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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

A Semantic Search Capability for a Grid Model Repository

The operation of electric grids is becoming more challenging due to continued increases in wind and solar generation, the proliferation of decentralization generation resources, and cyber security threats. To support the needed research, new large scale realistic public grid models have been developed and placed in a repository for access by researchers. To assist researchers in easily finding grid models that are suitable for their specific research needs, an approach to searching numerous large grid models was evaluated, prototyped and developed. Here, the research identified multiple possible approaches to using graph database techniques and a natural language processing for the search algorithm. Results of the prototype, the final approach for production repository and the search algorithm formulation are described.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

A Machine Learning Framework to Deconstruct the Primary Drivers for Electricity Market Price Events

As the electricity grid is moving towards a 100% Renewable Energy Source Bulk Power Grid, the overall operations of the power system operations and electricity markets are changing. The electricity markets are not only dispatching resources economically but also taking into account various controllable actions like renewable curtailment, transmission congestion mitigation, and energy storage optimization to make sure the grid is operating reliably. As a result, price formations in electricity markets have become quite complex. Traditional root cause analysis and statistical approaches are rendered inapplicable to analyze and infer the main drivers behind price formation in the modern grid and markets with variable renewable energy (VRE). In this paper, we propose a machine learning analysis framework to deconstruct some primary drivers for price formation in modern electricity markets with high renewable energy and the outcomes can be utilized for various critical aspects of market design, renewable dispatch and curtailment, operations, and cyber-security applications. The framework can be applied to any ISO or market data and in this paper it is applied to open-source publicly available datasets from California Independent System Operator (CAISO) and ISO New England.

machine learning (ML), electricity markets, Renewa↗
DOE OSTI2017

Big Data Sanitization and Cyber Situational Awareness: A Network Telescope Perspective

This paper addresses the problems of data sanitization and cyber situational awareness by analyzing 910 GB of real Internet-scale traffic, which has been passively collected by monitoring close to 16.5 million darknet IP addresses from a /8 and a /13 network telescopes. First, the paper offers a novel probabilistic darknet preprocessing model, which aims at sanitizing darknet data to prepare it for effective use in the task of cyber threat intelligence generation. Such model has been engineered using a distributed multithreaded approach, rendering it highly effective on darknet big data. Second, the paper further contributes by presenting an innovative approach to infer large-scale orchestrated probing campaigns by leveraging darknet data, for Internet cyber situational awareness. The approach uniquely reduces the dimensionality of such big data by utilizing its artifacts, instead of processing the actual raw data. This is accomplished by extracting and analyzing probing time series using formal methods rooted in Fourier transform and Kalman filtering. Thorough empirical evaluations indeed validate the accuracy and the performance of the proposed methods. We assert that such approaches are of significant value, given their highly applicable nature to the field of Internet measurements for cyber security in the era of big data.

Bou-Harb, Elias↗
DOE OSTI2022

Hardware-Based Randomized Encoding for Sensor Authentication in Power Grid SCADA Systems

Supervisory Control and Data Acquisition (SCADA) systems are utilized extensively in critical power grid infrastructures. Modern SCADA systems have been proven to be susceptible to cyber-security attacks and require improved security primitives in order to prevent unwanted influence from an adversarial party. One section of weakness in the SCADA system is the integrity of field level sensors providing essential data for control decisions at a master station. In this paper we propose a lightweight hardware scheme providing inferred authentication for SCADA sensors by combining an analog to digital converter and a permutation generator as a single integrated circuit. Through this method we encode critical sensor data at the time of sensing, so that unencoded data is never stored in memory, increasing the difficulty of software attacks. We show through experimentation how our design stops both software and hardware false data injection attacks occurring at the field level of SCADA systems.

42 ENGINEERING↗
DOE OSTI2017

Human Factors in Streaming Data Analysis: Challenges and Opportunities for Information Visualization: Human Factors in Streaming Data Analysis

State-of-the-art visual analytics models and frameworks mostly assume a static snapshot of the data, while in many cases it is a stream with constant updates and changes. Exploration of streaming data poses unique challenges as machine-level computations and abstractions need to be synchronized with the visual representation of the data and the temporally evolving human insights. In the visual analytics literature, we lack a thorough characterization of streaming data and analysis of the challenges associated with task abstraction, visualization design, and adaptation of the role of human-in-the-loop for exploration of data streams. We aim to fill this gap by conducting a survey of the state-of-the-art in visual analytics of streaming data for systematically describing the contributions and shortcomings of current techniques and analyzing the research gaps that need to be addressed in the future. Our contributions are: i) problem characterization for identifying challenges that are unique to streaming data analysis tasks, ii) a survey and analysis of the state-of-the-art in streaming data visualization research with a focus on the visualization design space for dynamic data and the role of the human-in-the-loop, and iii) reflections on the design-trade-offs for streaming visual analytics techniques and their practical applicability in real-world application scenarios.

streaming data↗
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 OSTI2019

Situational Awareness of Network System Roles (SANSR)

In a large enterprise it is difficult for cyber security analysts to know what services and roles every machine on the network is performing (e.g. file server, domain name server, email server). Understanding the roles of the systems in the network provides analysts with a situational awareness that will allow them to detect consequential changes in the network, initiate an incident response plan, and optimize their security posture. Using the network flow data, already collected by most enterprises, we developed a tool that enables analysts to automatically detect/classify services and roles of every machine that’s operating on a network (e.g. file server, domain name server, email server) for better situational awareness of potential threats to the network. his tool queries Elasticsearch for network flow data, creates a temporal behavior model of each system, uses unsupervised machine learning to cluster the models with a set of labeled temporal behavior models, and the resulting information can be printed to the console or programmatically accessed. The results include the likelihood that a machine has a labeled role and lists other machines that are most similar in behavior.

Huffer, KellyM.↗
DOE OSTI2015

Autonomic Intelligent Cyber Sensor (AICS) Version 1.0.1

The Autonomic Intelligent Cyber Sensor (AICS) provides cyber security and industrial network state awareness for Ethernet based control network implementations. The AICS utilizes collaborative mechanisms based on Autonomic Research and a Service Oriented Architecture (SOA) to: 1) identify anomalous network traffic; 2) discover network entity information; 3) deploy deceptive virtual hosts; and 4) implement self-configuring modules. AICS achieves these goals by dynamically reacting to the industrial human-digital ecosystem in which it resides. Information is transported internally and externally on a standards based, flexible two-level communication structure.

Vollmer, DenisTodd↗
DOE OSTI2020

Cybersecurity Framework Tool

The Cybersecurity Framework Tool is a web-based tool that provides five (5) concurrent and continuous functions to identify, detect, protect, respond and recover from cyber security threats to critical infrastructure.

Mylrea, Michael↗
DOE OSTI2022

Cybersecurity-Tools/V2W-BERT

Software Vulnerabilities to Weakness Mapping. V2W-BERT optimizes the learnable parameters θ of Fθ in two steps. In the first step, the pre-trained BERT language model is further fine-tuned with CVE/CWE descriptions specific to cyber security. In the second step, the trained BERT model is employed in a Siamese network architecture to establish links between CVEs and CWEs. The architecture takes a specific CVE-CWE pair as input and predicts whether the CVE belongs to the CWE or not, with a confidence value. V2W-BERT includes a Mask Language Model (LM) based Reconstruction Decoder to ensure that the descriptions’ contexts are not changed too much during the training process.

Halappanavar,, Mahantesh↗
DOE OSTI2017

pnnl/safekit

Some of the most pressing machine learning applications such as cyber security and object recognition lack enough ground-truth training data to build a classifier. We solve this problem by using unsupervised deep learning techniques to determine when data were anomalous or deviated from the norm. Traditional methods require either specific context dependent expertise to construct models, or prior examples of events and large amounts of well-labeled data to perform machine learning. Some cutting-edge deep learning approaches have been used to characterize complex events, but these have not been applied to the streaming environment and require a great deal of data. We demonstrated the use of an autoencoder and relevant featurization techniques to both learn a feature space and then identify anomalous portions without the aid of labeled data or domain knowledge. In this way, we demonstrated the detection of anomalous features without domain knowledge or tagged examples, and delivered these features to users.

Central, PNNL Developer↗
DOE OSTI2025

Cyber Informed Engineering Cie Analysis Tool

Main Benefits: • Collaborate on assessment via the web and access and share assessments on your mobile device. • Helps you maximize your cybersecurity investment and resources • Saves you significant time and money by eliminating the requirement to research each government and industry standard in order to understand your cybersecurity posture • Contains easy to follow, step by step instructions to guide you through the process of identifying the cybersecurity posture of your organization • Provides a place to begin with cybersecurity improvement and a way to prioritize your tasks and budgets. • Covers all major cyber relevant topic areas for a comprehensive assessment of your organization’s cybersecurity posture. • Dives deep into the details of each topic area. • Contributes to the organization's risk management and decision-making process • Highlights vulnerabilities and gaps in your organization's IT and control systems. • Raises awareness and facilitates discussion on cybersecurity within your organization • Educates the controls system community on cyber security.

Hansen, Barry [Idaho National Laboratory (INL), Id↗
DOE OSTI2026

ROSE

Developed at Lawrence Livermore National Laboratory (LLNL), ROSE is an open source compiler infrastructure to build source-to-source program transformation and analysis tools for large-scale C (C89 to C23), C++ (C++98 to C++23), UPC, Fortran (Fortran4, 66, 77, 95, 2003), OpenMP, Java, Python, and Binary applications. ROSE users range from experienced compiler researchers to library and tool developers who may have minimal compiler experience. ROSE is particularly well suited for building custom tools for static analysis, program optimization, arbitrary program transformation, domain-specific optimizations, complex loop optimizations, performance analysis, and cyber-security. ROSE is: A library (and set of associated tools) to quickly and easily apply compiler techniques to one's code in order to improve application performance and developer productivity. A research and development compiler infrastructure for for writing custom source-to-source translators to perform source code transformations, analysis, and optimizations. Is

Pinnow, NathanT [Lawrence Livermore National Labor↗
DOE OSTI2013

Siemens-INL SCADA System Assessments: Assessment 1: Spectrum Power TG & Assessment 2: Spectrum Power 3 (Final CRADA Report)

INL, in collaboration with Siemens Energy, Inc., performed a laboratory assessment on Siemens SCADA systems. During the assessment, vulnerabilities were discovered, evaluated, and mitigation strategies recommended. These results were reported to Siemens Energy, Inc. to guide their efforts towards improved cyber security in their software products.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Fujitsu and BEA Collaboration on Cyber Enabled Smart Grid Performance (CRADA Final report)

In conclusion of this CRADA, this letter documents the actions taken in regard to this collaboration. While the statement of work overviews several joint tasks associated with this effort, the availability of resources on the FLA side prevented any collaboratory development of shared Intellectual Property. Therefore, no patents, copyrights or publications resulted from this effort. However, several meetings at the INL and FLA offices maintained a good relationship and sharing of other potential areas of collaboration in the area of building/community energy management and cyber security.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

National Electric Sector Cybersecurity Organization Resource (NESCOR)

The goal of the National Electric Sector Cybersecurity Organization Resource (NESCOR) project was to address cyber security issues for the electric sector, particularly in the near and mid-term. The following table identifies the strategies from the DOE Roadmap to Achieve Energy Delivery Systems Cybersecurity published in September 2011 that are applicable to the NESCOR project.

Source record↗
DOE OSTI2014

Where Big Data and Prediction Meet

Our ability to assemble and analyze massive data sets, often referred to under the title of “big data”, is an increasingly important tool for shaping national policy. This in turn has introduced issues from privacy concerns to cyber security. But as IBM’s John Kelly emphasized in the last Innovation, making sense of the vast arrays of data will require radically new computing tools. In the past, technologies and tools for analysis of big data were viewed as quite different from the traditional realm of high performance computing (HPC) with its huge models of phenomena such as global climate or supporting the nuclear test moratorium. Looking ahead, this will change with very positive benefits for both worlds. Societal issues such as global security, economic planning and genetic analysis demand increased understanding that goes beyond existing data analysis and reduction. The modeling world often produces simulations that are complex compositions of mathematical models and experimental data. This has resulted in outstanding successes such as the annual assessment of the state of the US nuclear weapons stockpile without underground nuclear testing. Ironically, while there were historically many test conducted, this body of data provides only modest insight into the underlying physics of the system. A great deal of emphasis was thus placed on the level of confidence we can develop for the predictions. As data analytics and simulation come together, there is a growing need to assess the confidence levels in both data being gathered and the complex models used to make predictions. An example of this is assuring the security or optimizing the performance of critical infrastructure systems such as the power grid. If one wants to understand the vulnerabilities of the system or impacts of predicted threats, full scales tests of the grid against threat scenarios are unlikely. Preventive measures would need to be predicated on well-defined margins of confidence in order to take mitigating actions that could have wide ranging impacts. There is a rich opportunity for interaction and exchange between the HPC simulation and data analytics communities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Cheyney University Curriculum and Infrastructure Enhamcement in STEM

Cheyney University is the oldest historically Black educational institution in America. Initially established as a “normal” school emphasizing the matriculation of educators, Cheyney has become a comprehensive university, one of 14 state universities comprising the Pennsylvania State System of Higher Education (PASSHE). Cheyney University graduates still become teachers, but they also enter such fields as journalism, medicine, science, mathematics, law, communication and government. Cheyney University is a small state owned HBCU with very limited resource. At present the university has about a thousand students with 15% in STEM. The CUCIES II grant made significant contribution in saving the computer science program from being a discontinued program in the university. The grant enabled the university to hire a temporary faculty to teach in and update the computer science program. The program is enhanced with three tracks; cyber security, human computer interaction and general. The updated and enhanced computer science program will prepare professionals in the area of computer science with the knowledge, skills, and professional ethic needed for the current market. The new curriculum was developed for a professional profile that would focus on the technologies and techniques currently used in the industry. With faculty on board, the university worked with the department to bring back the computer science program from moratorium. Once in the path of being discontinued and loosing students, the program is now growing. Currently the student number has increased from 12 to 30. University is currently in the process of hiring a tenure track faculty in the computer science program. Another product of the grant is the proposal for introductory course in nanotechnology. The course is intended to generate interest in the nanotechnology field. The Natural and Applied Science department that houses all of the STEM programs in Cheyney University, is currently working to bring back environmental science program from moratorium. The university has been working to improve minority participation in STEM and made significant stride in terms of progressing students toward graduate programs and into professoriate track. This success is due to faculty mentors who work closely with students to guiding them through the application processes for research internship and graduate programs; it is also due to the university forming collaborative agreements with research intensive institutions, federal and state agencies and industry. The grant assisted in recruiting and retaining students in STEM by offering tuition scholarship, research scholarship and travel awards. Faculty professional development was supported by the grant by funding travel to conferences, meetings and webinar. As many HBCU Cheyney University is also trying to do more with less. As the STEM programs are inherently expensive, these are the ones that suffer more when resources are scarce. One of the goals of Cheyney University strategic plan is to strengthen STEM programs that is coherent with the critical skill need of Department of Energy. All of the Cheyney University STEM programs are now located in the new science building funded by Pennsylvania state.

99 GENERAL AND MISCELLANEOUS↗