Results for “detection network” — Page 39

DOE OSTI2024

Summer 2024 INL Intern Poster Session Submission - Brian Schumitz

This LRS submission is my poster for the INL Intern Poster Session, Summer 2024. Abstract: The Software Engineering and Cybersecurity Lab (SECL) at Montana State University has developed PIQUE, a system for evaluating software quality. PIQUE's adaptability allows for language-specific static-analysis operations, including a model for assessing cloud microservice ecosystems. These ecosystems often rely on Docker for efficient deployment and management of containerized services. Our research focuses on evaluating the network quality within these microservice ecosystems. To automate this process, we're utilizing Snort, an open-source intrusion detection system renowned for its ability to detect and log network traffic. By leveraging Snort's customizable rules, we aim to construct comprehensive testing methods for measuring and quantifying the network quality based on traffic between Docker containers. This research aims to enhance the overall security and reliability of cloud microservice ecosystems by providing automated and robust quality evaluation mechanisms, ultimately contributing to the advancement of software engineering practices in these environments

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2016

MPLNET V3 Cloud and Planetary Boundary Layer Detection

The NASA Micropulse Lidar Network Version 3 algorithms for planetary boundary layer and cloud detection are described and differences relative to the previous Version 2 algorithms are highlighted. A year of data from the Goddard Space Flight Center site in Greenbelt, MD consisting of diurnal and seasonal trends is used to demonstrate the results. Both the planetary boundary layer and cloud algorithms show significant improvement of the previous version.

cloud detection↗

DOE OSTI2016

Two-dimensional plasmons in lateral carbon nanotube network structures and their effect on the terahertz radiation detection

We consider the carrier transport and plasmonic phenomena in the lateral carbon nanotube (CNT) networks forming the device channel with asymmetric electrodes. One electrode is the Ohmic contact to the CNT network and the other contact is the Schottky contact. These structures can serve as detectors of the terahertz (THz) radiation. We develop the device model for collective response of the lateral CNT networks which comprise a mixture of randomly oriented semiconductor CNTs (s-CNTs) and quasi-metal CNTs (m-CNTs). The proposed model includes the concept of the collective two-dimensional (2D) plasmons in relatively dense networks of randomly oriented CNTs (CNT “felt”) and predicts the detector responsivity spectral characteristics exhibiting sharp resonant peaks at the signal frequencies corresponding to the 2D plasmonic resonances. The detection mechanism is the rectification of the ac current due the nonlinearity of the Schottky contact current-voltage characteristics under the conditions of a strong enhancement of the potential drop at this contact associated with the plasmon excitation. The detector responsivity depends on the fractions of the s- and m-CNTs. The burning of the near-contact regions of the m-CNTs or destruction of these CNTs leads to a marked increase in the responsivity in agreement with our experimental data. The resonant THz detectors with sufficiently dense lateral CNT networks can compete and surpass other THz detectors using plasmonic effects at room temperatures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2018

Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection

Deep learning has recently demonstrated state-of-the art performance on key tasks related to the maintenance of computer systems, such as intrusion detection, denial of service attack detection, hardware and software system failures, and malware detection. In these contexts, model interpretability is vital for administrator and analyst to trust and act on the automated analysis of machine learning models. Deep learning methods have been criticized as black box oracles which allow limited insight into decision factors. In this work we seek to "bridge the gap'' between the impressive performance of deep learning models and the need for interpretable model introspection. To this end we present recurrent neural network (RNN) language models augmented with attention for anomaly detection in system logs. Our methods are generally applicable to any computer system and logging source. By incorporating attention variants into our RNN language models we create opportunities for model introspection and analysis without sacrificing state-of-the art performance. We demonstrate model performance and illustrate model interpretability on an intrusion detection task using the Los Alamos National Laboratory (LANL) cyber security dataset, reporting upward of 0.99 area under the receiver operator characteristic curve despite being trained only on a single day's worth of data.

Anomaly detection, Attention, Recurrent Neural Net↗

DOE OSTI2015

NetMOD Version 2.0 Mathematical Framework

NetMOD ( Net work M onitoring for O ptimal D etection) is a Java-based software package for conducting simulation of seismic, hydroacoustic and infrasonic networks. Network simulations have long been used to study network resilience to station outages and to determine where additional stations are needed to reduce monitoring thresholds. NetMOD makes use of geophysical models to determine the source characteristics, signal attenuation along the path between the source and station, and the performance and noise properties of the station. These geophysical models are combined to simulate the relative amplitudes of signal and noise that are observed at each of the stations. From these signal-to-noise ratios (SNR), the probabilities of signal detection at each station and event detection across the network of stations can be computed given a detection threshold. The purpose of this document is to clearly and comprehensively present the mathematical framework used by NetMOD, the software package developed by Sandia National Laboratories to assess the monitoring capability of ground-based sensor networks. Many of the NetMOD equations used for simulations are inherited from the NetSim network capability assessment package developed in the late 1980s by SAIC (Sereno et al., 1990).

58 GEOSCIENCES↗

NASA NTRS1996

NDSC Lidar Intercomparisons and Validation: OPAL and MLO3 Campaigns in 1995

The Network for the Detection of Stratospheric Change (NDSC) has developed and adopted a Validation Policy in order to ensure that the results submitted and stored in its archives are of a known, high quality. As a part of this validation policy, blind instrument intercomparisons are considered an essential element in the certification of NDSC instruments and a specific format for these campaigns has been recommended by the NDSC-Steering Committee.

Stratospheric Change The Network for the Detection↗

DOE OSTI2018

Deep Learning for Unsupervised Insider Threat Detection in Structured Cyber Security Data Streams

Analysis of an organization's computer network activity is a key component of early detection and mitigation of insider threat, a growing concern for many organizations. Raw system logs are a prototypical example of streaming data that can quickly scale beyond the cognitive power of a human analyst. As a prospective filter for the human analyst, we present an online unsupervised deep learning approach to detect anomalous network activity from system logs in real time. Our models decompose anomaly scores into the contributions of individual user behavior features for increased interpretability to aid analysts reviewing potential cases of insider threat. Using the CERT Insider Threat Dataset v6.2 and threat detection recall as our performance metric, our novel deep and recurrent neural network models outperform Principal Component Analysis, Support Vector Machine and Isolation Forest based anomaly detection baselines. For our best model, the events labeled as insider threat activity in our dataset had an average anomaly score in the 95.53 percentile, demonstrating our approach's potential to greatly reduce analyst workloads.

Deep Learning, Insider Threat↗

DOE OSTI2021

Combined Mixed Potential Electrochemical Sensors and Artificial Neural Networks for the Quantificationand Identification of Methane in Natural Gas Emissions Monitoring

Sensors capable of quantifying methane concentration and discriminating between possible sources are needed for natural gas leak detection where multiple spatially overlapping sources including wetlands and agriculture may be present. We report on the fabrication by an additive manufacturing process of a four electrode La 0.87 Sr 0.13 CrO 3 , Indium Tin Oxide (In 2 O 3 90 wt%, SnO 2 10 wt%), Au, Pt mixed potential electrochemical sensor using yttria-stabilized zirconia (YSZ) as a solid electrolyte to natural gas detection. Artificial neural networks (ANNs) are used to automatically decode the possible source and concentration of methane. The ANNs trained on sensor data are capable of correctly discriminating between three sources of methane emissions from simulated mixtures of emissions from cattle, wetlands, or natural gas with >98% accuracy. Quantification error for methane in mixtures of CH 4 in air, CH 4 + NH3 in air, and simulated natural gas is less than 1.5% ppm when a two-temperature dataset is employed.

03 NATURAL GAS↗

NASA NTRS1991

STROZ LITE - Stratospheric Ozone Lidar Trailer Experiment

A mobile dual-wavelength differential absorption lidar capable of making precise measurements of stratospheric ozone between 20 and 45 km has been developed at the Goddard Space Flight Center as part of the international Network for the Detection of Stratospheric Change. The system is installed in a 46-ft trailer, which enables the instrument to act as a network transfer standard and to be set up at any location where power can be obtained. A description of the instrument is presented, along with a discussion of the data analysis. Some results from an intercomparison held at JPL's Table Mountain Observatory in California during October and November 1988 are also presented.

Mcgee, Thomas J.↗

NASA NTRS1994

NDSC and the JPL Stratospheric Lidars

The Network for the Detection of Stratospheric Change (NDSC) is an international cooperation providing a set of high-quality, remote-sensing instruments at observing stations around the globe. A brief description of the NDSC and its goals is presented. Lidar has been selected as the NDSC instrument for measurements of stratospheric profiles of ozone, temperature, and aerosol. The Jet Propulsion Laboratory has developed and implemented two stratospheric lidar systems for NDSC. These are located at Table Mountain, California and at Mauna Loa, Hawaii. These systems, which utilize different absorption lidar, Rayleigh lidar, Raman lidar, and backscatter lidar, to measure ozone, temperature, and aerosol profiles in the stratosphere are briefly described. Examples of results obtained for both long-term and individual profiles are presented.

lidar↗

DOE OSTI2023

Functional characterization and regulatory modeling of lignocellulose deconstruction in the saprophytic bacterium Cellvibrio japonicus (Final Technical Report)

The goal of this project was to establish a fundamental systems-level model of lignocellulose deconstruction by saprophytic soil bacteria while simultaneously identifying and characterizing novel enzymes that can be used for applied bioenergy technologies. This project was significant because it elevated our mechanistic and regulatory understanding of bacterial plant biomass degradation and led to technologies to enable bottlenecks in biofuel production R&D. Furthermore, the completed work was novel because it integrated in vivo, in vitro, and in silico approaches with bioenergy-relevant substrates. We identified the required enzymes for lignocellulose degradation and mechanisms of regulation through a combination of transcriptomic profiling (RNAseq), enzymatic analysis, the functional characterization of mutants, and modeling of the regulatory networks used to detect and degrade lignocellulose. This project employed the lignocellulose-degrading bacterium Cellvibrio japonicus to dissect the functional properties and underlying regulatory networks saprophytic bacteria use to depolymerize and utilize lignocellulose. There were four Objectives to this project, the specific metrics of which are summarized in this report.

09 BIOMASS FUELS↗

DOE OSTI2017

Imaging Fracture Networks Using Joint Seismic and Electrical Change Detection Techniques (Final Report)

During the initial phase of this SubTER project, we conducted a series of high resolution seismic imaging campaigns designed to characterize induced fractures. Fractures were emplaced using a novel explosive source, designed at Sandia National Laboratories, that limits damage to the borehole. This work provided evidence that fracture locations could be imaged at inch scales using high-frequency seismic tomography but left many fracture properties (i.e. permeability) unresolved. We present here the results of the second phase of the project, where we developed and demonstrated emerging seismic and electrical geophysical imaging technologies that characterize 1) the 3D extent and distribution of fractures stimulated from the explosive source, 2) 3D fluid transport within the stimulated fracture network through use of a contrasting tracer, and 3) fracture attributes through advanced data analysis. Focus was placed upon advancing these technologies toward near real-time acquisition and processing in order to help provide the feedback mechanism necessary to understand and control fracture stimulation and fluid flow. Results from this study include a comprehensive set of 4D cross-hole seismic and electrical data that take advantage of change detection methodologies allowing for perturbations associated with the fracture emplacement and particulate tracer to be isolated. During the testing the team also demonstrated near real-time 4D electrical resistivity tomography imaging and 4D seismic tomography using the CASSM approach with a temporal resolution approaching 1 minute. All of the data collected were used to develop methods of estimating fracture attributes from seismic data, develop methods of assimilating disparate and transient data sets to improve fracture network imaging resolution, and advance capabilities for near real-time inversion of cross-hole tomographic data. These results are illustrated here. Advancements in these areas are relevant to all situations where fracture emplacement is used for reservoir stimulation (e.g. Enhanced Geothermal Systems (EGS) and tight shale gases).

42 ENGINEERING↗

NASA NTRS2023

Flexible User Radio for Lunar Missions

NASA’s Artemis program and other lunar exploration and development programs are planning over 40 lunar missions before 2030. Lunar missions, both crewed and uncrewed, include orbiters, landers, rovers, and surface stations. All these missions require communications with Earth, either via Direct to Earth (DTE) links or through relays in lunar orbit. Multiple DTE options are available among existing and planned ground stations: Deep Space Network (DSN), European Space Agency, and others. Relay options include the planned Lunar Gateway, LunaNet compliant relays, and some lunar landers propose to launch dedicated orbiters. The dilemma for lunar system designers is to identify a communication link which meets mission requirements but does not have issues of limited access (e.g. DSN is in high demand supporting deep space missions with high priority and some with inflexible schedules), system impacts (high power Radio Frequency (RF) for DTE links), cost (dedicated relay), or operational date. To avoid this difficult decision, a Flexible Radio for Lunar Missions is proposed, which will enable system designs to proceed prior to any final decision on the communication network to be used, by enabling compatibility with any of multiple DTE or orbital relay communication systems. The Flexible Radio will support the necessary frequency, bandwidth, modulation, and power requirements to interoperate with the majority of known or planned DTE or relay systems, and can be designed into a lunar mission without prior knowledge of which link will ultimately be used. Furthermore, the link being used can be changed as needed during the mission, in near real time. The Flexible Radio design will leverage work already completed at NASA in the areas of Wideband RF, Software Defined Radio, Adaptive Coding and Modulation, and Phased Array antennas. The Flexible Radio requires sufficient bandwidth to cover the allocated frequencies for both the operation of links in cislunar space and for space-to-Earth links; the flexibility to support multiple modulations, data rates, and coding schemes; the ability to identify available relays, detect and recognize the signals of those relays, and adapt its own frequency, modulation, symbol rate, and code rate to operate with the detected relay (or DTE station); and finally, it requires appropriate software to support network configuration and interoperability with the detected network. The Flexible Radio can be designed in a sufficiently small, lightweight, and low-power package to be used in a wide variety of lunar systems. The initial implementation, as proposed, will focus on the Ka-band, supporting up to 2 GHz bandwidth around the 27 GHz frequency for return links, and 23 GHz for forward links. Other frequency bands are under consideration for future configurations. The software defined modem will support OQPSK, BPSK, and NASA-defined modulations which also support two-way ranging with data. For near-real time adaptation, the Flexible Radio will scan the sky for available relays, using a phased array antenna with Adaptive/Cognitive Communications. It will then configure for network interoperability supporting DTN and other protocol options. The Flexible Radio will support scheduled connections and on-demand use when available.

Lunar Space Communications↗

NASA NTRSDate not supplied

Flexible User Radio for Lunar Missions

NASA’s Artemis program and other lunar exploration and development programs are planning over 40 lunar missions before 2030. Lunar missions, both crewed and uncrewed, include orbiters, landers, rovers, and surface stations. All these missions require communications with Earth, either via Direct to Earth (DTE) links or through relays in lunar orbit. Multiple DTE options are available among existing and planned ground stations: Deep Space Network (DSN), European Space Agency, and others. Relay options include the planned Lunar Gateway, LunaNet compliant relays, and some lunar landers propose to launch dedicated orbiters. The dilemma for lunar system designers is to identify a communication link which meets mission requirements but does not have issues of limited access (e.g. DSN is in high demand supporting deep space missions with high priority and some with inflexible schedules), system impacts (high power Radio Frequency (RF) for DTE links), cost (dedicated relay), or operational date. To avoid this difficult decision, a Flexible Radio for Lunar Missions is proposed, which will enable system designs to proceed prior to any final decision on the communication network to be used, by enabling compatibility with any of multiple DTE or orbital relay communication systems. The Flexible Radio will support the necessary frequency, bandwidth, modulation, and power requirements to interoperate with the majority of known or planned DTE or relay systems, and can be designed into a lunar mission without prior knowledge of which link will ultimately be used. Furthermore, the link being used can be changed as needed during the mission, in near real time. The Flexible Radio design will leverage work already completed at NASA in the areas of Wideband RF, Software Defined Radio, Adaptive Coding and Modulation, and Phased Array antennas. The Flexible Radio requires sufficient bandwidth to cover the allocated frequencies for both the operation of links in cislunar space and for space-to-Earth links; the flexibility to support multiple modulations, data rates, and coding schemes; the ability to identify available relays, detect and recognize the signals of those relays, and adapt its own frequency, modulation, symbol rate, and code rate to operate with the detected relay (or DTE station); and finally, it requires appropriate software to support network configuration and interoperability with the detected network. The Flexible Radio can be designed in a sufficiently small, lightweight, and low-power package to be used in a wide variety of lunar systems. The initial implementation, as proposed, will focus on the Ka-band, supporting up to 2 GHz bandwidth around the 27 GHz frequency for return links, and 23 GHz for forward links. Other frequency bands are under consideration for future configurations. The software defined modem will support OQPSK, BPSK, and NASA-defined modulations which also support two-way ranging with data. For near-real time adaptation, the Flexible Radio will scan the sky for available relays, using a phased array antenna with Adaptive/Cognitive Communications. It will then configure for network interoperability supporting DTN and other protocol options. The Flexible Radio will support scheduled connections and on-demand use when available.

Lunar Space Communications↗

NASA NTRS2019

Small target detection for search and rescue operations using distributed deep learning and synthetic data generation

It is important to find the target as soon as possible for search and rescue operations. Surveillance camera systems and unmanned aerial vehicles (UAVs) are used to support search and rescue. Automatic object detection is important because a person cannot monitor multiple surveillance screens simultaneously for 24 hours. Also, the object is often too small to be recognized by the human eye on the surveillance screen. This study used UAVs around the Port of Houston and fixed surveillance cameras to build an automatic target detection system that supports the US Coast Guard (USCG) to help find targets (e.g., person overboard). We combined image segmentation, enhancement, and convolution neural networks to reduce detection time to detect small targets. We compared the performance between the auto-detection system and the human eye. Our system detected the target within 8 seconds, but the human eye detected the target within 25 seconds. Our systems also used synthetic data generation and data augmentation techniques to improve target detection accuracy. This solution may help the search and rescue operations of the first responders in a timely manner.

Chow, Edward↗

DOE OSTI2025

MSU IETC ML for Modbus (AN EDGE)

This study explores machine learning for decoding Modbus RTU data using K-Nearest Neighbors (KNN) models. An initial KNN model trained on 8,000 packets achieved 95.15% accuracy. Although ML improves generalization, accuracy still falls short of deterministic methods. These findings have implications for Modbus traffic analysis, intrusion detection in industrial networks, and adaptive error correction in real-time monitoring systems. By refining ML-based decoding, future work could enable more efficient anomaly detection and predictive maintenance in industrial automation and cybersecurity applications.

Communication Protocol↗

NASA NTRS1990

Neural network approach to Space Shuttle Main Engine health monitoring

A neural network was trained to distinguish anomalies in Space Shuttle Main Engine sensor data from noisy normal steady-state sensor data. Power spectra of successive windows of individual sensor data were presented to a neural network using Kohonen's topological feature map training algorithm. The trained network for each sensor was then tested to determine if it would detect anomalies in the sensor data, and if so, the time at which the anomaly would be detected. Power spectra from a few hundred seconds of actual test data from NASA tests 901-364 and 904-044 were used to test the network. In both cases, the neural network detected the onset of anomalous engine behavior at approximately the same time within each test as the onset times reported by NASA and Rocketdyne experts in their post-test analyses.

Whitehead, Bruce A.↗

NASA NTRS1992

The JPL Table Mountain and Mauna Loa Stratospheric Ozone Lidars

The development of a differential absorption lidar (DIAL) system for long-term measurements of stratospheric ozone and for potential inclusion in the Network for the Detection of Stratospheric Change (NDSC) began at JPL in 1986, concurrent with the first workshop that considered the priorities and appropriate measurement techniques for such a network. The DIAL system at the JPL Table Mountain Facility (TMF) was developed specifically to have characteristics suitable for longterm measurements as proposed for NDSC. Regular measurements of stratospheric ozone concentration profiles commenced in February 1988 and this system has been fully described elsewhere.

Mcdermid, I. Stuart↗