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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Multi-Agent Control Planes for Quantum Networks: A Scalable Architecture for Autonomous Quantum Internet Management

Quantum networks are expected to enable distributed quantum computing, secure communication, and global entanglement distribution. However, operating such networks presents significant challenges, including stochastic quantum processes, fragile entanglement resources, dynamic topology, and cross-layer control requirements. Current quantum network control architectures largely rely on centralized or hierarchical controllers inspired by classical software-defined networking (SDN). While effective for small testbeds, these approaches face scalability, latency, and reliability limitations as quantum networks grow. This paper proposes a multi-agent control plane architecture for quantum networks. In this design, intelligent software agents operate at quantum nodes, repeaters, and orchestration layers, collectively managing entanglement generation, routing, purification, and scheduling. The distributed intelligence of the agent system allows the network to adapt dynamically to quantum hardware variability and environmental noise. We argue that multi-agent systems provide significant advantages over centralized control approaches, including scalability, resilience, local autonomy, and real-time adaptation. The paper discusses architectural design principles, agent coordination mechanisms, and research challenges in deploying multi-agent control planes for the emerging quantum Internet.

Alnajjar, Anees [ORNL] (ORCID:0000000237101601)↗
DOE OSTI2025

Hybrid classical-quantum communication networks

Over the past several decades, the proliferation of global classical communication networks has transformed various facets of human society. Concurrently, quantum networking has emerged as a dynamic field of research, driven by its potential applications in distributed quantum computing, quantum sensor networks, and secure communications. This prompts a fundamental question: rather than constructing quantum networks from scratch, can we harness the widely available classical fiber-optic infrastructure to establish hybrid quantum–classical networks? This paper aims to provide a comprehensive review of ongoing research endeavors aimed at integrating quantum communication protocols, such as quantum key distribution, into existing lightwave networks. This approach offers the substantial advantage of reducing implementation costs by allowing classical and quantum communication protocols to share optical fibers, communication hardware, and other network control resources—arguably the most pragmatic solution in the near term. In the long run, classical communication will also reap the rewards of innovative quantum communication technologies, such as quantum memories and repeaters. Accordingly, our vision for the future of the Internet is that of heterogeneous communication networks thoughtfully designed for the seamless support of both classical and quantum communications.

Fiber-optic communication↗
DOE OSTI2024

Secure authentication using recurrent neural networks

A computer-implemented method of user authentication is provided. The method comprises combining, by a computer system, a user recurrent neural network with a system recurrent neural network to form a unique combined recurrent neural network. The user recurrent neural network is configured to generate a unique user key, and the system recurrent neural network is configured to generate a system key. The computer system inputs a predetermined input into the combined recurrent neural network, and the combined recurrent neural network generates a unique combined key from the input, wherein the combined key differs from both the user key and system key. The computer system then associates the combined key with a unique access authorization to authenticate a user.

Aimone, James Bradley↗
DOE OSTI2024

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.

Marfo, William↗
DOE OSTI2025

Dependable classical-quantum computing systems engineering

Increasing evidence suggests quantum computing (QC) complements traditional High-Performance Computing (HPC) by leveraging its unique capabilities, leading to the emergence of a new, hybrid paradigm, QHPC. However, this integration introduces new challenges, with dependability–defined by reproducibility, resiliency, and security and privacy–emerging as a central concern for building trustworthy systems that provide an advantage to the users. This paper proposes a framework for dependable QHPC system design, organized around these three pillars. We identify integration challenges, anticipate roadblocks, and highlight productive synergies across QC, HPC, cloud platforms, and network security. Drawing from both classical computing principles and quantum-specific insights, we present a roadmap for co-design that supports robust hybrid architectures. Our approach offers concrete metrics for assessing dependability, provides design guidance for engineers working at the QC-HPC interface, and surfaces new engineering questions around complexity, scale, and fault tolerance. Ultimately, designing for dependability is key to realizing practical, scalable QHPC systems and accelerating the broader quantum ecosystem capable of translating quantum promises into actual application delivery.

HPC↗
DOE OSTI2024

Privacy Preserving Federated Learning for Advanced Scientific Ecosystems

We present a framework to provide privacy preserving (PP) federating learning (FL) across multiple computational and experimental facilities. This work joins the compute capabilities of National Energy Research Scientific Computing Center (NERSC) and Oak Ridge National Laboratory Research Cloud (ORC) with simulated experimental data, such as those produced at the SLAC National Accelerator Laboratory and Spallation Neutron Source (SNS). We describe the software infrastructure developed to provide privacy for computational and experimental networks. We developed algorithmic privacy across the federated system by embedding database security, computation, and communication into the federation architecture, utilizing scientific tools developed by the experimental community.

Archibald, Rick [ORNL] (ORCID:0000000245389780)↗
DOE OSTI2022

Communication systems and methods

A communication system is provided that include one or more processors that are configured to instruct computing devices that communicate messages with each other via a time-sensitive network to securely exchange the messages using secret information, ad direct the computing devices to exchange the secret information via a dedicated quantum channel in the time-sensitive network. The one or more processors are also configured to determine a quantum channel synchronization time associated with the secret information exchanged via the dedicated quantum channel, and modify a local classical oscillator based on the quantum channel synchronization time, the local classical oscillator configured to provide a current time.

Bush, Stephen Francis↗
DOE OSTI2024

End-to-End Encryption for Cyber-Physical Systems Using Fully Homomorphic Encryption

Cyber-physical systems require reliable, safe, and secure control of critical infrastructure, combining computational and networking capabilities, which heighten the risk of cyber attacks. These attacks can disrupt the physical process, causing unforeseen consequences. One solution is the use of fully homomorphic encryption (FHE) to protect the control loop, allowing for secure computations and communications without compromising signal and control system privacy. The challenge with FHE, however, is its requirement for inputs to be integers. This presentation introduces a modified Learning With Errors (LWE) FHE approach that encodes control system dynamics and signals into integers. Our proposed scheme leverages a generalized LWE encoding function and modifies the Gentry-Sahai-Waters gadget decomposition tool to encrypt the control system. Using the modified LWE scheme, we formalize a fully encrypted control system, supported by simulated results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Encrypted Control Using Modified Learning With Errors-based Schemes

Cyber-physical systems (CPSs) require reliable, safe, and secure control of critical infrastructure, combining computational and networking capabilities, which heighten the risk of cyber attacks. These attacks can disrupt the physical process, causing unforeseen consequences. One solution is the use of fully homomorphic encryption (FHE) to protect the control loop, allowing for secure computations and communications without compromising signal and control system privacy. The challenge with FHE, however, is its requirement for inputs to be integers. This paper introduces a modified Learning With Errors (LWE) FHE approach that encodes control system dynamics and signals into integers. Our proposed scheme leverages a generalized LWE encoding function and modifies the Gentry-Sahai-Waters (GSW) gadget decomposition tool to encrypt the control system. Using the modified LWE scheme, we formalize a fully encrypted control system, supported by simulated results.

42 - ENGINEERING↗
DOE OSTI2016

Best Paper Award: Gathering Threat Intelligence Through Computer Network Deception

Presented at the 2016 IEEE International Symposium on Technologies for Homeland Security (HST), this LDRD-funded research received a Best Paper award, the highest award presented at the conference. The research focused on developing, implementing, and testing a novel computer network operations architecture that enables proactive defense by managing and monitoring enterprise resource allocations and network flows. The architecture leverages three emerging concepts: software-defined networks, cloud computing, and deception, to enable the detection and identification of anomalous access and intrusions to adjust to the dynamic nature of the adversary and to provide a mechanism to discover and react to the adversary’s attacks in a methodical and proactive manner. Additionally, the work allows network defenders to gather information on an adversary’s tools, tactics and procedures, providing insight into the “what, why, and how” of the adversary’s operations. A summary of the work presented at IEEE can be found at http://tiny.sandia.gov/8d8lu.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2019

Mathematical approaches to secure in-vehicle networks

Modern vehicles rely on numerous small computers called electronic control units (ECUs) that continually communicate life-critical messages, e.g., wheel speeds and steering angle, through a few controller area networks (CANs). Originally designed as a closed network for the life of the vehicle, CAN protocol developed bereft of security features (e.g., authentication or encryption). For emission testing, updates, and other diagnostics, vehicle CANs are now physically accessible, but this comes with vulnerability to cyber attack. Mandatory on-board diagnostic ports (OBD-II) provide adversaries with direct access to the vehicle. Remote attacks, e.g., via Bluetooth or cellular connections, are possible but require deep expertise. In short, the critical networks for vehicle functionality are exposed to remote and local cyber attacks with little to no built-in defenses. Most notably, the infamous 2015 research of Miller and Valasek exhibited a remote takeover of a Jeep’s brakes resulting in a massive recall and ample media coverage.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Order priors for Bayesian network discovery with an application to malware phylogeny

Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges) in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Using advanced data structures to enable responsive security monitoring

Write-optimized data structures (WODS), offer the potential to keep up with cyberstream event rates and give sub-second query response for key items like IP addresses. These data structures organize logs as the events are observed. To work in a real-world environment and not fill up the disk, WODS must efficiently expire older events. As the basis for our research into organizing security monitoring data, we implemented a tool, called Diventi, to index IP addresses in connection logs using RocksDB (a write-optimized LSM tree). In this work, we extended Diventi to automatically expire data as part of the data structures’ normal operations. We guarantee that Diventi always tracks the N most recent events and tracks no more than N + k events for a parameter k < N, while ensuring the index is opportunistically pruned. To test Diventi at scale in a controlled environment, we used anonymized traces of IP communications collected at SuperComputing 2019. We synthetically extended the 2.4 billion connection events to 100 billion events. We tested Diventi vs. Elasticsearch, a common log indexing tool. In our test environment, Elasticsearch saw an ingestion rate of at best 37,000 events/s while Diventi sustained ingestion rates greater than 171,000 events/s. Our query response times were as much as 100 times faster, typically answering queries in under 80 ms. Furthermore, we saw no noticeable degradation in Diventi from expiration. We have deployed Diventi for many months where it has performed well and supported new security analysis capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

An Overview of the Usefulness of Machine Learning Techniques on Network Packet Data

Understanding the health and behavior of a computer network allows for better network efficiency and security. We present an overview of various machine learning techniques for classifying network packet data via packet metadata. While some classical machine learning approaches achieve reasonable results, the most accurate classification can be achieved with deep learning. On the four data sets studied herein, a basic deep learning model achieved at or near 100\% classification accuracy. We also propose a method for determining variable importance as a means for potential transfer learning applications to classifying yet unseen network packet data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Space-based quantum networks are an essential component of future architecture for distributed quantum computers and quantum-enhanced secure communication

Space-based quantum links show great promise for connecting and communicating between quantum computers over ultra-long distances without the high loss incurred through fiber. A successful US quantum satellite would require large investment, national priority, and a diverse set of expertise. But, it would deliver US-owned quantum links that would allow for quantum-enhanced secure communications and the ability to connect quantum computers over long distances.

97 MATHEMATICS AND COMPUTING↗
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↗