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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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At least 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

SHARP-Net: Platform for Self-Healing and Attack Resilient PMU Networks

Synchrophasor technology plays a pivotal role in developing the next generation of wide-area monitoring, protection, and control in the smart grid environment. As technology and communications infrastructures evolve, however, so do the attack surfaces in the synchrophasor network that can be exploited by advanced persistent threat (APT) actors to affect power system stability and reliability. In this paper, we propose a novel platform for developing a self-healing and attack-resilient PMU network (SHARP-Net) by instituting a state-of-the-art intrusion detection system (IDS) with an intrusion mitigation system (IMS) and an alert management system (AMS). In particular, the proposed platform detects anomalies during cyberattacks on phasor data concentrators (PDCs) based on the rules defined in the IDS, then the generated alerts are published to the IMS through the AMS. The proposed IMS proceeds to take automated corrective responses to mitigate cyberattacks by reconfiguring the synchrophasor network to isolate the compromised PDCs, and it orchestrates new PDCs to prevent the future propagation of attacks. Further, the IMS restores the system's observability by reconnecting the new PDCs to make the grid attack-resilient. In this work, the SHARP-Net platform is developed by using Python-based libraries, minimega's software-defined network, and virtual machine orchestration. We implement and validate the proposed SHARP-Net architecture by testing a PMU network in the smart grid environment. SHARP-Net showed promising performance in detecting cyberattacks and mitigating them through the network reconfiguration.

computer architecture↗
DOE OSTI2015

Social Sensor Analytics: Making Sense of Network Models in Social Media

Social networks can be thought of as noisy sensor networks mapping real world information to the web. Owing to the extensive body of literature in sensor network analysis, this work sought to apply several novel and traditional methods in sensor network analysis for the purposes of efficiently interrogating social media data streams from raw data. We carefully revisit our definition of a social media signal from previous work both in terms of time-varying features within the data and the networked nature of the medium. Further, we detail our analysis of global patterns in Twitter over the months of November 2013 and June 2014, detect and categorize events, and illustrate how these analyses can be used to inform graph-based models of Twitter, namely using a recent network influence model called PhySense: similar to PageRank but tuned to behavioral analysis by leveraging a sociologically inspired probabilistic model. We ultimately identify forms of information dissemination via analysis of time series and dynamic graph spectra and corroborate these findings through manual investigation of the data as a requisite step in modeling the diffusion process with PhySense. We hope to sufficiently characterize global behavior in a medium such as Twitter as a means of learning global model parameters one may use to predict or simulate behavior on a large scale. We have made our time series and dynamic graph analytical code available via a GitHub repository https://github.com/cpatdowling/salsa and our data are available upon request.

event detection↗
DOE OSTI2024

Losing Control of Your Linear Network? Try Resilience Theory

Resilience of cyber-physical networks to unexpected failures is a critical need widely recognized across domains. For instance, power grids, telecommunication networks, transportation infrastructures, and water treatment systems have all been subject to disruptive malfunctions and catastrophic cyberattacks. Following such adverse events, we investigate scenarios where a node of a linear network suffers a loss of control authority over some of its actuators. These actuators are not following the controller's commands and are instead producing undesirable outputs. The repercussions of such a loss of control can propagate and destabilize the whole network despite the malfunction occurring at a single node. To assess system vulnerability, we establish resilience conditions for networks with a subsystem enduring a loss of control authority over some of its actuators. Furthermore, we quantify the destabilizing impact on the overall network when such a malfunction perturbs a nonresilient subsystem. We illustrate our resilience conditions on two academic examples, on an islanded microgrid and on the linearized IEEE 39-bus system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Autonomic Intelligent Cyber Sensor to Support Industrial Control Network Awareness

The proliferation of digital devices in a networked industrial ecosystem, along with an exponential growth in complexity and scope, has resulted in elevated security concerns and management complexity issues. This paper describes a novel architecture utilizing concepts of Autonomic computing and a SOAP based IF-MAP external communication layer to create a network security sensor. This approach simplifies integration of legacy software and supports a secure, scalable, self-managed framework. The contribution of this paper is two-fold: 1) A flexible two level communication layer based on Autonomic computing and Service Oriented Architecture is detailed and 2) Three complementary modules that dynamically reconfigure in response to a changing environment are presented. One module utilizes clustering and fuzzy logic to monitor traffic for abnormal behavior. Another module passively monitors network traffic and deploys deceptive virtual network hosts. These components of the sensor system were implemented in C++ and PERL and utilize a common internal D-Bus communication mechanism. A proof of concept prototype was deployed on a mixed-use test network showing the possible real world applicability. In testing, 45 of the 46 network attached devices were recognized and 10 of the 12 emulated devices were created with specific Operating System and port configurations. Additionally the anomaly detection algorithm achieved a 99.9% recognition rate. All output from the modules were correctly distributed using the common communication structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Adaptive Protection of Scientific Backbone Networks Using Machine Learning

In this article, we propose a new protection scheme for backbone networks to guarantee high service availability. The presented scheme does not require any reconfiguration immediately after the failure (i.e., it is proactive). At the same time, it does not require any reserved backup network resources either. To achieve these seemingly contradictory goals, we utilize the recent advancements in Machine Learning (ML) to implement a network intelligence that periodically re-allocates the unused capacity as protection bandwidth to meet the service availability requirements of each connection. Our goal is achieved by two components (1) predicting the traffic for the next period on each link, and (2) intelligently selecting the best fit dedicated protection scheme for the next period depending on the estimated unused (spare) bandwidth and the previous service availability violations. Note that re-allocating protection bandwidth affects neither the operational connections nor the current best practice of operators to over-provision network bandwidth to support elephant flows. Finally, we provide a case study on the real traffic from Energy Sciences Network (ESnet), a high-speed, international scientific backbone network. The key benefit of our framework is that adaptively utilizing the over-provisioned bandwidth for spare capacity is sufficient to improve the availability from three-nines to five-nines (in ESnet for the 30 examined connections). The drawback is negligible bandwidth limitations; the user perceives a minor and very temporal bandwidth limitation in less than 0.1% of the time.

42 ENGINEERING↗
DOE OSTI2016

Optimizing End-to-End Big Data Transfers over Terabits Network Infrastructure

While future terabit networks hold the promise of significantly improving big-data motion among geographically distributed data centers, significant challenges must be overcome even on today's 100 gigabit networks to realize end-to-end performance. Multiple bottlenecks exist along the end-to-end path from source to sink, for instance, the data storage infrastructure at both the source and sink and its interplay with the wide-area network are increasingly the bottleneck to achieving high performance. In this study, we identify the issues that lead to congestion on the path of an end-to-end data transfer in the terabit network environment, and we present a new bulk data movement framework for terabit networks, called LADS. LADS exploits the underlying storage layout at each endpoint to maximize throughput without negatively impacting the performance of shared storage resources for other users. LADS also uses the Common Communication Interface (CCI) in lieu of the sockets interface to benefit from hardware-level zero-copy, and operating system bypass capabilities when available. It can further improve data transfer performance under congestion on the end systems using buffering at the source using flash storage. With our evaluations, we show that LADS can avoid congested storage elements within the shared storage resource, improving input/output bandwidth, and data transfer rates across the high speed networks. We also investigate the performance degradation problems of LADS due to I/O contention on the parallel file system (PFS), when multiple LADS tools share the PFS. We design and evaluate a meta-scheduler to coordinate multiple I/O streams while sharing the PFS, to minimize the I/O contention on the PFS. Finally, with our evaluations, we observe that LADS with meta-scheduling can further improve the performance by up to 14 percent relative to LADS without meta-scheduling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Self-Healing Attack-Resilient PMU Network for Power System Operation

In this paper, we propose a self-healing phasor measurement unit (PMU) network that exploits the features of dynamic and programmable configuration in a software-defined networking infrastructure to achieve resiliency against cyber-attacks. After a cyber-attack, the configuration of network switches is changed to isolate the compromised PMUs/phasor data concentrators to prevent further propagation of the attack; meanwhile, the disconnected yet uncompromised PMUs will be reconnected to the network to "self-heal" and thus restore the observability of the power system. Specifically, we formulate an integer linear programming model to minimize the overhead of the self-healing process (e.g., the recovery latency), while considering the constraints of power system observability, hardware resources, and network topology. We also propose a heuristic algorithm to decrease the computational complexity. Case studies of a PMU network based on the IEEE 30-bus and 118-bus systems are used to validate the effectiveness of the self-healing mechanism.

Cybersecurity↗
DOE OSTI2020

Layer-Parallel Training of Deep Residual Neural Networks

Residual neural networks (ResNets) are a promising class of deep neural networks that have shown excellent performance for a number of learning tasks, e.g., image classification and recognition. Mathematically, ResNet architectures can be interpreted as forward Euler discretizations of a nonlinear initial value problem whose time-dependent control variables represent the weights of the neural network. Hence, training a ResNet can be cast as an optimal control problem of the associated dynamical system. For similar time-dependent optimal control problems arising in engineering applications, parallel-in-time methods have shown notable improvements in scalability. This paper demonstrates the use of those techniques for efficient and effective training of ResNets. The proposed algorithms replace the classical (sequential) forward and backward propagation through the network layers with a parallel nonlinear multigrid iteration applied to the layer domain. This adds a new dimension of parallelism across layers that is attractive when training very deep networks. From this basic idea, we derive multiple layer-parallel methods. The most efficient version employs a simultaneous optimization approach where updates to the network parameters are based on inexact gradient information in order to speed up the training process. Finally, using numerical examples from supervised classification, we demonstrate that the new approach achieves a training performance similar to that of traditional methods, but enables layer-parallelism and thus provides speedup over layer-serial methods through greater concurrency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Towards Compact Neural Networks via End-to-End Training: A Bayesian Tensor Approach with Automatic Rank Determination

Post-training model compression can reduce the inference costs of deep neural networks, but uncompressed training still consumes enormous hardware resources and energy. To enable low-energy training on edge devices, it is highly desirable to directly train a compact neural network from scratch with a low memory cost. Low-rank tensor decomposition is an effective approach to reduce the memory and computing costs of large neural networks. However, directly training low-rank tensorized neural networks is a very challenging task because it is hard to determine a proper tensor rank a priori, and the tensor rank controls both model complexity and accuracy. Here, this paper presents a novel end-to-end framework for low-rank tensorized training. We first develop a Bayesian model that supports various low-rank tensor formats (e.g., CANDECOMP/PARAFAC, Tucker, tensor-train, and tensor-train matrix) and reduces neural network parameters with automatic rank determination during training. Then we develop a customized Bayesian solver to train large-scale tensorized neural networks. Our training methods shows orders-of-magnitude parameter reduction and little accuracy loss (or even better accuracy) in the experiments. On a very large deep learning recommendation system with over 4.2 ×10 9 model parameters, our method can reduce the parameter number to 1.6 ×10 5 automatically in the training process (i.e., by 2.6 ×10 4 times) while achieving almost the same accuracy. Code is available at https://github.com/colehawkins/bayesian-tensor-rank-determination.

compact neural networks↗
DOE OSTI2020

PETSc DMNetwork: A Library for Scalable Network PDE-Based Multiphysics Simulations

We present DMNetwork, a high-level package included in the PETSc library for the simulation of multiphysics phenomena over large-scale networked systems. The library aims at applications that have networked structures such as those in electrical, gas, and water distribution systems. DMNetwork provides data and topology management, parallelization for multiphysics systems over a network, and hierarchical and composable solvers to exploit the problem structure. DMNetwork eases the simulation development cycle by providing the necessary infrastructure through simple abstractions to define and query the network components. This article presents the design of DMNetwork, illustrates its user interface, and demonstrates its ability to solve multiphysics systems, such as an electric circuit, a network of power grid and water subnetworks, and transient hydraulic systems over large networks with more than 2 billion variables on extreme-scale computers using up to 30,000 processors.

extreme-scale computing↗
DOE OSTI2014

Exact reconstruction of gene regulatory networks using compressive sensing

We consider the problem of reconstructing a gene regulatory network structure from limited time series gene expression data, without any a priori knowledge of connectivity. We assume that the network is sparse, meaning the connectivity among genes is much less than full connectivity. We develop a method for network reconstruction based on compressive sensing, which takes advantage of the network’s sparseness. For the case in which all genes are accessible for measurement, and there is no measurement noise, we show that our method can be used to exactly reconstruct the network. For the more general problem, in which hidden genes exist and all measurements are contaminated by noise, we show that our method leads to reliable reconstruction. In both cases, coherence of the model is used to assess the ability to reconstruct the network and to design new experiments. We demonstrate that it is possible to use the coherence distribution to guide biological experiment design effectively. By collecting a more informative dataset, the proposed method helps reduce the cost of experiments. For each problem, a set of numerical examples is presented. The method provides a guarantee on how well the inferred graph structure represents the underlying system, reveals deficiencies in the data and model, and suggests experimental directions to remedy the deficiencies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Quantification of neural networks uncertainties with applications to SAFARI-1 axial neutron flux profiles

Deep Neural Networks (DNNs) have been widely used as a data-driven modelling tool in nuclear engineering. However, as a Machine Learning model, Artificial Neural Network (ANN) predictions are subjected to uncertainties originating from the noise in training data, incomplete coverage of the domain, and imperfect neural network architectures. In this work, we target at quantifying the prediction/approximation uncertainties of ANNs using Monte Carlo Dropout (MCD), as well as Bayesian Neural Networks (BNNs) which are solved by variational inference. With a demonstration problem in which neural networks are used to predict the assembly axial neutron flux profiles, the results have shown that the three different neural network models (regular DNNs, DNNs solved with MCD and BNNs) can produce results that agree very well among each other and with the measurement data, on cycles that are not used in the training process. Besides the excellent generalization capability, the uncertainty bands produced by MCD and BNN agree very well, and in general, they can fully envelop the noisy measurement data points. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Inference of Transmission Network Structure from HIV Phylogenetic Trees

Phylogenetic inference is an attractive means to reconstruct transmission histories and epidemics. However, there is not a perfect correspondence between transmission history and virus phylogeny. Both node height and topological differences may occur, depending on the interaction between within-host evolutionary dynamics and between-host transmission patterns. To investigate these interactions, we added a within-host evolutionary model in epidemiological simulations and examined if the resulting phylogeny could recover different types of contact networks. To further improve realism, we also introduced patient-specific differences in infectivity across disease stages, and on the epidemic level we considered incomplete sampling and the age of the epidemic. Second, we implemented an inference method based on approximate Bayesian computation (ABC) to discriminate among three well-studied network models and jointly estimate both network parameters and key epidemiological quantities such as the infection rate. Our ABC framework used both topological and distance-based tree statistics for comparison between simulated and observed trees. Overall, our simulations showed that a virus time-scaled phylogeny (genealogy) may be substantially different from the between-host transmission tree. This has important implications for the interpretation of what a phylogeny reveals about the underlying epidemic contact network. In particular, we found that while the within-host evolutionary process obscures the transmission tree, the diversification process and infectivity dynamics also add discriminatory power to differentiate between different types of contact networks. We also found that the possibility to differentiate contact networks depends on how far an epidemic has progressed, where distance-based tree statistics have more power early in an epidemic. Finally, we applied our ABC inference on two different outbreaks from the Swedish HIV-1 epidemic.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

This work is the first to take advantage of recurrent neural networks to predict influenza-like-illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data [1, 2] and the state-of-the-art machine learning models [3, 4], we build and evaluate the predictive power of Long Short Term Memory (LSTMs) architectures capable of nowcasting (predicting in \real-time") and forecasting (predicting the future) ILI dynamics in the 2011 { 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, stylistic and syntactic patterns, emotions and opinions, and communication behavior. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks. Finally, we combine ILI and social media signals to build joint neural network models for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance [1], specifically for military rather than general populations [3] in 26 U.S. and six international locations. Our approach demonstrates several advantages: (a) Neural network models learned from social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than syntactic and stylistic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

US LHCNet: Transatlantic Networking for the LHC and the U.S. HEP Community

US LHCNet provides the transatlantic connectivity between the Tier1 computing facilities at the Fermilab and Brookhaven National Labs and the Tier0 and Tier1 facilities at CERN, as well as Tier1s elsewhere in Europe and Asia. Together with ESnet, Internet2, and other R&E Networks participating in the LHCONE initiative, US LHCNet also supports transatlantic connections between the Tier2 centers (where most of the data analysis is taking place) and the Tier1s as needed. Given the key roles of the US and European Tier1 centers as well as Tier2 centers on both continents, the largest data flows are across the Atlantic, where US LHCNet has the major role. US LHCNet manages and operates the transatlantic network infrastructure including four Points of Presence (PoPs) and currently six transatlantic OC-192 (10Gbps) leased links. Operating at the optical layer, the network provides a highly resilient fabric for data movement, with a target service availability level in excess of 99.95%. This level of resilience and seamless operation is achieved through careful design including path diversity on both submarine and terrestrial segments, use of carrier-grade equipment with built-in high-availability and redundancy features, deployment of robust failover mechanisms based on SONET protection schemes, as well as the design of facility-diverse paths between the LHC computing sites. The US LHCNet network provides services at Layer 1(optical), Layer 2 (Ethernet) and Layer 3 (IPv4 and IPv6). The flexible design of the network, including modular equipment, a talented and agile team, and flexible circuit lease management, allows US LHCNet to react quickly to changing requirements form the LHC community. Network capacity is provisioned just-in-time to meet the needs, as demonstrated in the past years during the changing LHC start-up plans.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Analysis and Reduction of Complex Networks Under Uncertainty

This effort was a collaboration with Youssef Marzouk of MIT, Omar Knio of Duke University (at the time at Johns Hopkins University) and Habib Najm of Sandia National Laboratories. The objective of this effort was to develop the mathematical and algorithmic capacity to analyze complex networks under uncertainty. Of interest were chemical reaction networks and smart grid networks. The statements of work for USC focused on the development of stochastic reduced models for uncertain networks. The USC team was led by Professor Roger Ghanem and consisted of one graduate student and a postdoc. The contributions completed by the USC team consisted of 1) methodology and algorithms to address the eigenvalue problem, a problem of significance in the stability of networks under stochastic perturbations, 2) methodology and algorithms to characterize probability measures on graph structures with random flows. This is an important problem in characterizing random demand (encountered in smart grid) and random degradation (encountered in infrastructure systems), as well as modeling errors in Markov Chains (with ubiquitous relevance!). 3) methodology and algorithms for treating inequalities in uncertain systems. This is an important problem in the context of models for material failure and network flows under uncertainty where conditions of failure or flow are described in the form of inequalities between the state variables.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Low Emission Development Strategies: The Role of Networks and Knowledge Platforms

Considerable effort has been made to address the transition to low-carbon economy. A key focus of these efforts has been on the development of national low-emissions developments strategies (LEDS). One enabler of these plans is the existence of well-functioning national, regional and international low-emission development networks and knowledge platforms. To better understand the role of LEDS, weexamine this area in relation to network theory. We present a review of strengths and weaknesses of existing LEDS networks that builds on the findings of a study conducted by the Coordinated Low Emission Assistance Network (CLEAN). Based on the insights from theory and a mapping of the climate-related network space, we identify opportunities for further refinement of LEDS networks.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Mitigate: An Adaptive Network Data Anonymization Tool Using Condensation-Based Differential Privacy

Modern network devices collect a large amount of data that can be analyzed to identify bottlenecks, anomalies, cyber-attacks, etc. Therefore, there is often a need to analyze such collections of network data quite often by an external expert or by the research community. However, these collections of data contain sensitive, proprietary information. In order for the network data to be shared, it must first be anonymized. The overall objective of this project is to develop an innovative privacy management tool to anonymize network data and achieve sufficient privacy, acceptable data utility, and efficient data analysis at the same time. No existing anonymization methods can achieve all of these at the same time. The core of this technology is a differential private clustering algorithm that provides strong privacy protection, preserves data properties important for subsequent analysis, and allows the party receiving the anonymized data to conduct analysis directly on anonymized data without the need of decryption or any extra processing. The research carried out was to design, implement and verify a solution to this problem by completing the following tasks: 1) developing the core technology; 2) developing a context based method that automatically recommends fields that must be anonymized; 3) conducted experiments showing superior results using our approach compared to existing tools, and 4) developed an intuitive but basic user interface. The research that was conducted generated novel algorithmic techniques that utilize state-of-the-art methods such as condensation, differential privacy preservation, clustering, automated tuning based on contextual awareness, and recommendation techniques to specify columns to users for anonymization leading to optimal privacy that allows research analysis on the dataset. Experiments were conducted to evaluate the efficacy of these novel algorithmic techniques by performing analysis on original non-anonymized datasets, then conducting analysis on the same yet anonymized datasets and comparing the results of the analyses. Overall, the anonymized analysis results were within 1% of the original results, verifying that the generated technology not only guarantees a high level of privacy but also enables research analysis as if it were conducted on the original dataset. Potential applications of this technology include anonymization of any type of structured network datasets that contain sensitive identifiers, such as IP addresses, that can be used in multiple applications. For example, to create an AI or machine learning model for cyber security, e.g., to detect attacks, or for performance analysis, e.g., identify bottlenecks or predict performance. In addition, a market analysis that was conducted for potential applications of this technology identified a broader range of applications of our anonymization technology beyond the network sector that includes healthcare, banking, insurance, securities, finance (FISB), data brokering, cloud services, ad sales, and government.

97 MATHEMATICS AND COMPUTING↗