assurance. The goal of this minitrack is to develop science foundations, technologies, and practices that can improve the security and dependability of complex systems.
Sandia National Laboratories (SNL) continued evaluation of total system performance assessment (TSPA) computing systems for the previously considered Yucca Mountain Project (YMP). This was done to maintain the operational readiness of the computing infrastructure (computer hardware and software) and knowledge capability for total system performance assessment (TSPA) type analysis, as directed by the National Nuclear Security Administration (NNSA), DOE 2010. This work is a continuation of the ongoing readiness evaluation reported in Lee and Hadgu (2014), Hadgu et al. (2015) and Hadgu and Appel (2016), and Hadgu et al. (2017). The TSPA computing hardware (2014 server cluster -CL2014) and storage system described in Hadgu et al. (2015) were used for the current analysis. One floating license of Gold Sim with Versions 9.60.300, 10.5, 11.1 and 12.0 was installed on the cluster head node, and its distributed processing capability was mapped on the cluster processors. Other supporting software were tested and installed to support the TSPA-type analysis on the server cluster. The FY18 task included developing an inventory of software used for the Yucca Mountain Project process models and preliminary assessment of status of the software; enhancing security of the cluster and setting a backup system. The 2014 server cluster and supporting software systems are fully operational to support TSPA-LA type analysis.
The U.S Department of Energy (DOE) National Nuclear Security Administration (NNSA) Next Generation Safeguards Initiative (NGSI) and the International Atomic Energy Agency (IAEA) convened a workshop on Software Sustainability for Safeguards Instrumentation in Vienna, Austria, May 6-8, 2014. Safeguards instrumentation software must be sustained in a changing environment to ensure existing instruments can continue to perform as designed, with improved security. The approaches to the development and maintenance of instrument software used in the past may not be the best model for the future and, therefore, the organizers’ goal was to investigate these past approaches and to determine an optimal path forward. The purpose of this report is to provide input for the DOE NNSA Office of International Nuclear Safeguards (NA-241) and other stakeholders that can be utilized when making decisions related to the development and maintenance of software used in the implementation of international nuclear safeguards. For example, this guidance can be used when determining whether to fund the development, upgrade, or replacement of a particular software product. The report identifies the challenges related to sustaining software, and makes recommendations for addressing these challenges, supported by summaries and detailed notes from the workshop discussions. In addition the authors provide a set of recommendations for institutionalizing software sustainability practices in the safeguards community. The term “software sustainability” was defined for this workshop as ensuring that safeguards instrument software and algorithm functionality can be maintained efficiently throughout the instrument lifecycle, without interruption and providing the ability to continue to improve that software as needs arise.
The BioWatch Sample Management System is software developed by Los Alamos National Laboratory to support the Department of Homeland Security BioWatch program. Field Operations Supervisors use the software to record, track, and manage data for the BioWatch sample collection process. This includes installing the software, creating and updating databases, assigning user IDs and passwords, editing and customizing reports, correcting data errors, recovering lost data, and troubleshooting. The BioWatch SMS Field Operations Supervisor Guide provides procedures for each task a Field Operations Supervisor performs. The reference information in this guide includes appendices of error messages and solutions, terms used interchangeably, and safety warnings along with a glossary, an index, and a list of suggested reference documents.
Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.
This paper discusses the key challenges and future research directions for privacy-preserving federated learning (PPFL), with a focus on its application to large-scale scientific AI models, in particular, foundation models~(FMs). PPFL enables collaborative model training across distributed datasets while preserving privacy-- an important collaborative approach for science. We discuss the need for efficient and scalable algorithms to address the increasing complexity of FMs, particularly when dealing with heterogeneous clients. In addition, we underscore the need for developing advance privacy-preserving techniques, such as differential privacy, to balance privacy and utility in large FMs emphasizing fairness and incentive mechanisms to ensure equitable participation among heterogeneous clients. Finally, we emphasize the need for a robust software stack supporting scalable and secure PPFL deployments across multiple high-performance computing facilities. We envision that PPFL would play a crucial role to advance scientific discovery and enable large-scale, privacy-aware collaborations across science domains.
This report provides the data qualification status of AGR-2 fuel irradiation experimental data from Advanced Test Reactor (ATR) cycles 149B, 150A, 150B, 151A, and 151B), as recorded in the Nuclear Data Management and Analysis System (NDMAS). The AGR-2 data streams addressed include thermocouple temperatures, sweep gas data (flow rate, pressure, and moisture content), and fission product monitoring system (FPMS) data for each of the six capsules in the experiment. A total of 3,307,500 5-minute thermocouple and sweep gas data records were received and processed by NDMAS for this period. There are no AGR-2 data for cycle 150A because the experiment was removed from the reactor. Of these data, 82.2% were determined to be Qualified based on NDMAS accuracy testing and data validity assessment. There were 450,557 Failed temperature records due to thermocouple failures, and 138,528 Failed gas flow records due to gas flow cross-talk and leakage problems that occurred in the capsules after cycle 150A. For FPMS data, NDMAS received and processed preliminary release rate and release-to-birth rate ratio (R/B) data for the first three reactor cycles (cycles 149B, 150B, and 151B). This data consists of 45,983 release rate records and 45,235 R/B records for the 12 radionuclides reported. The qualification status of these FPMS data has been set to In Process until receipt of QA-approved data generator reports. All of the above data have been processed and tested using a SAS®-based enterprise application software system, stored in a secure Structured Query Language database, and made available on the NDMAS Web portal (http://ndmas.inl.gov) for both internal and external VHTR project participants.
This report documents the data qualification status of AGR-2 fuel irradiation experimental data from Advanced Test Reactor (ATR) Cycles 152A, 152B, 153A, 153B, and 154A, as recorded in the Nuclear Data Management and Analysis System (NDMAS). The AGR-2 data streams addressed include thermocouple (TC) temperatures, sweep gas data (flow rate, pressure, and moisture content), and fission product monitoring system (FPMS) data for each of the six capsules in the experiment. A total of 13,400,520 every minute instantaneous TC and sweep gas data records were received and processed by NDMAS for this period. Of these data, 8,911,791 records (66.5% of the total) were determined to be Qualified based on NDMAS accuracy testing and data validity assessment. For temperature, there were 4,266,081 records (74% of the total TC data) that were Failed due to TC instrument failures. For sweep gas flows, there were 222,648 gas flow records (2.91% of the flow data) that were Failed. The inlet gas flow failures due to gas flow cross-talk and leakage problems that occurred after Cycle 150A were corrected by using the same gas mixture in all six capsules and the Leadout. For FPMS data, NDMAS received and processed preliminary release rate and release-to-birth rate ratio (R/B) data for three reactor cycles (Cycles 149B, 150B, and 151A) . This data consists of 45,983 release rate records and 45,235 R/B records for the 12 radionuclides reported. The qualification status of these FPMS data has been set to In Process until receipt of Quality Assurance-approved data generator reports. All of the above data have been processed and tested using a SAS®-based enterprise application software system, stored in a secure Structured Query Language database, made available on the NDMAS Web portal (http://ndmas.inl.gov), and approved by the INL STIM for release to both internal and appropriate external Very High Temperature Reactor Program participants.
This document describes technical implementation details on network access schemes connecting API-driven workflows to supercomputer centers. API-driven workflows are a central theme in connected computing, since they bring the terminal-mainframe' access pattern present since the 1970s up to the task of interfacing with modern web browser technologies. Both security (HTTPS/TLS/IPSec/VPNs/public key cryptography/digital signatures) and network protocol stacks (HTTP-REST APIs, tokens, gRPC, SRTP) have evolved to the point where implementing API-driven workflows is possible using stable, secure off-the-shelf software.
The exe-Guard Project is an alliance between Dominion Virginia Power (DVP), Sandia National Laboratories (SNL), Dartmouth University, and Schweitzer Engineering Laboratories (SEL). SEL is primary recipient on this project. The exe-Guard project was selected for award under DE-FOA-0000359 with CFDA number 81.122 to address Topic Area of Interest 4: Hardened platforms and Systems. The exe-Guard project developed an antivirus solution for control system embedded devices to prevent the execution of unauthorized code and maintain settings and configuration integrity. This project created a white list antivirus solution for control systems capable of running on embedded Linux® operating systems. White list antivirus methods allow only credible programs to run through the use of digital signatures and hash functions. Once a system’s secure state is baselined, white list antivirus software denies deviations from that state because of the installation of malicious code as this changes hash results. Black list antivirus software has been effective in traditional IT environments but has negative implications for control systems. Black list antivirus uses pattern matching and behavioral analysis to identify system threats while relying on regular updates to the signature file and recurrent system scanning. Black list antivirus is vulnerable to zero day exploits which have not yet been incorporated into a signature file update. System scans hamper the performance of high availability applications, as revealed in NIST special publication 1058 which summarizes the impact of blacklist antivirus on control systems: Manual or “on-demand” scanning has a major effect on control processes in that they take CPU time needed by the control process (Sometimes close to 100% of CPU time). Minimizing the antivirus software throttle setting will reduce but not eliminate this effect. Signature updates can also take up to 100% of CPU time, but for a much shorter period than a typical manual scanning process. Control systems are vulnerable to performance losses if off-the-shelf blacklist antivirus solutions aren’t implemented with care. This investment in configuration in addition to constant decommissioning to perform manual signature file updates is unprecedented and impractical. Additionally, control systems are often disconnected or islanded from the network making the delivery of signature updates difficult. Exe-Guard project developed a white list antivirus solution that mitigated the above drawbacks and allows control systems to cost-effectively apply malware protection while maintaining high reliability. The application of security patches can also be minimized since white listing maintains constant defense against unauthorized code execution. Security patches can instead be applied in less frequent intervals where system decommissioning can be scheduled and planned for. Since control systems are less dynamic than IT environments, the feasibility of maintaining a secure baselined state is more practical. Because upgrades are performed in infrequent, calculated intervals, it allows a new security baseline to be established before the system is returned to service. Exe-Guard built on the efforts of SNL under the Code Seal project. SNL demonstrated prototype Trust Anchors on the project which are independent monitoring and control devices that can be integrated into untrustworthy components. The exe-Guard team started with the lessons learned under this project then designed commercial solution for white list malware protection. Malware is a real threat, even on islanded or un-networked installations, since operators can unintentionally install infected files, plug in infected mass storage devices, or infect a piece of equipment on the islanded local area network that can then spread to other connected equipment. Protection at the device level is one of the last layers of defense in a security-in-depth defense model before an asset becomes compromised. This project provided non-destructive intrusion, isolation and automated response solution, achieving a goal of the Department of Energy (DOE) Roadmap to Secure Control Systems. It also addressed CIP-007-R4 which requires asset owners to employ malicious software prevention tools on assets within the electronic security perimeter. In addition, the CIP-007-R3 requirement for security patch management is minimized because white listing narrows the impact of vulnerabilities and patch releases. The exe-Guard Project completed all tasks identified in the statement of project objective and identified additional tasks within scope that were performed and completed within the original budget. The cost share was met and all deliverables were successfully completed and submitted on time. Most importantly the technology developed and commercialized under this project has been adopted by the Energy sector and thousands of devices with exe-Guard technology integrated in them have now been deployed and are protecting our power systems today
The evolution of cyber-physical infrastructure has made its security more challenging. The last few years have witnessed a convergence of hardware and software segments in various domains, including operational technology (OT) which is responsible for carrying out critical tasks such as monitoring and controlling power grids, nuclear plants, transportation, and emergency services. Both hardware and software encapsulate numerous open source and proprietary subcomponents, making it crucial for end-users to understand the composition of the products they are using. For example, wind turbines incorporate thousands of lines of code (software) used for the turbine's design, planning, operation, and analytics in addition to the numerous hardware subcomponents that construct it. Due to the highly complex nature of software and hardware, knowledge of the components and subcomponents is required to mitigate cyber vulnerabilities and defend against cyberattacks. There has also been a transformation from a traditional linear supply chain into a global, dynamic, diverse, and interconnected system. The digitization of the supply chain makes it easier to find and exploit vulnerabilities. Critical infrastructures (e.g., power grids, oil, natural gas, water, and wastewater) rely on OT to function, and if the OT is compromised, equipment damage and potential interruption of services could result. A significant security measure to protect OT systems from disruption is to develop a supply chain bill of materials (BoM) corresponding to the software and hardware used in OT, along with attestations amongst vendors and asset owners. A supply chain BoM is a proactive way to understand the inherent vulnerabilities in the system and mitigate them in advance of being exploited. BoMs bolster the trust placed in the digital infrastructure and enhance software supply chain security by sustaining the management of component obsolescence and compliance, along with the seclusion of unsafe segments of a specific product. Adopting BoM tools is becoming increasingly important across various government sectors, as evidenced by the recent U.S. executive order on cybersecurity (NIST 2021). This paper aims to classify BoMs based on structure, functionality, component type, and architecture. The work also discusses case studies to further highlight the benefits of BoMs. In addition, it identifies missing pieces in existing BoM implementations so that future research may identify bounds on where it could expect to make improvements and directly enable researchers to identify promising areas for exploration. Further, the authors provide valuable recommendations to tool developers, researchers, and standardizing organizations (policymakers), additionally benefitting critical infrastructure owners and government executives. This aids in paving a path for future work, thereby, providing suggestions to determine a tool for consumers that best suit their needs.
Here we present Singularity, software developed to bring containers and reproducibility to scientific computing. Using Singularity containers, developers can work in reproducible environments of their choosing and design, and these complete environments can easily be copied and executed on other platforms. Singularity is an open source initiative that harnesses the expertise of system and software engineers and researchers alike, and integrates seamlessly into common workflows for both of these groups. As its primary use case, Singularity brings mobility of computing to both users and HPC centers, providing a secure means to capture and distribute software and compute environments. This ability to create and deploy reproducible environments across these centers, a previously unmet need, makes Singularity a game changing development for computational science.