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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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14 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

An approach to online network monitoring using clustered patterns

Network traffic monitoring is a core element in network operations and management for various purposes such as anomaly detection, change detection, and fault/failure detection. In this study, we introduce a new approach to online monitoring using a pattern-based representation of the network traffic. Unlike the past online techniques limited to a single variable to summarize (e.g., sketch), the focus of this study is on capturing the network state from the multivariate attributes under consideration. To this end, we employ clustering with its benefit of the aggregation of multidimensional variables. The clustered result represents the state of the network with regard to the monitored variables, which can also be compared with the previously observed patterns visually and quantitatively. Finally, we demonstrate the proposed method with two popular use cases, one for estimating state changes and the other for identifying anomalous states, to confirm its feasibility.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

A lightweight network anomaly detection technique

While the network anomaly detection is essential in network operations and management, it becomes further challenging to perform the first line of detection against the exponentially increasing volume of network traffic. In this paper, we develop a technique for the first line of online anomaly detection with two important considerations: (i) availability of traffic attributes during the monitoring time, and (ii) computational scalability for streaming data. The presented learning technique is lightweight and highly scalable with the beauty of approximation based on the grid partitioning of the given dimensional space. With the public traffic traces of KDD Cup 1999 and NSL-KDD, we show that our technique yields 98.5% and 83% of detection accuracy, respectively, only with a couple of readily available traffic attributes that can be obtained without the help of post-processing. Finally, the results are at least comparable with the classical learning methods including decision tree and random forest, with approximately two orders of magnitude faster learning performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

The Open Science Cyber Risk Profile: The Rosetta Stone for Open Science and Cybersecurity

The Open Science Cyber Risk Profile (OSCRP) working group has created a document that motivates scientists by demonstrating how improving their security posture reduces the risks to their science. This effort aims to both bridge the communication gap between scientists and IT security professionals and allows for the effective management of risks to open science caused by IT security threats.

97 MATHEMATICS AND COMPUTING↗
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 OSTI2019

Descriptive Analytics-Based Anomaly Detection for Cybersecure Load Forecasting

As power delivery systems evolve and become increasingly reliant on accurate forecasts, they will be more and more vulnerable to cybersecurity issues. A coordinated data attack by sophisticated adversaries can render existing data corrupt or outlier detection methods ineffective. This would have a very negative impact on operational decisions. The focus of this paper is to develop descriptive analytics-based methods for anomaly detection to protect the load forecasting process against cyberattacks to essential data. We propose an integrated solution (IS) and a hybrid implementation of IS (HIIS) that can detect and mitigate cyberattack induced long sequence anomalies. HIIS is also capable of improving true positive rates and reducing false positive rates significantly comparing with IS. The proposed HIIS can serve as an online cybersecure load forecasting scheme.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Preventing the Cyber Zombie Apocalypse

Cybercrime rates are on the rise, but what exactly does that mean? Cybercrime is any sort of crime using a computer—simple enough. And now that most people in the United States have a computer or access to one, cybercrime is more common than ever. Los Alamos National Laboratory has been working on cybersecurity techniques, processes and tools to prevent and detect cyberattacks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Analysis in Motion Initiative – Summarization Capability

Analysts are tasked with integrating information from multiple data sources for important and timely decision making. What if sense making and overall situation awareness could be improved through visualization techniques? The Analysis in Motion initiative is advancing the ability to summarize and abstract multiple streams and static data sources over time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Situ/FASGuard: Combining Anomaly Detection with Signature Generation

Despite the best efforts of cyber security analysts, networked computing assets are regularly compromised, resulting in the loss of intellectual property, the disclosure of state secrets, and financial damages in the billions. A 2014 report from the Center for Strategic and International Studies estimated the global cost of cyber crime at $\$$400 billion annually. There has also been a rise of sophisticated attack groups that continually develop novel methods of penetrating networks that current technologies are typically unable to detect.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Protecting drinking water utilities from cyberthreats

Cyber-security challenges have the potential for becoming one of the defining issues of our time. Cyber-attacks have become an ever-increasing threat and the United States (US) Federal Bureau of Investigation (FBI) now ranks cyber-crime as one of its most important law enforcement activities. In addition to the general problems associated with cyber-crime, critical infrastructure (CI) related to energy production, manufacturing, water supply and other systems have come under attack. For example, drinking water utilities are increasingly incorporating computer technology into their routine operations and are therefore increasingly vulnerable to cyber- threats. Systems control and data acquisition (SCADA) systems used to manage automated physical processes essential to water treatment and distribution systems have become standard in medium to large drinking water utilities and in many small water systems. However, even with the application of standard information technology cybersecurity best practices these types of systems have proven to be vulnerable to cyber-attacks. In 2015, the US Department of Homeland Security (DHS) responded to 25 cybersecurity incidents in the Water Sector and to 46 incidents in the Energy Sector. Comparatively, between 2014 and 2015, the reported number of Water Sector incidents actually increased by 78.6% (from 14 to 25). The DHS is in a collaborative partnership with the US Environmental Protection Agency to ensure cybersecurity in the Water Sector. As a result of this partnership a number of guidance documents and techniques have been developed to counter cyber-attacks and minimize cyber vulnerability. These approaches are documented along with a summary of common vulnerabilities. However, a new approach which has great promise in protecting drinking water systems against hacking and cyber-attacks, based on the concept of unidirectional gateways, is presented and discussed.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Estimating economic damage from climate change in the United States

Estimates of climate change damage are central to the design of climate policies. Here, we develop a flexible architecture for computing damages that integrates climate science, econometric analyses, and process models. We use this approach to construct spatially explicit, probabilistic, and empirically derived estimates of economic damage in the United States from climate change. The combined value of market and nonmarket damage across analyzed sectors—agriculture, crime, coastal storms, energy, human mortality, and labor—increases quadratically in global mean temperature, costing roughly 1.2% of gross domestic product per +1°C on average. Importantly, risk is distributed unequally across locations, generating a large transfer of value northward and westward that increases economic inequality. By the late 21st century, the poorest third of counties are projected to experience damages between 2 and 20% of county income (90% chance) under business-as-usual emissions (Representative Concentration Pathway 8.5).

Hsiang, Solomon (ORCID:0000000220740829)↗
DOE OSTI2021

Incorporating space and time into random forest models for analyzing geospatial patterns of drug-related crime incidents in a major U.S. metropolitan area

The opioid crisis has hit American cities hard, and research on spatial and temporal patterns of drug-related activities including detecting and predicting clusters of crime incidents involving particular types of drugs is useful for distinguishing hot zones where drugs are present that in turn can further provide a basis for assessing and providing related treatment services. In this study, we investigated spatiotemporal patterns of more than 52,000 reported incidents of drug-related crime at block group granularity in Chicago, IL between 2016 and 2019. We applied a space-time analysis framework and machine learning approaches to build a model using training data that identified whether certain locations and built environment and sociodemographic factors were correlated with drug-related crime incident patterns, and establish the top contributing factors that underlaid the trends. Space and time, together with multiple driving factors, were incorporated into a random forest model to analyze these changing patterns. We accommodated both spatial and temporal autocorrelation in the model learning process to assist with capturing the changes over time and tested the capabilities of the space-time random forest model by predicting drug-related activity hot zones. Overall, we focused particularly on crime incidents that involved heroin and synthetic drugs as these have been key drug types that have highly impacted cities during the opioid crisis in the U.S.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Robust Approaches to Handling Complex Geometries with Galerkin Difference Methods

The Galerkin difference (GD) basis is a set of continuous, piecewise polynomials defined using a finite-difference-like grid of degrees of freedom. The one dimensional GD basis functions are naturally extended to multiple dimensions using a tensor product construction on quadrilateral elements. The GD basis can be used to define the solution space for a discontinuous Galerkin finite element discretization of partial differential equations. In this work we propose two approaches to handling complex geometries within this setting: (1) using nonconforming, curvilinear GD elements and (2) coupling affine GD elements with curvilinear simplicial elements. In both cases the (semidiscrete) discontinuous Galerkin method is provably energy stable even when variational crimes are committed. Additionally, for both element types a weight-adjusted mass matrix is used, which ensures that only the reference mass matrix must be inverted. We also present sufficient conditions on the treatment of metric terms for the curvilinear, nonconforming GD elements to ensure that the scheme is both constant preserving and conservative. Numerical experiments confirm the stability results and demonstrate the accuracy of the coupled schemes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Spy the Lie: Detecting Malicious Insiders

Insider threat is a hard problem. There is no ground truth, there are innumerable variables, and the data is sparse. The types of crimes and abuses associated with insider threats are significant; the most serious include espionage, sabotage, terrorism, embezzlement, extortion, bribery, and corruption. Malicious activities include an even broader range of exploits, such as negligent use of classified data, fraud, cybercrime, unauthorized access to sensitive information, and illicit communications with unauthorized recipients. Inadvertent action or inaction without malicious intent (e.g., disposing of sensitive documents incorrectly) can also cause harm to an organization. This review article will explore insider threat, specifically behaviors, beliefs, and current debates within the field. Additionally particular focus is given to deception, a significant behavioral component of the malicious insider. Finally, research and policy implications for law enforcement and the intelligence community are addressed.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Multivariate regression modelling for gender prediction using volatile organic compounds from hand odor profiles via HS-SPME-GC-MS

The efficacy of using human volatile organic compounds (VOCs) as a form of forensic evidence has been well demonstrated with canines for crime scene response, suspect identification, and location checking. Although the use of human scent evidence in the field is well established, the laboratory evaluation of human VOC profiles has been limited. This study used Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) to analyze human hand odor samples collected from 60 individuals (30 Females and 30 Males). The human volatiles collected from the palm surfaces of each subject were interpreted for classification and prediction of gender. The volatile organic compound (VOC) signatures from subjects’ hand odor profiles were evaluated with supervised dimensional reduction techniques: Partial Least Squares-Discriminant Analysis (PLS-DA), Orthogonal-Projections to Latent Structures Discriminant Analysis (OPLS-DA), and Linear Discriminant Analysis (LDA). The PLS-DA 2D model demonstrated clustering amongst male and female subjects. The addition of a third component to the PLS-DA model revealed clustering and minimal separation of male and female subjects in the 3D PLS-DA model. The OPLS-DA model displayed discrimination and clustering amongst gender groups with leave one out cross validation (LOOCV) and 95% confidence regions surrounding clustered groups without overlap. The LDA had a 96.67% accuracy rate for female and male subjects. The culminating knowledge establishes a working model for the prediction of donor class characteristics using human scent hand odor profiles.

59 BASIC BIOLOGICAL SCIENCES↗