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Extending quantum key distribution through proxy re-encryption
Modern quantum key distribution (QKD) network designs are based on sending photons from one node to another and require free-space or dedicated fiber optic cables between nodes. The purpose of this is to co-generate secret key material on both sides of the quantum channel. In addition to this quantum link, there are several insecure classical channels that allow QKD algorithms to exchange book-keeping information and send symmetrically encrypted data. The attenuation of photons transmitted through fiber becomes too high to practically generate key material over fiber at distances of more than 100 km. Free-space transmission through the atmosphere or the vacuum of space can reduce attenuation, but at the cost of system complexity and sensitivity to other impairments, such as weather. To extend the effective range of QKD networks, we present a method that combines QKD algorithms with post-quantum, homomorphic key-switching to allow multiple parties to effectively share secret key material over longer distances through semi-trusted relay nodes. We define how such a system should work for arbitrary network topologies and provide proofs that our scheme is both correct and secure. We assess the feasibility of this solution by building and evaluating two implementations based on lattice-based cryptography: learning with errors.
Secure boot, trusted boot and remote attestation for ARM TrustZone-based IoT Nodes
With the extensive application of IoT techniques, IoT devices have become ubiquitous in daily lives. Meanwhile, attacks against IoT devices have emerged to compromise IoT devices by tampering with system pre-installed programs or injecting new malware. To mitigate these attacks, integrity enforcement of IoT systems has been proposed. The integrity of an IoT device system includes load-time integrity and runtime integrity. In this paper, we design an IoT system based on ARM TrustZone to enforce the system integrity. First, we establish the root of trust and propose a hybrid booting approach consisting of both secure boot and trusted boot to enforce the system load-time integrity. Second, we investigate a paging-based process integrity measurement method to measure the NW processes and conduct remote attestation based on the measurement results ensuring the NW runtime process integrity. We implement an IoT prototype system on a NXP i.MX6Q SABRE SD development board to assess its feasibility. Finally, real-world experiment results demonstrate that our prototype introduces negligible performance overhead to the original system.
Accessible Telemetry Streams using a Zero Trust Architecture for the Flight Operations Directorate
As a result of information technology based work becoming increasingly distributed, unique challenges have been presented within the realm of defined network perimeters, namely with respect to secure access to resources. Historically, and from a simplistic abstract perspective, the common approach has been to adopt the, so-called, moat model whereby a physical network perimeter (or interconnected perimeters) is defined to encapsulate resources behind a boundary protected by a firewall. Users are provisioned access through a virtual private network (VPN) and may be further constrained to resources through specific firewall allow and disallow rulesets. Virtual Private Networks and firewall rulesets lead to common problems, particularly at scale and, as a result, perimeter-less architectures provided over the public internet are increasingly becoming prevalent, particularly with its more popular implementation, the Zero Trust Architecture. We present a proposed implementation of the Zero Trust Architecture with a particular concrete example utilizing a de-perimeterized network that requires authentication and authorization for each action between nodes and does not operate within an implicit trust boundary. It should be noted that this paper is not an attempt at providing comprehensive resolutions for the specific problem space with respect to perimeter based security and is more directed at providing information with regard to our proposed implementation of a Zero Trust Architecture for the Flight Operations Directorate. We direct the reader to our Introduction and Background section for more details on specific documentation and where it can be located as it relates to de-perimeterization and Zero Trust.
Distributed Detection of Malicious Attacks on Consensus Algorithms with Applications in Power Networks
Consensus-based distributed algorithms are well suited for coordination among agents in a cyber-physical system. These distributed schemes, however, suffer from their vulnerability to cyber attacks that are aimed at manipulating data and control ow. In this article, we present a novel distributed method for detecting the presence of such intrusions for a distributed multi-agent system following ratio consensus. We employ a Max-Min protocol to develop low cost, easy to implement detection strategies where each participating node detects the intrusion independently, eliminating the need for a trusted certifying agent in the network. The effectiveness of the detection method is demonstrated by numerical simulations on a 1000 node network to demonstrate the efficacy and simplicity of implementation.
A DNA-Inspired Encryption Methodology for Secure, Mobile Ad Hoc Networks
Users are pushing for greater physical mobility with their network and Internet access. Mobile ad hoc networks (MANET) can provide an efficient mobile network architecture, but security is a key concern. A figure summarizes differences in the state of network security for MANET and fixed networks. MANETs require the ability to distinguish trusted peers, and tolerate the ingress/egress of nodes on an unscheduled basis. Because the networks by their very nature are mobile and self-organizing, use of a Public Key Infra structure (PKI), X.509 certificates, RSA, and nonce ex changes becomes problematic if the ideal of MANET is to be achieved. Molecular biology models such as DNA evolution can provide a basis for a proprietary security architecture that achieves high degrees of diffusion and confusion, and resistance to cryptanalysis. A proprietary encryption mechanism was developed that uses the principles of DNA replication and steganography (hidden word cryptography) for confidentiality and authentication. The foundation of the approach includes organization of coded words and messages using base pairs organized into genes, an expandable genome consisting of DNA-based chromosome keys, and a DNA-based message encoding, replication, and evolution and fitness. In evolutionary computing, a fitness algorithm determines whether candidate solutions, in this case encrypted messages, are sufficiently encrypted to be transmitted. The technology provides a mechanism for confidential electronic traffic over a MANET without a PKI for authenticating users.
Zero-Trust Architecture for Autonomous Edge Computing
We are at the apex of an aviation revolution where autonomy will play a central role in enabling complex, multi-agent systems to communicate, interact, and collaborate on a myriad of applications spanning autonomous swarms to wild-fire management. Autonomy is not an absolute but rather a spectrum ranging from a system requiring significant human intervention to one requiring little to none [1]. For example, the extreme, in the case of an autonomous aircraft, is one that operates independently in the airspace interacting with all other elements (air traffic controllers, other pilots) as if it were a human pilot. Critical to this vision is an architecture that enables autonomous agents to interact with minimal latency. Edge computing is an emerging architecture where compute and storage is pushed to the ‘edge’ of the network in order to minimize the round-trip time from agent to resource thereby mitigating the latency associated with cloud-only based approaches. Additionally, services can generate massive amounts of data (e.g., video feeds), which may require analysis in near real-time. Moving this data to the cloud for further processing may not be feasible due to latency, bandwidth, and cost. Privacy, security, and reliability can also be improved by edge computing architectures. However, this geo-distributed and dynamic* architecture complicates the establishment of unambiguous network security boundaries and can lead to vulnerabilities including man in the middle attacks, replay attacks, physical security breaches of edge nodes, signal interception, etc. This motivates the need for zero-trust architectures [2–4] which de-emphasize the notion of static network perimeters and, as the name implies, do not instill any innate trust in any particular agent. It is required that all agents must be authorized and approved in every transaction. In this paper, we present a zero-trust architecture suitable for edge-computing applications that demand significant low-latency, security, privacy, and reliability.
Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes: The case of water–methane dimer
Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely trusted many-body method for solving the Schrödinger equation, known for its reliable predictions of material and molecular properties. Furthermore, its excellent scalability with system complexity and near-perfect utilization of computational power make FN-DMC ideally positioned to leverage new advances in computing to address increasingly complex scientific problems. Even though the method is widely used as a computational gold standard, reproducibility across the numerous FN-DMC code implementations has yet to be demonstrated. This difficulty stems from the diverse array of DMC algorithms and trial wave functions, compounded by the method’s inherent stochastic nature. Here, this study represents a community-wide effort to assess the reproducibility of the method, affirming that yes, FN-DMC is reproducible (when handled with care). Using the water–methane dimer as the canonical test case, we compare results from eleven different FN-DMC codes and show that the approximations to treat the non-locality of pseudopotentials are the primary source of the discrepancies between them. In particular, we demonstrate that, for the same choice of determinantal component in the trial wave function, reliable and reproducible predictions can be achieved by employing the T-move, the determinant locality approximation, or the determinant T-move schemes, while the older locality approximation leads to considerable variability in results. These findings demonstrate that, with appropriate choices of algorithmic details, fixed-node DMC is reproducible across diverse community codes—highlighting the maturity and robustness of the method as a tool for open and reliable computational science.
Diagnosable structured logic array
A diagnosable structured logic array and associated process is provided. A base cell structure is provided comprising a logic unit comprising a plurality of input nodes, a plurality of selection nodes, and an output node, a plurality of switches coupled to the selection nodes, where the switches comprises a plurality of input lines, a selection line and an output line, a memory cell coupled to the output node, and a test address bus and a program control bus coupled to the plurality of input lines and the selection line of the plurality of switches. A state on each of the plurality of input nodes is verifiably loaded and read from the memory cell. A trusted memory block is provided. The associated process is provided for testing and verifying a plurality of truth table inputs of the logic unit.
A Distributed Trust Model Simulator for Energy Grid of Things Distributed Energy Resource Management System
The evolution of networks into more distributed, self-reliant nodes has mitigated single-point failures that plagued traditional centralized networks. Applied to power grids, distributed systems can increase the integrity and availability of grid services while also offering a power management solution. However, while distributed networks provide scalability, security, and sustainability compared to centralized networks, their distributed nature makes them harder for anomaly detection and prevention. Incorporating a Distributed Trust Model (DTM) System into an Energy Grid of Things Distributed Energy Resource Management System (EGOT DERMS) allows grid participants to be characterized and their communication to be analyzed for possible attacks. A Trust Model simulator is needed to evaluate and improve the DTM System.Trustworthiness is calculated using a Trust Model. While many trust models exist, most only consider 2-3 matrices to evaluate trust. The TM proposed in this thesis uses a Metric Vector of Trust (MVoT) monitoring 17 parameters when assessing trust. Moreover, unlike standard trust models, the proposed trust model establishes a method to test the trust between various actors within the network and probe the trust model itself. Using a Trust Model Simulator, MVoT calaculations, initial values, and parameters are fine-tuned to achieve high-confidence message classifications and minimize false positives. The DTM System and Trust Mode Simulation Suite allow for distributed trust evaluation with a real-time classification of EGOT DERMS actors, providing additional security for distributed systems.
pnnl/UUDEX
UUDEX describes a communications architecture and protocol suite that allows organizations to exchange data and information. It does this by defining relations between a set of client nodes that share data via a set of server nodes. The primary focus of UUDEX is to facilitate communications between control centers, operations centers, and other trusted organization.
Newton trust-region methods with primary variable switching for simulating high temperature multiphase porous media flow
Coupling multiphase flow with energy transport due to high temperature heat sources introduces significant new challenges since boiling and condensation processes can lead to dry-out conditions with subsequent re-wetting. The transition between two-phase and single-phase behavior can require changes to the primary dependent variables adding discontinuities as well as extending constitutive nonlinear relations to extreme physical conditions. Practical simulations of large-scale engineered domains lead to Jacobian systems with a very large number of unknowns that must be solved efficiently using iterative methods in parallel on high-performance computers. Performance assessment of potential nuclear repositories, carbon sequestration sites and geothermal reservoirs can require numerous Monte-Carlo simulations to explore uncertainty in material properties, boundary conditions, and failure scenarios. Due to the numerical challenges, standard NR iteration may not converge over the range of required simulations and require more sophisticated optimization method like trust-region. In this study, we use the open-source simulator PFLOTRAN for the important practical problem of the safety assessment of future nuclear waste repositories in the U.S. DOE geologic disposal safety assessment Framework. The simulator applies the PETSc parallel framework and a backward Euler, finite volume discretization. We demonstrate failure of the conventional NR method and the success of trust-region modifications to Newton’s method for a series of test problems of increasing complexity. Trust-region methods essentially modify the Newton step size and direction under some circumstances where the standard NR iteration can cause the solution to diverge or oscillate. Furthermore, we show how the Newton Trust-Region method can be adapted for Primary Variable Switching (PVS) when the multiphase state changes due to boiling or condensation. The simulations with high-temperature heat sources which led to extreme nonlinear processes with many state changes in the domain did not converge with NR, but they do complete successfully with the trust-region methods modified for PVS. This implementation effectively decreased weeks of simulation time needing manual adjustments to complete a simulation down to a day. Finally, we show the strong scalability of the methods on a single node and multiple nodes in an HPC cluster.
A Distributed Simulation-to-Flight Framework to Support Investigating Trust/Trustworthiness in Multi-Agent Systems
As autonomous systems continue to grow both in use and complexity, the necessity for robust and extensible simulation-to-flight methods is paramount for establishing an effective architecture for autonomous systems. A fundamental objective of the ATTRACTOR (Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability) project was to design and develop a distributed mixed-reality simulation environment to begin establishing a basis for certification of autonomous systems via research into trust and trustworthiness. In this paper, we present an autonomous systems architecture and development framework paired with a persistent distributed modeling and simulation environment for test and evaluation of autonomous systems. The Autonomous Entity Operations Network (AEON) framework enables autonomous system development with an easily extensible collection of libraries and plug-n-play nodes facilitated by the Data Distribution Service (DDS) communication protocol standard. The Baseline Environment for Autonomous Modeling (BEAM) simulation environment is a distributed mixed-reality Unity™-based environment built around the same DDS communication paradigm allowing for easy integration with AEON-based autonomous applications. They were designed under ATTRACTOR in order to measure and establish trustworthiness and trust in single- and multi-agent human-machine systems whether these machines are fixed-wing general aviation, rotary-wing Unmanned Aerial Vehicles (UAVs), ground rovers, or even spacecraft. Together AEON and BEAM enable sim-to-flight with minimal configuration changes. By using AEON and BEAM, source code that runs in simulation ports directly to hardware and has successfully flown in the lab and in the National Airspace System (NAS) at NASA LaRC many times over the lifetime of ATTRACTOR.
A Distributed Simulation-to-Flight Framework to Support Investigating Trust/Trustworthiness in Multi-Agent Systems
As autonomous systems continue to grow both in use and complexity, the necessity for robust and extensible simulation-to-flight methods is paramount for establishing an effective architecture for autonomous systems. A fundamental objective of the ATTRACTOR (Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability) project was to design and develop a distributed mixed-reality simulation environment to begin establishing a basis for certification of autonomous systems via research into trust and trustworthiness. In this paper, we present an autonomous systems architecture and development framework paired with a persistent distributed modeling and simulation environment for test and evaluation of autonomous systems. The Autonomous Entity Operations Network (AEON) framework enables autonomous system development with an easily extensible collection of libraries and plug-n-play nodes facilitated by the Data Distribution Service (DDS) communication protocol standard. The Baseline Environment for Autonomous Modeling (BEAM) simulation environment is a distributed mixed-reality Unity™-based environment built around the same DDS communication paradigm allowing for easy integration with AEON-based autonomous applications. They were designed under ATTRACTOR in order to measure and establish trustworthiness and trust in single- and multi-agent human-machine systems whether these machines are fixed-wing general aviation, rotary-wing Unmanned Aerial Vehicles (UAVs), ground rovers, or even spacecraft. Together AEON and BEAM enable sim-to-flight with minimal configuration changes. By using AEON and BEAM, source code that runs in simulation ports directly to hardware and has successfully flown in the lab and in the National Airspace System (NAS) at NASA LaRC many times over the lifetime of ATTRACTOR.
Transient thermal analysis of fluid systems
Computer program performs transient thermal analysis of any 2-node to 200-node-thermal network, which transports heat by fluid flow convection. Program can be modified to add conduction along tubes and radiation.
Emulation and Adversarial Analysis of EV Charging Networks
In the effort of decarbonization and evolution of the modern electrical grid, electric vehicles (EVs) play a key part to transform the grid. However, due to the rapid adoption of EVs and the demand of the charging infrastructure required to power said EVs, risk of a cyber-attack may impose serious consequences. There is a need to analyze and protect the charging ecosystem infrastructure from cyber threats before it reaches wide-scale deployment. In an effort to secure vehicle to grid (V2G) communications, standardization is necessary for continued reliable system operation. The protocol ISO 15118 outlines controls and practices that should be implemented for secure vehicle to grid (V2G) communications. The standard is gaining momentum for American markets as the demand for EV infrastructure grows. The adoption of ISO 15118 in American markets poses several challenges: the deployment of a public key infrastructure (PKI) as outlined within the standard, interoperability of charging different EVs with chargers from different manufactures using the PKI, and scaling the ecosystem to meet the demand while managing risks. This project was created to understand potential cyber and scaling challenges of PKI for EV infrastructure through utilizing a series of emulated components mapping to what exists in the EV ecosystem today, and the components of the PKI that are under development. The key nodes within the emulation that are under development are: electric vehicle (EV), electric vehicle supply equipment (EVSE), charge network operator (CNO), certificate authority (CA), and online certificate status protocol (OCSP) that must all interact using secure and trusted communications. With these emulated components and utilizing orchestration methods to rapidly deploy and scale the components, the ability to analyze risks of the ecosystem and address gaps before the PKI ecosystem is fully deployed to production should yield a more robust and mature production charging infrastructure. Our approach will use a modular architecture of virtual machines within an orchestration platform and will target scales of 100s, 1000s, and 10,000s of entities interacting. The core research questions trying to be answered with this scope of work are: what are the impacts of a rogue CA, what are the risks of certificate revocation list (CRL) management, what is the value of OCSP stapling, what components are vulnerable to DOS attacks, and what test effective payloads may impact the components.
Resilient information and inference networks under mixed-trust sensing
With ubiquitous digitization, sensing, and computational intelligence deployed in increasingly more and broader domains, including critical infrastructure, potentially misleading and destabilizing effects of multimodal anomalies and adversarial behavior are growing in importance. Here, we develop randomized and reinforcement learning-based strategies for strategically recruiting and utilizing deployed (and, thus, vulnerable and potentially faulty and/or compromised) nodes from information and inference networks, while defending against adversaries that attempt to misguide assessments of inferred variables. Recognizing that, besides communication and other costs, sampling from any observable node can either provide true data or dangerously expose our inference to misinformation (without being easily distinguishable what actually happens), the proposed strategies proceed by progressively recruiting nodes and cautiously scaling their information contribution based on assumed, or, in our reinforcement learning approach, intelligently weighed trustworthiness, with the learning approach also considering network-wide, threat-inclusive risk/value tradeoffs. While avoiding the hardware, communication, analytical and computational burden of explicit redundancy, the proposed defensive schemes enable on-the-fly assessments of underlying processes, and system-wide situational awareness with demonstrable resilience against adversarial activities.
Linear and Nonlinear Solvers for Simulating Multiphase Flow within Large-Scale Engineered Subsurface Systems
Simulation of multiphase flow in the subsurface is well-known to be computationally challenging. While there have been many studies that have explored approaches to overcoming these challenges, they often utilize relatively simple case studies. In this paper, we focus on the unique numerical challenges posed by modeling large-scale engineered subsurface systems, characterized by discrete features embedded in a heterogeneous natural subsurface setting. The man-made features such as shafts, tunnels, and barriers often cause multiple challenges in modeling the domain for multiphase porous media flow. This flow scenario can have a wide range of applications such as nuclear waste repositories, enhanced recovery of a petroleum reservoir, geothermal engineering, and carbon sequestration. An example of these severe numerical challenges is the case of performance assessment (PA) for Waste Isolation Pilot Plant (WIPP), the only operating deep geological repository in the US, which simulates extreme material properties of bedded salt rock formation and extreme contrast due to open excavation next to the formation. The models have extremes not only of permeability and porosity but also of the constitutive models needed for multiphase flow; additionally, they have process models like salt creep closure reducing porosity over time, fracturing in clay and anhydrite interbeds of the bedded salt, gas generation from the waste materials, and unintentional human borehole intrusions in some scenarios. Numerical simulations require the solution of coupled systems of nonlinear PDEs; in our work, we use the open-source simulator PFLOTRAN which is based on Finite Volume discretization. The solution of the nonlinear equations requires use of the Newton-Raphson iteration at each time step, which entails the solution of the linearized Jacobian system at each iteration. The effects of all the processes (i.e., large number of unknowns, highly nonlinear constitutive relations, large contrasts in material properties in short distances) lead to an ill-conditioned Jacobian matrix that severely challenges traditional linear solver, i.e., stabilized biconjugate gradient with block Jacobi incomplete LU preconditioner (BCGS-ILU) leading to non-convergence for traditional Newton-Raphson nonlinear solver causing unacceptably long computation time for each model. This paper presents linear solvers such as constrained pressure residual (CPR) two-stage preconditioner with alternate-block-factorization (ABF) and quasi- implicit pressure and explicit saturation (QIMPES) decouplers and flexible generalized residual solver (FGMRES). The new general-purpose nonlinear solver, Newton trust-region dogleg Cauchy (NTRDC), is also introduced to resolve extreme nonlinearities in the models. We demonstrate the effectiveness of each method relative to the default BCGS-Newton solver. The two best cases had nearly 50 times speed-up and achieved completion of a simulation in 14 hours that never completed due to non-convergence with the default solver. We also investigate the strong scalability of each method and discuss some of the deficiencies found for Block Jacobi preconditioner using parallel domain decomposition, and node packing effects of modern processor architecture.