Transportation Secure Data Center: Real-World Data for Planning, Modeling, & Analysis (Fact Sheet) (Revised)
Fact sheet on Transportation Secure Data Center.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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Establishing a Secure Data Center with Remote Access: Preprint
Access to existing travel data is critical for many analysis efforts that lack the time or resources to support detailed data collection. High-resolution data sets provide particular value, but also present a challenge for preserving the anonymity of the original survey participants. To address this dilemma of providing data access while preserving privacy, the National Renewable EnergyLaboratory and the U.S. Department of Transportation have launched the Transportation Secure Data Center (TSDC). TSDC data sets include those from regional travel surveys and studies that increasingly use global positioning system devices. Data provided by different collecting agencies varies with respect to formatting, elements included and level of processing conducted in support of theoriginal purpose. The TSDC relies on a number of geospatial and other analysis tools to ensure data quality and to generate useful information outputs. TSDC users can access the processed data in two different ways. The first is by downloading summary results and second-by-second vehicle speed profiles (with latitude/longitude information removed) from a publicly-accessible website. The secondmethod involves applying for a remote connection account to a controlled-access environment where spatial analysis can be conducted, but raw data cannot be removed. The TSDC website is http://www.nrel.gov/vehiclesandfuels/secure_transportation_data.html.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Secure Data Transfer Guidance for Industrial Control and SCADA Systems
This document was developed to provide guidance for the implementation of secure data transfer in a complex computational infrastructure representative of the electric power and oil and natural gas enterprises and the control systems they implement. For the past 20 years the cyber security community has focused on preventative measures intended to keep systems secure by providing a hard outer shell that is difficult to penetrate. Over time, the hard exterior, soft interior focus changed to focus on defense-in-depth adding multiple layers of protection, introducing intrusion detection systems, more effective incident response and cleanup, and many other security measures. Despite much larger expenditures and more layers of defense, successful attacks have only increased in number and severity. Consequently, it is time to re-focus the conventional approach to cyber security. While it is still important to implement measures to keep intruders out, a new protection paradigm is warranted that is aimed at discovering attempted or real compromises as early as possible. Put simply, organizations should take as fact that they have been, are now, or will be compromised. These compromises may be intended to steal information for financial gain as in the theft of intellectual property or credentials that lead to the theft of financial resources, or to lie silent until instructed to cause physical or electronic damage and/or denial of services. This change in outlook has been recently confirmed by the National Security Agency [19]. The discovery of attempted and actual compromises requires an increased focus on monitoring events by manual and/or automated log monitoring, detecting unauthorized changes to a system's hardware and/or software, detecting intrusions, and/or discovering the exfiltration of sensitive information and/or attempts to send inappropriate commands to ICS/SCADA (Industrial Control System/Supervisory Control And Data Acquisition) systems.
03 NATURAL GAS↗
Immutable Secure Data Exchange and Storage for UAM Environments
Urban Air Mobility (UAM) has become a focus for the next generation of aerial passenger transportation. UAM operations will be leveraging a service-based architecture for airspace solutions. The UAM environment will leverage diverse communications and system access approaches. These approaches include independent Providers of Services for UAM and supplemental data service providers that exchange data between themselves and UAM operators. This research focuses on the secure data exchange and storage of this decentralized UAM environment to address these challenges. This research intends to leverage a permissioned blockchain approach to address cybersecurity threats that may impact a UAM environment.
Urban Air Mobility↗
Transportation Secure Data Center: Real-World Data for Planning, Modeling and Analysis (Fact Sheet)
The National Renewable Energy Laboratory (NREL) and the U.S. Department of Transportation (DOT) have launched the free, web-based Transportation Secure Data Center (TSDC). The TSDC (www.nrel.gov/tsdc) preserves respondent anonymity while making vital transportation data available to a broad group of users through secure, online access. The TSDC database gives, metropolitan planning organizations, universities, national laboratories, air quality management districts, disaster planning agencies and auto manufacturers free-of-charge web-based access to valuable transportation data. The TSDC's two levels of access make composite data available with simple online registration, and allow researchers to use detailed spatial data after completing a straight forward application process.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Immutable Secure Data Exchange and Storage for Urban Air Mobility Environments
Urban air mobility (UAM) is a concept that proposes to develop short-range aerial vehicles to overcome increasing surface congestion. Within the UAM environment, UAM operators work collaboratively to manage aerial vehicles in the urban environment. Providers of Services for UAM (PSU), UAM operators, and Supplemental Data Service Providers (SDSP) provide services to support flight operations within the UAM environment. The growth in the development of UAM systems, and the associated data exchange and service interactions will be at risk due to numerous types of cybersecurity attacks. To address these challenges, this research focuses on the secure data exchange and storage of this decentralized UAM environment. The intent of this research is to leverage a permissioned blockchain approach to address cybersecurity threats that may impact a UAM environment.
Urban Air Mobility↗
Immutable Secure Data Exchange and Storage for Urban Air Mobility Environments
Urban air mobility (UAM) is a concept that proposes to develop short-range aerial vehicles to overcome increasing surface congestion. Within the UAM environment, UAM operators work collaboratively to manage aerial vehicles in the urban environment. Providers of Services for UAM (PSU), UAM operators, and Supplemental Data Service Providers (SDSP) provide services to support flight operations within the UAM environment. The growth in the development of UAM systems, and the associated data exchange and service interactions will be at risk due to numerous types of cybersecurity attacks. To address these challenges, this research focuses on the secure data exchange and storage of this decentralized UAM environment. The intent of this research is to leverage a permissioned blockchain approach to address cybersecurity threats that may impact a UAM environment.
Urban Air Mobility↗
Detecting Periodic Subsequences in Cyber Security Data
Anomaly detection for cyber-security defence has garnered much attention in recent years providing an orthogonal approach to traditional signature-based detection systems. Anomaly detection relies on building probability models ofnormal computer network behaviour and detecting deviations from the model. Most data sets used for cyber-security have a mix of user-driven events and automated network events, which most often appears as polling behaviour. Separating these automated events from those caused by human activity is essential to building good statistical models for anomaly detection. In this article, we present a change point detection framework for identifying automated network events appearing as periodic subsequences of event times. The opening event of each subsequence is interpreted as a human action which then generates an automated, periodic process. Difficulties arising from the presence of duplicate and missing data are addressed. The methodology is demonstrated using authentication data from Los Alamos National Laboratory's enterprise computer network.
97 MATHEMATICS AND COMPUTING↗
Immutable Secure Data Exchange and Storage for Urban Air Mobility Environments
The Urban Air Mobility (UAM) environment is derived from the Unmanned Traffic Management (UTM) concept of operations. Within the environment, UAM operators work independently to manage aerial vehicles in the urban environment. Providers of Services (PSU), UAM operators, and Supplemental Data Service Providers provide services to support flight operations within the UAM environment. The intent of this work is to leverage a permissioned blockchain approach, to, simulate secure data exchange and storage for UAM environments. Blockchain technologies can be used for identity management of vehicles, people, and systems.
Blockchain↗
Autonomous Information Unit: Why Making Data Smart Can also Make Data Secured?
In this paper, we introduce a new fine-grain distributed information protection mechanism which can self-protect, self-discover, self-organize, and self-manage. In our approach, we decompose data into smaller pieces and provide individualized protection. We also provide a policy control mechanism to allow 'smart' access control and context based re-assembly of the decomposed data. By combining smart policy with individually protected data, we are able to provide better protection of sensitive information and achieve more flexible access during emergency conditions. As a result, this new fine-grain protection mechanism can enable us to achieve better solutions for problems such as distributed information protection and identity theft.
data security↗
Simulating Secure Data Exchange and Storage for Urban Air Mobility Environments
In this paper I give some background on UAM, the need for security in UAM, as well as blockchain. Then I discuss how blockchain can facilitate a secure exchange and storage of data in a UAM environment, focusing on a simulation we developed to that simulates a UAM environment. Specifically, the simulation focuses on the negotiation between UAM operators, PSUs, and the DSS when two operators want to claim the same airspace. We discuss how this process, as well as the subsequent vehicle telemetry data, is captured in the blockchain.
UAM↗
Quasi-Wireless Capacitive Power Transfer with Secure Data Acquisition for Robotic Systems in Space Infrastructure
Space exploration is dependent on robotic systems that utilize end-effectors to collect samples, probe surfaces, and manipulate objects. These systems can rarely be designed to do all three, forcing engineers to make tradeoffs based on the mission parameters - i.e. should the robotic appendage have a claw, drill, or shovel, and which would be best suited for the mission? Additionally, as more industrial and government entities partake in space exploration, data protection is needed in transit and at rest. To address these challenges, we present a first-of-its- kind robotic linkage that has no wiring between the joints. Instead, quasi-wireless capacitive (QWiC) power transfer is used to send energy over the robot’s chassis without a return wire. This enables the system to be completely modular through the use of single-contact permanent magnet connections, allowing rapid alterations in joint kinematics and/or the changing of end-effectors. For collecting sensor data from the robotic arm and to send remote commands to it, we use a Supervisory Control and Data Acquisition (SCADA) system. Data transmission relies on MQTT and OPC UA communication protocols with encryption. The SCADA server logs and archives sensor data and provides the functionality for authorized users to send remote commands from SCADA client(s) to motors. A SCADA client can be any of the web browsers that connects to a server via a secure communication channel using SSL protocol. Furthermore, as an extra data protection mechanism, we inject noise to the sensor data traffic, which obfuscates the timing of sensor data packets and adds confusion about which data packet represents which motor.
wireless sensor networks↗
A Blockchain-Based Mechanism for Secure Data Exchange in Smart Grid Protection Systems.
Abstract not provided.
Sikeridis, Dimitrios↗
Hybridizing Classifiers and Collection Systems to Maximize Intelligence and Minimize Uncertainty in National Security Data Analytics Applications
There are numerous applications that combine data collected from sensors with machine-learning based classification models to predict the type of event or objects observed. Both the collection of the data itself and the classification models can be tuned for optimal performance, but we hypothesize that additional gains can be realized by jointly assessing both factors together. Through this research, we used a seismic event dataset and two neural network classification models that issued probabilistic predictions on each event to determine whether it was an earthquake or a quarry blast. Real world applications will have constraints on data collection, perhaps in terms of a budget for the number of sensors or on where, when, or how data can be collected. We mimicked such constraints by creating subnetworks of sensors with both size and locational constraints. We compare different methods of determining the set of sensors in each subnetwork in terms of their predictive accuracy and the number of events that they observe overall. Additionally, we take the classifiers into account, treating them both as black-box models and testing out various ways of combining predictions among models and among the set of sensors that observe any given event. We find that comparable overall performance can be seen with less than half the number of sensors in the full network. Additionally, a voting scheme that uses the average confidence across the sensors for a given event shows improved predictive accuracy across nearly all subnetworks. Lastly, locational constraints matter, but sometimes in unintuitive ways, as better-performing sensors may be chosen instead of the ones excluded based on location. This being a short-term research effort, we offer a lengthy discussion on interesting next-steps and ties to other ongoing research efforts that we did not have time to pursue. These include a detailed analysis of the subnetwork performance broken down by event type, specific location, and model confidence. This project also included a Campus Executive research partnership with Texas A&M University. Through this, we worked with a professor and student to study information gain for UAV routing. This was an alternative way of looking at the similar problem space that includes sensor operation for data collection and the resulting benefit to be gained from it. This work is described in an Appendix.
97 MATHEMATICS AND COMPUTING↗
The National Security Data Solution (NSDS) [Slides]
Abstract not provided.
97 MATHEMATICS AND COMPUTING↗
Considerations for Secure Data Exchange to Achieve Cyber-Physical Situational Awareness in the Electric Grid
Explore the source record for details and available documents.
Chavez, Adrian R.↗
Blockchain in UAM: Secure Data Exchange and Storage
Explore the source record for details and available documents.
UAM↗