Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “attack graph”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1973

A simulator investigation of the influence of engine response characteristics on the approach and landing for an externally blown flap aircraft. Part 2: Aerodynamic model

An analysis of the influence of engine response characteristics on the approach and landing of an externally blown flap aircraft was conducted using flight simulator facilities. The configuration of the aerodynamic model is described. The aerodynamic characteristics as a function of angle of attack, thrust coefficient, and flap deflection are presented in tabular form and as graphs.

Ciffone, D. L.↗
NASA NTRS1942

Pressure distribution over an NACA 23012 airfoil with a fixed slot and a slotted flap

Report presents the results of a pressure-distribution investigation conducted in the Langley Memorial Aeronautical Laboratory 7 by 10-foot wind tunnel to determine the air loads on an NACA 23012 airfoil in combination with a fixed leading-edge slot and a slotted flap. Pressures were measured over the upper and lower surfaces of the component parts of the combination for several angles of attack and at several flap settings. The data, presented as pressure diagrams and graphs of section coefficients, are applicable to rib, slat, and flap designs for the combination.

Harris, Thomas A↗
NASA NTRS2015

Analyzing Cyber Security Threats on Cyber-Physical Systems Using Model-Based Systems Engineering

The spectre of cyber attacks on aerospace systems can no longer be ignored given that many of the components and vulnerabilities that have been successfully exploited by the adversary on other infrastructures are the same as those deployed and used within the aerospace environment. An important consideration with respect to the mission/safety critical infrastructure supporting space operations is that an appropriate defensive response to an attack invariably involves the need for high precision and accuracy, because an incorrect response can trigger unacceptable losses involving lives and/or significant financial damage. A highly precise defensive response, considering the typical complexity of aerospace environments, requires a detailed and well-founded understanding of the underlying system where the goal of the defensive response is to preserve critical mission objectives in the presence of adversarial activity. In this paper, a structured approach for modeling aerospace systems is described. The approach includes physical elements, network topology, software applications, system functions, and usage scenarios. We leverage Model-Based Systems Engineering methodology by utilizing the Object Management Group's Systems Modeling Language to represent the system being analyzed and also utilize model transformations to change relevant aspects of the model into specialized analyses. A novel visualization approach is utilized to visualize the entire model as a three-dimensional graph, allowing easier interaction with subject matter experts. The model provides a unifying structure for analyzing the impact of a particular attack or a particular type of attack. Two different example analysis types are demonstrated in this paper: a graph-based propagation analysis based on edge labels, and a graph-based propagation analysis based on node labels.

MBSE↗
NASA NTRS1985

Experimental study of forward-located jet-interaction nozzles

The effects of forward-mounted sonic jet-interaction nozzles with one, three equal, or one large and two small orifices on the transitional or turbulent boundary-layer flow over a biconic (9-deg-25-arcmin/6-deg) model at angle of attack -10 to the 15 deg and Reynolds number 8.10 x 10 to the 6th/ft are investigated experimentally using pressure taps, force measurements, oil-flow visualization, and schlieren photography in the 20-in. Mach-6 wind tunel at NASA Langley. The injection flow rates are 0.055 and 0.026 lbm/s, with stagnation pressure 500 psia, stagnation temperature 1000 R, and dynamic pressure 8.7 psia. The results are presented in diagrams, graphs, and photographs and characterized. Moment amplification factors of 1.0 or better (maximum 1.8 at angle of attack 15 deg and flow rate 0.055 lbm/s) are observed for all nozzle types, but the configuration with three unequal orifices is found to give improvements of 10 percent over the single-orifice nozzle at a given flow rate.

Cavalleri, R. J.↗
NASA NTRS1941

Pressure distribution over NACA 23012 airfoil with a slotted and a split flap

A pressure-distribution investigation has been conducted in the NACA 4 by 6-foot vertical wind tunnel to determine the air loads on an NACA 23012 airfoil in combination with a 25.66-percent-chord slotted flap and a 20-percent-chord split flap. Pressures were measured on both the upper and the lower surfaces of the main airfoil and the flaps for several angles of attack and at several flap settings. The data, presented as pressure diagrams and as graphs of the section coefficients for the flap alone and for the airfoil-flap combinations, are applicable to rib and flap design for a combination of a thick airfoil and a slotted or a split flap. The results of previous tests of a NACA 23012 airfoil with a slotted flap are compared with the present results.

Harris, Thomas A↗
DOE OSTI2025

Robustness of Deep Learning Classification to Adversarial Input on GPUs: Asynchronous Parallel Accumulation Is a Source of Vulnerability

The ability of machine learning (ML) classification models to resist small, targeted input perturbations—known as adversarial attacks—is a key measure of their safety and reliability. We show that floating-point non associativity (FPNA) coupled with asynchronous parallel programming on GPUs is sufficient to result in misclassification, without any perturbation to the input. Additionally, we show that this misclassification is particularly significant for inputs close to the decision boundary and that standard adversarial robustness results may be overestimated up to 4.6 when not considering machine-level details. We first study a linear classifier, before focusing on standard Graph Neural Network (GNN) architectures and datasets used in robustness assessments. We develop a novel black-box attack using Bayesian optimization to discover external workloads that can change the instruction scheduling which bias the output of reductions on GPUs and reliably lead to misclassification. Motivated by these results, we present a new learnable permutation (LP) gradient-based approach to learning floating-point operation orderings that lead to misclassifications. The LP approach provides a worst-case estimate in a computationally efficient manner, avoiding the need to run identical experiments tens of thousands of times over a potentially large set of possible GPU states or architectures. Finally, using instrumentation-based testing, we investigate parallel reduction ordering across different GPU architectures under external background workloads, when utilizing multi-GPU virtualization, and when applying power capping. Our results demonstrate that parallel reduction ordering varies significantly across architectures under the first two conditions, substantially increasing the search space required to fully test the effects of this parallel scheduler-based vulnerability. These results and the methods developed here can help to include machine-level considerations into adversarial robustness assessments, which can make a difference in safety and mission critical applications.

Shanmugavelu, Sanjif [Maxeler Technologies, a Groq↗
NASA NTRS1938

Pressure distribution over an NACA 23012 airfoil with a slotted and a plain flap

Report presents the results of pressure-distribution of an NACA 23012 airfoil equipped with a slotted flap and with a plain flap conducted in the 7 by 10-foot wind tunnel. A test installation was used in which the 7-foot-span airfoil was mounted vertically between the upper and lower sides of the closed test section so that two-dimensional flow was approximated. The pressures were measured on the upper and lower surfaces at one chord section both on the main airfoil and on the flaps for several different flap deflections and at several angles of attack. The data are presented in the form of pressure-distribution diagrams and as graphs of calculated section coefficients for the airfoil-and-flap combinations and also for the flaps alone. The results are useful for application to rib and flap structural design; in addition, the plain-flap data furnish considerable information applicable to the structural design of plain ailerons.

Wenzinger, Carl J↗
DOE OSTI2026

Community detection robustness of graph neural networks

Graph neural networks (GNNs) are increasingly widely used for community detection in attributed networks. They combine structural topology with node attributes through message passing and pooling. However, their robustness or lack thereof with respect to different perturbations and targeted attacks in conjunction with community detection tasks is not well understood. To shed light on latent mechanisms behind GNN sensitivity on community detection tasks, we conduct a systematic computational evaluation of six widely adopted GNN architectures graph convolutional network, graph attention network, graph sample and aggregate (GraphSAGE), differentiable pooling (DiffPool), minimum cut pooling (MinCUT), and deep modularity networks (DMoN). The analysis covers three perturbation categories: node attribute manipulations, edge topology distortions, and adversarial attacks. We use element-centric similarity as the evaluation metric on synthetic benchmarks and real-world citation networks. Our findings indicate that supervised GNNs tend to achieve higher baseline accuracy, while unsupervised methods, particularly DMoN, maintain stronger resilience under targeted and adversarial perturbations. Furthermore, robustness appears to be strongly influenced by community strength, with well-defined communities reducing performance loss. Across all models, node attribute perturbations associated with targeted edge deletions and shifts in attribute distributions tend to cause the largest degradation in community recovery. These findings highlight important trade-offs between accuracy and robustness in GNN-based community detection and offer insights into selecting architectures resilient to noise and adversarial attacks.

Goel, Jaidev [Virginia Polytechnic Inst. and State↗
DOE OSTI2024

GraphCH: A Deep Framework for Assessing Cyber-Human Aspects in Insider Threat Detection

Insider threat is one of the most damaging cyber attacks that could cause the loss of intellectual property and enterprise data security breaches. Action sequence data such as host logs are used to investigate such threats and develop anomaly-based AI detectors. However, insider threat actions are similar to legitimate user activities, causing AI detectors to fail and suffer from high false alarm rates. Therefore, user cyber activity logs are inadequate to fully unfold insider threats. In this study, we adopt human psychological principles of risk-taking and impulsiveness along with host data to assess the influence and usefulness of human behavioral aspects in insider threat detection. Here, we hypothesize that individuals' impulsive and risk-taking behavior correlates with cyberspace activities. To validate our hypothesis, we conducted an IRB-approved study recruiting 35 participants who work in a large U.S. university and collected their cyber and psychological data for 90 days. Host and human-behavioral data analysis and mapping indicate that impulsive and risk-taking users trigger more system errors causing (un)intentional insider threats and are susceptible to attackers' social engineering and cognitive hacking. Utilizing cyber-human aspects, we introduce a Cyber-Human Graph Neural Network (GNN) based framework GraphCH to identify abnormal user behaviors and detect insider threats.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1975

An investigation in the NASA MSFC 14-inch trisonic wind tunnel to determine the pressure distribution over the components of a 0.004 scale version of the Rockwell MCR 0074 baseline shuttle ascent configuration (IA32F), volume 1

Data were obtained for Mach numbers from 0.6 to 3.48, angles of attack from -10 to 10 degrees, and angles of sideslip from -10 to 10 degrees at zero angle of attack. Also, -4 and 4 degrees sideslip were run for an angle of attack of -5 and 5 degrees. Aerodynamic configurations of the solid rocket motors, external tank, and orbiter are shown. Graphs of plotted pressure data (pressure coefficients) for the external tank and solid rocket motors are given. A description of the test facility is included.

Ramsey, P. E.↗
NASA NTRS1980

Curved flow wind tunnnel test of F-18 aircraft

The curved flow capability of a stability wind tunnel was used to investigate the lateral directional characteristics of an F-18 aircraft. The model is described and the procedures used to obtain and correct the data and a graphical presentation of the results are presented. The results include graphs of lateral directional derivatives versus sideslip or static plots, the lateral directional static stability derivatives versus angle of attack, and finally the lateral directional derivatives versus nondimensional yaw rate for different angles of attack and sideslip. Results are presented for several configurations including complete, complete without vertical tails, complete without horizontal tails, fuselage wing and fuselage alone. Each of these were tested with and without wing leading edge extensions.

Lutze, F. H.↗
NASA NTRS1998

Tribometer for Lubrication Studies in Vacuum

The NASA Lewis Research Center has developed a new way to evaluate the liquid lubricants used in ball bearings in space mechanisms. For this evaluation, a liquid lubricant is exercised in the rolling contact vacuum tribometer shown in the photo. This tribometer, which is essentially a thrust bearing with three balls and flat races, has contact stresses similar to those in a typical preloaded, angular contact ball bearing. The rotating top plate drives the balls in an outward-winding spiral orbit instead of a circular path. Upon contact with the "guide plate," the balls are forced back to their initial smaller orbit radius; they then repeat this spiral orbit thousands of times. The orbit rate of the balls is low enough, 2 to 5 rpm, to allow the system to operate in the boundary lubrication regime that is most stressful to the liquid lubricant. This system can determine the friction coefficient, lubricant lifetime, and species evolved from the liquid lubricant by tribodegradation. The lifetime of the lubricant charge is only few micrograms, which is "used up" by degradation during rolling. The friction increases when the lubricant is exhausted. The species evolved by the degrading lubricant are determined by a quadrupole residual gas analyzer that directly views the rotating elements. The flat races (plates) and 0.5-in.-diameter balls are of a configuration and size that permit easy post-test examination by optical and electron microscopy and the full suite of modern surface and thin-film chemical analytical techniques, including infrared and Raman microspectroscopy and x-ray photoelectron spectroscopy. In addition, the simple sphere-on-a-flat-plate geometry allows an easy analysis of the contact stresses at all parts of the ball orbit and an understanding of the frictional energy losses to the lubricant. The analysis showed that when the ball contacts the guide plate, gross sliding occurs between the ball and rotating upper plate as the ball forced back to a smaller orbit radius. The friction force due to gross sliding is sensed by the piezoelectric force transducer behind the guide plate and furnishes the coefficient of friction for the system. This tribometer has been used to determine the relative lifetimes of Fomblin Z-25, a lubricant often used in space mechanisms, as a function of the material of the plates against which it was run. The balls were 440C steel in all cases; the plate materials were aluminum, chromium (Cr), 440C steel (17 wt % Cr), and 4150 steel (1 wt % Cr). As shown in the bar graph, the lifetime is greatest for the plate material with least chromium, thus implicating chromium as a tribochemically active element attacking Fomblin Z-25.

Pepper, Stephen V.↗
DOE OSTI2023

Transmission and Distribution Real-Time Analysis Software for Monitoring and Control: Design and Simulation Testing

The US electric grid is facing operational, stability, and security challenges. Transmission system operators need some measure of visibility into distribution system renewable generation. Distribution system generation needs to support transmission system voltage. The grid is experiencing an expansion in measurement systems. How to take full advantage of this expansion and defend against attacks, both cyber and physical, poses additional challenges. This paper introduces software designed to meet these challenges. At the center of the software is an Integrated System Model (ISM) that spans from transmission to secondary distribution. The ISM is employed in real-time abnormality detection, voltage stability forecasting, and multi-mode control. The software architecture along with selected analysis modules is presented. Testing results are presented for: 1—attacks on utility infrastructure; 2—energy savings from optimal control; 3—distribution system control response during a low voltage transmission system event; 4—cyber-attacks on PV inverters, where physical inverters are used in hardware-in-the-simulation-loop studies. Contributions of this work include real-time analysis that spans from three-phase transmission through secondary distribution; an approach for detecting abnormalities that employs measurements from three independent measurement systems; and a multi-mode distribution system control that responds to cyber-attacks, physical attacks, equipment failures, and transmission system needs.

14 SOLAR ENERGY↗
DOE OSTI2021

Directional Laplacian Centrality for Cyber Situational Awareness

Cyber operations is drowning in diverse, high-volume, multi-source data. To get a full picture of current operations and identify malicious events and actors, analysts must see through data generated by a mix of human activity and benign automated processes. Although many monitoring and alert systems exist, they typically use signature-based detection methods. We introduce a general method rooted in spectral graph theory to discover patterns and anomalies without a priori knowledge of signatures. We derive and propose a new graph-theoretic centrality measure based on the derivative of the graph Laplacian matrix in the direction of a vertex. To build intuition about our measure, we show how it identifies the most central vertices in standard network datasets and compare to other graph centrality measures. Finally, we focus our attention on studying its effectiveness in identifying important IP addresses in network flow data. Using both real and synthetic network flow data, we conduct several experiments to test our measure’s sensitivity to two types of injected attack profiles and show that vertices participating in injected attack profiles exhibit noticeable changes in our centrality measures, even when the injected anomalies are relatively small, and in the presence of simulated network dynamics.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

Modeling Cyber Supply Chain Incidents with Multilayered Graph Motifs

As noted within the literature, supply chain includes people and organizations---manufacturers, integrators, and third-party vendors---that are involved in one or more stages of a product lifecycle. Since supply chains, by definition, include organizations and people, supply chain risk management activities must consider dependencies between an organization's business processes and third-party resources. Just as adversarial tactics can be implemented via techniques implemented via networked computer systems, so can such tactics be expressed via legal business relationships. A cyber incident may have an exponential impact downstream, for example, by leveraging a product's distribution channel (e.g. malicious updates in SolarWinds, buggy updates in CrowdStrike). Similarly, legitimate and legal business relationships also affect the attack surface exposure of systems, enabling long-term persistence and/or unknown impacts to product quality that are hard to detect. This paper catalogs several recent digital supply chain incidents and applies a multilayered network formalism to develop structural indicators (graph motifs) that reflect potentially-adversarial behavior. Finally, we compare and contrast the characteristics of adversarial tactics (e.g. Loss of Availability, Data Collection) that leverage cyber-physical dependencies to those that leverage legal organizational relationships.

97 - MATHEMATICS AND COMPUTING↗
NASA NTRS1987

Supercomputing of supersonic flows using upwind relaxation and MacCormack schemes

The performance of two numerical solution schemes, (1) an implicit upwind relaxation with a finite-volume discretization (Thomas and Walters, 1985) and (2) an explicit-implicit MacCormack (1981) scheme with a finite-difference discretization, is compared in two-dimensional simulations of supersonic flow past a flat plate with leading edge, a rearward-facing step, a 10-deg compression corner, a NACA 0012 airfoil at high angle of attack, and a cavity. The algebraic turbulence model, the solution methods, and the boundary conditions and SIMD coding are explained, and the results are presented in tables and graphs and characterized with reference to published experimental data. Scheme (1) is found to converge more rapidly and to give more accurate results than (2) in a wide range of problem types.

Baysal, O.↗
DOE OSTI2022

Optimizing power system restoration with damaged communications

Utility procedures for power system blackstart and restoration typically assume that energization decisions can be reliably communicated across the grid. In reality, the communications and control network would likely also be affected in power outages, such as those caused by extreme weather events or cyber-attacks. This paper studies the effect of damage to the power system communications and control infrastructure on restoration operations following a blackout. We model the communications infrastructure as a graph, overlaying the power grid, and imposing the requirement that every energized element in the power grid be observable from a control center. We expand on a specialized branch-and-bound algorithm from the literature to optimize the restoration process and devise an initialization heuristic and a rounding heuristic to improve solution speed. We perform numerical experiments on synthetic systems for Illinois and Texas with outages based on a solar flare or hurricane. We compare the results of our specialized branch-and-bound algorithm to the results from (i) the initialization heuristic alone, (ii) a variation of this heuristic that we use as a baseline, and (iii) the restoration optimization for the power system without communications constraints. Here, we find that damage to the communications infrastructure significantly increases the time required to re-energize the grid. Moreover, by simultaneously optimizing communications repairs and grid energization decisions, we are able to re-energize the grid significantly faster than if communications repairs and energization decisions were made independently or with partial coordination, motivating improvements to current industry practice.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A distributed voltage inference framework for cyber-physical attacks detection and localization in active distribution grids

The transition to active distribution grids with real-time monitoring and control depends on the proliferation of advanced communication networks and devices. This paradigm shift towards a cyber-physical architecture also introduces new vulnerabilities for adversaries to exploit and launch sophisticated cyber-physical attacks targeting grid observability. Current research highlights the challenges in distinguishing attacks on voltage phasor or nodal injection measurements and isolating multi-source attack locations in a multiphase distribution grid. The attack detection and localization methods in literature face accuracy issues, applications across diverse attack scenarios, or scalability limits. Here, to bridge these gaps, this paper proposes a distributed Voltage Inference framework for real-time detection and localization of cyber-physical attacks, addressing scalability, adaptability, and accuracy challenges in state-of-the-art methods. The proposed methodology leverages the distributed nature of the Voltage Inference framework through a two-step process of prediction and correction, together with a tractable graph partitioning approach, providing a reliable solution to identify compromised measurement sources and facilitate isolation. Extensive testing on IEEE 13 and 123-node distribution feeders underscores the algorithm’s efficacy, enhancing the security and resilience of active distribution grids against evolving cyber threats. Additionally, Hardware-in-the-Loop (HIL) implementation validates the proposed strategy’s practical applicability in real-world scenarios.

active distribution grids↗