The Department of Homeland Security (DHS) Science and Technology Directorate (S&T) developed Rapid DNA technology under a prior Small Business Innovative Research (SBIR) program to provide family relationship verifications in the field, a capability that no other biometric provides. Rapid DNA is an innovative technology that reduces the testing and analysis time for Deoxyribonucleic Acid (DNA) from the classical three to six months down to 90 minutes using a printer-size portable device. Rapid DNA also internally analyses the DNA profiles and with OBIM Store/Match/Share software can verify family relationship claims of biological relatedness (kinship). This has direct application to improving processes and reducing fraud in immigration, human trafficking/ smuggling at the borders, and for reunification of families following a mass casualty event. This SBIR topic builds on the established Rapid DNA capability, adding the necessary capability to provide for reach-back review of Rapid DNA results in an accredited environment. DHS S&T has had a significant role in developing, overseeing, testing and evaluating the Rapid DNA technology and it is now commercially available and ready to be implemented. Better than 90% of the time Rapid DNA produces a DNA profile cable of supporting a match and the instrument returns a green checkmark. But the remaining 8% of the time, the profiles receive either a yellow or red flag and need to be reviewed. Some of these yellow or red flags are due to issues with the DNA profile that will not impact the kinship analysis and some are due to processing issues by the technology. Either way, DHS needs an ability to reach-back to a DNA analyst to review the DNA profiles and to re-run a DNA sample when necessary. The DNA analyst and the facility also need to be accredited so that the fielded Rapid DNA results and those of the reach-back capability are shown to be repeatable and accurate to stand up in court, if challenged. The DHS Customs and Border Protection (CBP) Laboratories and Scientific Services Directorate (LSSD) has multiple regional laboratories and satellite offices for the processing of multiple forensic sample types, but does not currently have a human DNA laboratory. We are seeking any innovative/alternative solutions that would provide a reach-back capability for fielded Rapid DNA systems, anticipating that the developed solution would ultimately transition into the LSSD laboratory for long-term operational support to DHS field components. The research into potential reach-back solutions would need to address the analysis of innovative or potential solutions to provide reach-back support for Rapid DNA, the interface an analyst uses to review and annotate Rapid DNA field results, the use of DNA data sharing standards, the accreditation of the reach back capability, location/staffing/costs for the reach-back capability, and the eventual transition of the new capability to DHS LSSD facilities. Once the alternative reach-back solutions are proposed, a pilot solution would be developed to implement the reach back capability. This would include specifying and acquiring the appropriate technology, developing the detailed documentation to establish and maintain accreditation, researching and developing training materials, establishing performance metrics and risk mitigation recommendations and measurement plans, and addressing access and privacy protection solutions.

The Department of Homeland Security (DHS) Science and Technology Directorate's (S&T) Project Responder 5 Report identified key capabilities to help first responders be more effective in their mission. Among the findings included the need to securely share information, validate responders from other organizations, and securely maintain records. These challenges only increase as responders rely on more data. There is a critical need for responders to securely validate users and share information. Identity, Credential, & Access Management (ICAM) principles can mitigate these challenges. ICAM is a framework of policies built into an organization's IT infrastructure that allows system owners to have assurance that the right person is accessing the right information at the right time for the right reason. First Responders need to safely and securely share information between jurisdictions, but first responder organizations do not currently have federations set up to aid in information sharing. Instead, during multi-jurisdictional responses, organization might be forced to manually provision an un-vetted new user or take days to vet a new user's identity and certificates. Lead agencies require quick and secure solutions to vet identities and credentials in real time as well as auto-provision users into information sharing applications. ICAM On-the-Fly would allow new users to show up to assist in a public safety event, bringing their own credential, their own device and the role they are to provide during the event. Fundamentally, ICAM On-The-Fly must: -Perform Quick Identity Proofing; (e.g. validate that the user is who they says they are) -Validate applicable certifications and attributes required to access the information to be shared; (e.g. EMT Certified, sworn law enforcement) -Automatically Provision (register) New Users; -Be built using open standards to preserve interoperability; -Be cross platform (iOS/Android) compatible; and -Recognize a broad array of credential attributes in diverse environments (i.e. multiple types of LDAP, Active Directory, etc.)

First responders will need to carry many more devices such as sensors (environmental, physiological monitoring, hazard), IoT devices in addition to their cell phones and radios and peripheral devices (e.g., heads up displays) that require power. Each of these devices may have different power requirement (e.g., USB, USB-C, Apple,microUSB) and may need to be charged at different intervals depending on battery life and use. Requiring first responders to charge and track battery levels for all these devices would be an additional burden and work load. The innovation sought here is to develop a power module for first responders (PMFR) that would service all the current and emerging requirements of on-body devices. The Power Module would provide long-term, exchangeable and rechargeable battery, or viable alternative such as fuel cell, power to the various modules for extended use. Currently, DHS is aware of some power modules/battery packs that have been developed for Department of Defense (DoD) applications but none for the first responder civilian applications. It is anticipated that in the future if these power modules are deployed ubiquitously then sensors and peripheral devices no longer need built in power systems and can rely on the PMFR for power. Use of external power subsystems would then reduce the costs, size and form factor of sensors and peripheral devices. The PMFR should be: -Flexible to support a number of devices and power requirements (IoT devices, sensor modules, cellular and radio systems) -Swappable (swap out a unit with low charged with a fully charged device); ideally hot swappable -Portable (low size and weight for use on day to day applications and for carrying) -Low cost (objective $50/threshold $100 for non-intrinsic models) -Available for different applications (intrinsically safe for fire applications or standard ruggedized for EMS or law enforcement IP68 or CSA for intrinsic applications) -Operate for 24 hours (objective) or 8-12 hours (threshold)) -Rechargeable through 110 Volts or 12 volts (from vehicle) -Capable of providing battery status, report run-time remaining and alert when charge falls below a threshold -Capable of using standard battery or batteries (for backup) -Capable of detecting and reporting modules connected to the Power Module and provide battery status -Power status application with low-power alert function;

DHS and first responders need low cost, high performance sensors that can be used to detect chemical materials in different environments. A persistent problem in chemical sensing is the inability of the sensor system to reliably address complex sensing tasks and environments. Such conditions are regularly encountered in situations involving environmental monitoring, industrial process control, toxic chemical and fire detection. Often, these tasks are centered on the detection of chemical signatures rather than individual chemical compounds. However, detection of individual analytes is often complicated significantly by environmental conditions that exist in backgrounds with multiple potentially interfering chemical species. This can lead to surprisingly poor performance in real-world environments after excellent results have been demonstrated in the laboratory. Hence understanding the surrounding details of a chemical sensing problem is critical to finding a solution, together with knowing and addressing the target analytes themselves. Different types of sensors, a large number of them being based on molecular sensing capability and coupled with nanostructured surfaces, are being developed. However, most of these sensor developments are empirical and their performance, particularly the interplay between sensitivity and selectivity, cannot be predicted until the sensors are fully tested in a real-world environment. The costs to the user are therefore quite substantial for each sensor development before an objective assessment with regards to their usability can be made. On the other hand, a modeling-based approach, which would allow design of surfaces as well as the sensing device diagnostics, could allow for an inexpensive, user friendly approach to designing sensor materials that can be integrated with electronics to produce any type of sensor - chemical or biological, with parts per trillion (ppt) sensitivity and fast (seconds) response times. The reduction in cost compared to the current sensor development approaches which are empirical in design is expected to be at least an order of magnitude. The goal of the project is to develop sensors based on the rational designs of the theoretical models and evaluate the sensor performance in both pristine and complex environments relevant to the needs of the user community.

Currently fielded explosive detection equipment uses electromagnetic signals, such as X-rays or MMWs to interrogate passengers and their belongings. Automatic algorithms process the images generated by the screening hardware either to clear the passenger/property or to identify specific anomalies for further investigation. The use of machine learning and deep learning approaches to develop these algorithms have shown significant promise in improving overall system performance. The DHS S&T/TSA Passenger Screening Algorithm results showed the effectiveness of deep learning applied to passenger screening. Development of the equipment and its associated detection algorithms is time consuming and expensive because system screening performance is difficult to accurately model. Currently: -Prototype systems must be built and tested to measure and understand the interaction of X-rays/MMWs with explosives in various containment configurations. -Development requires physical test articles to be fabricated or acquired. Suitable test articles may even be impossible to create if the explosives involved are unsafe to synthesize. -If machine learning or deep learning algorithms are developed for detection, many test articles must be created and scanned to build datasets for algorithm development, training, and testing. This is particularly labor intensive in order to generate large, representative datasets. In order to accelerate the advancement of explosive detection equipment, the DHS S&T Directorate seeks to develop tools to create virtual models of human travelers, their baggage and its contents. These models: -Should be representative of the stream of commerce. -Should be capable of including simulated explosives and prohibited items. -Should be able to be generated in large numbers (many thousands or millions) in a reasonable amount of time (under 1 second per image). -Should be useable by researchers and vendors to predict the performance of emerging explosive detection technologies and to train machine learning-based detection algorithms. The predictions and training will make use of tools (see, for example, https://www1.aps.anl.gov/science/scientific-software) that simulate the propagation of X-rays/MMWs through simulated objects. -Should be useable for assessing a system's ability to detect emerging threats that are unsafe to synthesize. -Should be useable for a variety of electromagnetic interrogation methods including synthetic aperture radar, computed tomography, and single and multi-view (AT2) line scanners. These technologies use transmission, diffraction, and phase contrast to detect explosives and prohibited items. The tools should: -Include methods to create shape descriptions for explosives and other objects, and methods to insert these items into representative scans. The mathematical descriptions may be based on the union of geometric primitives, polygon meshes, and sampled three-dimensional volumes. -Include parametric descriptions for the features of explosives, so that users do not require access to classified information. -Be compatible with tools in the public domain for simulating X-ray/MMW interactions with objects. -Be compatible with script- or code-based algorithms targeting open-source multi-dimensional modeling software (e.g., MakeHuman and Blender) -Provide for a real-time means of dynamic configurability, especially as regards the physical properties of virtual materials to be used in the modeling and the system's input/output file pathways (e.g., use of "config files")

Organizations throughout the American economy and government are faced with designing and then operating cybersecurity risk management, in a complicated and dynamic environment. They have been provided with a useful starting point, a cybersecurity risk management framework, developed by NIST, supported by DHS, and filled out in some detail by different critical infrastructure sectors and organizations. But sustaining risk management operations is more difficult, as organizations must somehow blend a great deal of technical input (vulnerability reports, incident reports, threat analysis, technical guidance, etc.) with their own organizational experience. The cybersecurity "knowledge management" challenge is significant for any particular organization, regardless of size or critical infrastructure domain. Additionally, several million organizations and companies across the country are faced with this challenge, continuously. Most information sharing systems assume that these many organizations and companies should report their cybersecurity experiences vertically to commercial and governmental centers, which are to synthesize these various reports and report back analytical insight. But what does not yet exist is a peer-to-peer version of this reporting activity, where an organization can directly leverage related experiences of thousands of organizations and companies, through a tool that can capture and report their own experiences and connect them with comparable experience of other organizations and companies, to better help them understand and manage their cybersecurity risk. The end product of this effort should address capabilities such as: -Key internal risk assessment elements -The time/dynamics of internal risk assessment elements -Outside context for these assessments (vulnerabilities, operating data, etc.) -Multiple information sharing mechanisms (one to one, one to many, collaboration drafts, etc.) The key requirement is that this tool must be able to support enterprise consideration of cybersecurity risk, by bringing into the process valuable insight from other enterprise' consideration of risk

Networks, and systems of networks are ubiquitous in modern technology used throughout society today. Identification of risk in these networks often requires a model to be developed for the network or system of networks. These models range from the simple to the mathematically complicated models used for large networks. Some risks, such as cascading failures in a network, are difficult to identify. The goal for this effort is to develop the tools necessary to identify these risks, with a potential to identify mitigation strategies with an initial focus on emergency communications networks. The tool should be capable of including information about the network, such as number and type of nodes, appropriate labels for nodes, and known risks or defects for the network. The tools will also be capable of performing counterfactual or "what-if" analysis, to identify risks in the network, such as the potential for cascading failures. The tool shall be able to incorporate information about the network or system from the PARIDINE project. PARIDINE is intended to provide disruptive event information for large networks or the Internet. This includes: 1) a definition of a disruptive event; 2) identification of data to identify disruptive events; 3) identification and operational reporting via an API for disruptive events and 4) attribution or root cause analysis of the disruptive events, with a measure of attribution accuracy. At least three state space models will be produced under the phase I effort.

Blockchain and Distributed Ledger Technology (DLT) are emerging technologies being leveraged for a wide range of commercial and governmental applications. The most well-known use case would likely be Bitcoin, within the newly emerged cryptocurrency arena, which has spurred further interest and developments. Prior efforts have addressed Bitcoin analytics, which covers only a limited scope within the realm of cryptocurrencies. This proposal seeks applications of blockchain forensic analytics for newer cryptocurrencies, such as Zcash and Monero. And, ongoing research within the field also contributes to new technological implementations and techniques that continue to multiply the specific types of consensus, privacy, security, and proof mechanisms. A key feature underlying these newer blockchain platforms that is frequently emphasized is the capability for anonymity and privacy protection. While these features are desirable, there is similarly a compelling interest in tracing and understanding transactions and actions on the blockchain of an illegal nature. To that end, this proposal calls for solutions that enable law enforcement investigations to perform forensic analysis on blockchain transactions. This analysis can be approached in any number of ways and may consider different data situation use cases depending on whether additional data from off-chain sources are available. Furthermore, with the proliferation of new blockchain variants, the desired solution should either attempt to show generality or extensibility, or at least provide working approaches to treating newer blockchain implementations.

This topic seeks the development of relevant communications protocols, application programming interfaces (APIs), and interface control documentation (ICDs) to allow legacy and emerging radiation detection systems in operational use to be integrated into current and emerging networked systems. The effort would encompass surveying commonly deployed legacy radiation detection systems, cost-benefit analyses to assess the relative importance of which detection systems merit integration, and subsequent development of the required interfaces to permit integration of those systems. The effort must include the ability to transmit/stream the data from the sensor(s) to current and emerging networked systems. It should take into account that there are a multitude of sensors that can be categorized as permanent, deployable, and roving, all of which can be in GPS-denied environments. Proposed technical solutions must provide near-real-time transmission of sensor data when cellular or WiFi communication is unavailable. These capabilities are critical to operational environments where cellular is not readily available, such as U.S. Coast Guard operations, and U.S. Customs and Border Protection (CBP) U.S. Border Patrol (BP) operations. Solutions should be proposed that are capable of high bandwidth, secured, rugged, scalable, cost effective, and low size, weight, and power. Additionally, solutions that allow transmission of data while minimizing signatures for geolocation of the transmitter would also permit a wider range of CONOPS. Proposers should expect to develop working relationships with original equipment manufacturers (OEMs) of deployed legacy R/N detection systems and current performers supporting the current networked system.

The goal of this effort is to prove the concept of automated UAS to conduct radiation detection operations in a cluttered three-dimensional environment such as a cargo container yard, stadium, or parking lot. The only operator action will be to define the boundaries of the environment to be searched, to include defining basic search parameters (e.g. minimum separation distance from obstacles and flight line spacing). The UAS may include multiple small unmanned aerial vehicles. UAS capabilities must include: 1. Operation within 2 m of objects to be inspected during flight. 2. Detection of anomalous gamma-ray and neutron radiation. The onboard radiation detection systems will meet the radiological test detection requirements of the ANSI N42.48. 3. Production of a real-time "heat map" for radiation as flight is conducted. 4. Dwelling at locations where radiation anomalies are identified for as little as 30 seconds and no more than 5 minutes. 5. Optimization of search pattern to minimize search time while maintaining the ability to localize and identify radiological threats, including the ability to provide the operator with search time and battery usage estimates based on the definition of optimized search area and flight parameters provided by the operator. 6. LIDAR for collision avoidance and to map search area and using that information to develop an optimized search pattern. 7. Visual cameras to provide live feed of flight profile. 8. The ability to transmit location information of one small unmanned aerial vehicle (UAV) relative to the object being scanned and other unmanned aerial vehicles (if applicable). 9. Logging and transmitting to the operator and/or a designated reachback center geo-referenced gamma-ray spectra, visual imagery, LIDAR profile, and all flight parameters when the UAS records either a gamma-ray or neutron alarm. 10. Flexible communications (Satellite, Cellular Tower, Wireless, hardwired, etc.) depending on what is available at a given deployment location. 11. The ability to launch from a designated site, perform search, and return before running out of power or when "mission" is complete. 12. Communication of system health status back to operator ("heartbeat"). 13. Recharging for subsequent assignment. 14. A human interface that allows for all automated functions to be controlled manually. 15. A "kill" button for emergency power-down on both the human interface and the unmanned aerial vehicle itself. 16. Field repairs on limited life components prone to deteriorate due to the nature of their function/design. 17. Running full diagnostics on the UAS platform for maintenance purposes as well as firmware updates, etc.