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Articles 1 - 30 of 146
Full-Text Articles in Physical Sciences and Mathematics
Exploring Quaternion Neural Network Loss Surfaces, Jeremiah Bill, Bruce A. Cox
Exploring Quaternion Neural Network Loss Surfaces, Jeremiah Bill, Bruce A. Cox
Faculty Publications
This paper explores the superior performance of quaternion multi-layer perceptron (QMLP) neural networks over real-valued multi-layer perceptron (MLP) neural networks, a phenomenon that has been empirically observed but not thoroughly investigated. The study utilizes loss surface visualization and projection techniques to examine quaternion-based optimization loss surfaces for the first time. The primary contribution of this research is the statistical evidence that QMLP models yield smoother loss surfaces than real-valued neural networks, which are measured and compared using a robust quantitative measure of loss surface “goodness” based on estimates of surface curvature. Extensive computational testing validates the effectiveness of these surface …
Data Supporting Research On Personalized Learning Paths, Sean Mochocki, Mark Reith
Data Supporting Research On Personalized Learning Paths, Sean Mochocki, Mark Reith
Faculty Publications
Personalized Learning Paths (PLPs) are a key application of Artificial Intelligence in E-Learning. In contrast to regular Learning Paths, they return a unique sequence of learning materials identified as meeting the individual needs of the students. In the literature, PLPs are often created from knowledge graphs, which assist with ordering topics and their associated learning materials. Knowledge graphs are typically directed and acyclic, to capture prerequisite relationships between topics, though they can also have bidirectional edges when these prerequisite relationships are not necessary. This data package provides a primarily un-directed knowledge graph, with associated repository of open-source learning materials that …
The Impact Of Data Preparation And Model Complexity On The Natural Language Classification Of Chinese News Headlines, Torrey J. Wagner, Dennis Guhl, Brent T. Langhals
The Impact Of Data Preparation And Model Complexity On The Natural Language Classification Of Chinese News Headlines, Torrey J. Wagner, Dennis Guhl, Brent T. Langhals
Faculty Publications
Given the emergence of China as a political and economic power in the 21st century, there is increased interest in analyzing Chinese news articles to better understand developing trends in China. Because of the volume of the material, automating the categorization of Chinese-language news articles by headline text or titles can be an effective way to sort the articles into categories for efficient review. A 383,000-headline dataset labeled with 15 categories from the Toutiao website was evaluated via natural language processing to predict topic categories. The influence of six data preparation variations on the predictive accuracy of four algorithms was …
Relative Vectoring Using Dual Object Detection For Autonomous Aerial Refueling, Derek B. Worth, Jeffrey L. Choate, James Lynch, Scott L. Nykl, Clark N. Taylor
Relative Vectoring Using Dual Object Detection For Autonomous Aerial Refueling, Derek B. Worth, Jeffrey L. Choate, James Lynch, Scott L. Nykl, Clark N. Taylor
Faculty Publications
Once realized, autonomous aerial refueling will revolutionize unmanned aviation by removing current range and endurance limitations. Previous attempts at establishing vision-based solutions have come close but rely heavily on near perfect extrinsic camera calibrations that often change midflight. In this paper, we propose dual object detection, a technique that overcomes such requirement by transforming aerial refueling imagery directly into receiver aircraft reference frame probe-to-drogue vectors regardless of camera position and orientation. These vectors are precisely what autonomous agents need to successfully maneuver the tanker and receiver aircraft in synchronous flight during refueling operations. Our method follows a common 4-stage process …
Gnss Software Defined Radio: History, Current Developments, And Standardization Efforts, Thomas Pany, Dennis Akos, Javier Arribas, M. Zahidul H. Bhuiyan, Pau Closas, Fabio Dovis, Ignacio Fernandez-Hernandez, Carles Fernandez-Prades, Sanjeev Gunawardena, Todd Humphreys, Zaher M. Kassas, Jose A. Lopez Salcedo, Mario Nicola, Mario L. Psiaki, Alexander Rugamer, Yong-Jin Song, Jong-Hoon Won
Gnss Software Defined Radio: History, Current Developments, And Standardization Efforts, Thomas Pany, Dennis Akos, Javier Arribas, M. Zahidul H. Bhuiyan, Pau Closas, Fabio Dovis, Ignacio Fernandez-Hernandez, Carles Fernandez-Prades, Sanjeev Gunawardena, Todd Humphreys, Zaher M. Kassas, Jose A. Lopez Salcedo, Mario Nicola, Mario L. Psiaki, Alexander Rugamer, Yong-Jin Song, Jong-Hoon Won
Faculty Publications
Taking the work conducted by the global navigation satellite system (GNSS) software-defined radio (SDR) working group during the last decade as a seed, this contribution summarizes, for the first time, the history of GNSS SDR development. This report highlights selected SDR implementations and achievements that are available to the public or that influenced the general development of SDR. Aspects related to the standardization process of intermediate-frequency sample data and metadata are discussed, and an update of the Institute of Navigation SDR Standard is proposed. This work focuses on GNSS SDR implementations in general-purpose processors and leaves aside developments conducted on …
An Analysis Of Precision: Occlusion And Perspective Geometry’S Role In 6d Pose Estimation, Jeffrey Choate, Derek Worth, Scott Nykl, Clark N. Taylor, Brett J. Borghetti, Christine M. Schubert Kabban
An Analysis Of Precision: Occlusion And Perspective Geometry’S Role In 6d Pose Estimation, Jeffrey Choate, Derek Worth, Scott Nykl, Clark N. Taylor, Brett J. Borghetti, Christine M. Schubert Kabban
Faculty Publications
Achieving precise 6 degrees of freedom (6D) pose estimation of rigid objects from color images is a critical challenge with wide-ranging applications in robotics and close-contact aircraft operations. This study investigates key techniques in the application of YOLOv5 object detection convolutional neural network (CNN) for 6D pose localization of aircraft using only color imagery. Traditional object detection labeling methods suffer from inaccuracies due to perspective geometry and being limited to visible key points. This research demonstrates that with precise labeling, a CNN can predict object features with near-pixel accuracy, effectively learning the distinct appearance of the object due to perspective …
Garbage In ≠ Garbage Out: Exploring Gan Resilience To Image Training Set Degradations, Nicholas Crino, Bruce A. Cox, Nathan B. Gaw
Garbage In ≠ Garbage Out: Exploring Gan Resilience To Image Training Set Degradations, Nicholas Crino, Bruce A. Cox, Nathan B. Gaw
Faculty Publications
Generative Adversarial Networks (GANs) have received immense attention in recent years due to their ability to capture complex, high-dimensional data distributions without the need for extensive labeling. Since their conception in 2014, a wide array of GAN variants have been proposed featuring alternative architectures, optimizers, and loss functions with the goal of improving performance and training stability. This manuscript focuses on quantifying the resilience of a GAN architecture to specific modes of image degradation. We conduct systematic experimentation to empirically determine the effects of 10 fundamental image degradation modes, applied to the training image dataset, on the Fréchet inception distance …
Passive Physical Layer Distinct Native Attribute Cyber Security Monitor, Christopher M. Rondeau, Michael A. Temple, Juan Lopez Jr, J. Addison Betances
Passive Physical Layer Distinct Native Attribute Cyber Security Monitor, Christopher M. Rondeau, Michael A. Temple, Juan Lopez Jr, J. Addison Betances
AFIT Patents
A method for cyber security monitor includes monitoring a network interface that is input-only configured to surreptitiously and covertly receive bit-level, physical layer communication between networked control and sensor field devices. During a training mode, a baseline distinct native attribute (DNA) fingerprint is generated for each networked field device. During a protection mode, a current DNA fingerprint is generated for each networked field device. The current DNA fingerprint is compared to the baseline DNA fingerprint for each networked field device. In response to detect at least one of RAA and PAA based on a change in the current DNA fingerprint …
Analysis And Requirement Generation For Defense Intelligence Search: Addressing Data Overload Through Human–Ai Agent System Design For Ambient Awareness, Mark C. Duncan, Michael E. Miller, Brett J. Borghetti
Analysis And Requirement Generation For Defense Intelligence Search: Addressing Data Overload Through Human–Ai Agent System Design For Ambient Awareness, Mark C. Duncan, Michael E. Miller, Brett J. Borghetti
Faculty Publications
This research addresses the data overload faced by intelligence searchers in government and defense agencies. The study leverages methods from the Cognitive Systems Engineering (CSE) literature to generate insights into the intelligence search work domain. These insights are applied to a supporting concept and requirements for designing and evaluating a human-AI agent team specifically for intelligence search tasks. Domain analysis reveals the dynamic nature of the ‘value structure’, a term that describes the evolving set of criteria governing the intelligence search process. Additionally, domain insight provides details for search aggregation and conceptual spaces from which the value structure could be …
Ironnetinjector: Weaponizing .Net Dynamic Language Runtime Engines, Anthony J. Rose, Scott R. Graham, Jacob Krasnov
Ironnetinjector: Weaponizing .Net Dynamic Language Runtime Engines, Anthony J. Rose, Scott R. Graham, Jacob Krasnov
Faculty Publications
As adversaries evolve their Tactics, Techniques, and Procedures (TTPs) to stay ahead of defenders, Microsoft’s .NET Framework emerges as a common component found in the tradecraft of many contemporary Advanced Persistent Threats (APTs), whether through PowerShell or C#. Because of .NET’s ease of use and availability on every recent Windows system, it is at the forefront of modern TTPs and is a primary means of exploitation. This article considers the .NET Dynamic Language Runtime as an attack vector, and how APTs have utilized it for offensive purposes. The technique under scrutiny is Bring Your Own Interpreter (BYOI), which is the …
Hyperspectral Point Cloud Projection For The Semantic Segmentation Of Multimodal Hyperspectral And Lidar Data With Point Convolution-Based Deep Fusion Neural Networks, Kevin T. Decker, Brett J. Borghetti
Hyperspectral Point Cloud Projection For The Semantic Segmentation Of Multimodal Hyperspectral And Lidar Data With Point Convolution-Based Deep Fusion Neural Networks, Kevin T. Decker, Brett J. Borghetti
Faculty Publications
The fusion of dissimilar data modalities in neural networks presents a significant challenge, particularly in the case of multimodal hyperspectral and lidar data. Hyperspectral data, typically represented as images with potentially hundreds of bands, provide a wealth of spectral information, while lidar data, commonly represented as point clouds with millions of unordered points in 3D space, offer structural information. The complementary nature of these data types presents a unique challenge due to their fundamentally different representations requiring distinct processing methods. In this work, we introduce an alternative hyperspectral data representation in the form of a hyperspectral point cloud (HSPC), which …
The Characteristics Of Successful Military It Projects: A Cross-Country Empirical Study, Helene Berg, Jonathan D. Ritschel
The Characteristics Of Successful Military It Projects: A Cross-Country Empirical Study, Helene Berg, Jonathan D. Ritschel
Faculty Publications
No abstract provided.
Numerical Simulation Of The Korteweg–De Vries Equation With Machine Learning, Kristina O. F. Williams *, Benjamin F. Akers
Numerical Simulation Of The Korteweg–De Vries Equation With Machine Learning, Kristina O. F. Williams *, Benjamin F. Akers
Faculty Publications
A machine learning procedure is proposed to create numerical schemes for solutions of nonlinear wave equations on coarse grids. This method trains stencil weights of a discretization of the equation, with the truncation error of the scheme as the objective function for training. The method uses centered finite differences to initialize the optimization routine and a second-order implicit-explicit time solver as a framework. Symmetry conditions are enforced on the learned operator to ensure a stable method. The procedure is applied to the Korteweg–de Vries equation. It is observed to be more accurate than finite difference or spectral methods on coarse …
Toward A Simulation Model Complexity Measure, J. Scott Thompson, Douglas D. Hodson, Michael R. Grimaila, Nicholas Hanlon, Richard Dill
Toward A Simulation Model Complexity Measure, J. Scott Thompson, Douglas D. Hodson, Michael R. Grimaila, Nicholas Hanlon, Richard Dill
Faculty Publications
Is it possible to develop a meaningful measure for the complexity of a simulation model? Algorithmic information theory provides concepts that have been applied in other areas of research for the practical measurement of object complexity. This article offers an overview of the complexity from a variety of perspectives and provides a body of knowledge with respect to the complexity of simulation models. The key terms model detail, resolution, and scope are defined. An important concept from algorithmic information theory, Kolmogorov complexity, and an application of this concept, normalized compression distance, are used to indicate the possibility of measuring changes …
Emotion Classification Of Indonesian Tweets Using Bidirectional Lstm, Aaron K. Glenn, Phillip M. Lacasse, Bruce A. Cox
Emotion Classification Of Indonesian Tweets Using Bidirectional Lstm, Aaron K. Glenn, Phillip M. Lacasse, Bruce A. Cox
Faculty Publications
Emotion classification can be a powerful tool to derive narratives from social media data. Traditional machine learning models that perform emotion classification on Indonesian Twitter data exist but rely on closed-source features. Recurrent neural networks can meet or exceed the performance of state-of-the-art traditional machine learning techniques using exclusively open-source data and models. Specifically, these results show that recurrent neural network variants can produce more than an 8% gain in accuracy in comparison with logistic regression and SVM techniques and a 15% gain over random forest when using FastText embeddings. This research found a statistical significance in the performance of …
Data Augmentation For Neutron Spectrum Unfolding With Neural Networks, James Mcgreivy, Juan J. Manfredi, Daniel Siefman
Data Augmentation For Neutron Spectrum Unfolding With Neural Networks, James Mcgreivy, Juan J. Manfredi, Daniel Siefman
Faculty Publications
Neural networks require a large quantity of training spectra and detector responses in order to learn to solve the inverse problem of neutron spectrum unfolding. In addition, due to the under-determined nature of unfolding, non-physical spectra which would not be encountered in usage should not be included in the training set. While physically realistic training spectra are commonly determined experimentally or generated through Monte Carlo simulation, this can become prohibitively expensive when considering the quantity of spectra needed to effectively train an unfolding network. In this paper, we present three algorithms for the generation of large quantities of realistic and …
Accelerating A Software Defined Satnav Receiver Using Multiple Parallel Processing Schemes, Logan Reich, Sanjeev Gunawardena, Michael Braasch
Accelerating A Software Defined Satnav Receiver Using Multiple Parallel Processing Schemes, Logan Reich, Sanjeev Gunawardena, Michael Braasch
Faculty Publications
Excerpt: Satnav SDRs present many benefits in terms of flexibility and configurability. However, due to the high bandwidth signals involved in satnav SDR processing, the software must be highly optimized for the host platform in order to achieve acceptable runtimes. Modules such as sample decoding, carrier replica generation, carrier wipeoff, and correlation are computationally intensive components that benefit from accelerations.
Generating Realistic Cyber Data For Training And Evaluating Machine Learning Classifiers For Network Intrusion Detection Systems, Marc W. Chalé, Nathaniel D. Bastian
Generating Realistic Cyber Data For Training And Evaluating Machine Learning Classifiers For Network Intrusion Detection Systems, Marc W. Chalé, Nathaniel D. Bastian
Faculty Publications
No abstract provided.
Quantifying Dds-Cerberus Network Control Overhead, Andrew T. Park, Nathaniel R. Peck, Richard Dill, Douglas D. Hodson, Michael R. Grimaila, Wayne C. Henry
Quantifying Dds-Cerberus Network Control Overhead, Andrew T. Park, Nathaniel R. Peck, Richard Dill, Douglas D. Hodson, Michael R. Grimaila, Wayne C. Henry
Faculty Publications
Securing distributed device communication is critical because the private industry and the military depend on these resources. One area that adversaries target is the middleware, which is the medium that connects different systems. This paper evaluates a novel security layer, DDS-Cerberus (DDS-C), that protects in-transit data and improves communication efficiency on data-first distribution systems. This research contributes a distributed robotics operating system testbed and designs a multifactorial performance-based experiment to evaluate DDS-C efficiency and security by assessing total packet traffic generated in a robotics network. The performance experiment follows a 2:1 publisher to subscriber node ratio, varying the number of …
Distribution Of Dds-Cerberus Authenticated Facial Recognition Streams, Andrew T. Park, Nathaniel Peck, Richard Dill, Douglas D. Hodson, Michael R. Grimaila, Wayne C. Henry
Distribution Of Dds-Cerberus Authenticated Facial Recognition Streams, Andrew T. Park, Nathaniel Peck, Richard Dill, Douglas D. Hodson, Michael R. Grimaila, Wayne C. Henry
Faculty Publications
Successful missions in the field often rely upon communication technologies for tactics and coordination. One middleware used in securing these communication channels is Data Distribution Service (DDS) which employs a publish-subscribe model. However, researchers have found several security vulnerabilities in DDS implementations. DDS-Cerberus (DDS-C) is a security layer implemented into DDS to mitigate impersonation attacks using Kerberos authentication and ticketing. Even with the addition of DDS-C, the real-time message sending of DDS also needs to be upheld. This paper extends our previous work to analyze DDS-C’s impact on performance in a use case implementation. The use case covers an artificial …
Artificial Neural Networks And Gradient Boosted Machines Used For Regression To Evaluate Gasification Processes: A Review, Owen Sedej, Eric Mbonimpa, Trevor Sleight, Jeremy M. Slagley
Artificial Neural Networks And Gradient Boosted Machines Used For Regression To Evaluate Gasification Processes: A Review, Owen Sedej, Eric Mbonimpa, Trevor Sleight, Jeremy M. Slagley
Faculty Publications
Waste-to-Energy technologies have the potential to dramatically improve both the natural and human environment. One type of waste-to-energy technology that has been successful is gasification. There are numerous types of gasification processes and in order to drive understanding and the optimization of these systems, traditional approaches like computational fluid dynamics software have been utilized to model these systems. The modern advent of machine learning models has allowed for accurate and computationally efficient predictions for gasification systems that are informed by numerous experimental and numerical solutions. Two types of machine learning models that have been widely used to solve for quantitative …
Active 2d-Dna Fingerprinting Of Wirelesshart Adapters To Ensure Operational Integrity In Industrial Systems, Willie H. Mims, Michael A. Temple, Robert F. Mills
Active 2d-Dna Fingerprinting Of Wirelesshart Adapters To Ensure Operational Integrity In Industrial Systems, Willie H. Mims, Michael A. Temple, Robert F. Mills
Faculty Publications
The need for reliable communications in industrial systems becomes more evident as industries strive to increase reliance on automation. This trend has sustained the adoption of WirelessHART communications as a key enabling technology and its operational integrity must be ensured. This paper focuses on demonstrating pre-deployment counterfeit detection using active 2D Distinct Native Attribute (2D-DNA) fingerprinting. Counterfeit detection is demonstrated using experimentally collected signals from eight commercial WirelessHART adapters. Adapter fingerprints are used to train 56 Multiple Discriminant Analysis (MDA) models with each representing five authentic network devices. The three non-modeled devices are introduced as counterfeits and a total of …
A Unified View Of A Human Digital Twin, Michael Miller, Emily Spatz
A Unified View Of A Human Digital Twin, Michael Miller, Emily Spatz
Faculty Publications
The term human digital twin has recently been applied in many domains, including medical and manufacturing. This term extends the digital twin concept, which has been illustrated to provide enhanced system performance as it combines system models and analyses with real-time measurements for an individual system to improve system maintenance. Human digital twins have the potential to change the practice of human system integration as these systems employ real-time sensing and feedback to tightly couple measurements of human performance, behavior, and environmental influences throughout a product’s life cycle to human models to improve system design and performance. However, as this …
Automated Computer Network Exploitation With Bayesian Decision Networks, Graeme Roberts, Gilbert L. Peterson
Automated Computer Network Exploitation With Bayesian Decision Networks, Graeme Roberts, Gilbert L. Peterson
Faculty Publications
Penetration Testing (pentesting) is the process of using tactics and techniques to penetrate computer systems and networks to expose any issues in their cybersecurity \cite{rsa}. It is currently a manual process requiring significant experience and time that are in limited supply. One way to supplement the shortage is through automation. This paper presents the Automated Network Discovery and Exploitation System (ANDES) which demonstrates that it is feasible to automate the pentesting process. The uniqueness of ANDES is the use of Bayesian decision networks to represent the pentesting domain and subject matter expert knowledge. ANDES conducts multiple execution cycles, which build …
Factored Beliefs For Machine Agents In Decentralized Partially Observable Markov Decision Processes, Joshua Lapso, Gilbert L. Peterson
Factored Beliefs For Machine Agents In Decentralized Partially Observable Markov Decision Processes, Joshua Lapso, Gilbert L. Peterson
Faculty Publications
A shared mental model (SMM) is a foundational structure in high performing, task-oriented teams and aid humans in determining their teammate's goals and intentions. Higher levels of mental alignment between teammates can reduce the direct dialogue required for team success. For decision-making teams, a transactive memory system (TMS) offers team members a map of specialized knowledge, indicating source of knowledge and the source's credibility. SMM and TMS formulations aid human-agent team performance in their intended team types. However, neither improve team performance with a project team--one that requires both behavioral and knowledge integration. We present a hybrid cognitive model (HCM) …
Evolution Of Combined Arms Tactics In Heterogeneous Multi-Agent Teams, Robert J. Wilson, David W. King, Gilbert L. Peterson
Evolution Of Combined Arms Tactics In Heterogeneous Multi-Agent Teams, Robert J. Wilson, David W. King, Gilbert L. Peterson
Faculty Publications
Multi-agent systems research is concerned with the emergence of system-level behaviors from relatively simple agent interactions. Multi-agent systems research to date is primarily concerned with systems of homogeneous agents, with member agents both physically and behaviorally identical. Systems of heterogeneous agents with differing physical or behavioral characteristics may be able to accomplish tasks more efficiently than homogeneous teams, via cooperation between mutually complementary agent types. In this article, we compare the performance of homogeneous and heterogeneous teams in combined arms situations. Combined arms theory proposes that the application of heterogeneous forces, en masse, can generate effects far greater than outcomes …
Considerations For Radio Frequency Fingerprinting Across Multiple Frequency Channels, Jose A. Gutierrez Del Arroyo, Brett J. Borghetti, Michael A. Temple
Considerations For Radio Frequency Fingerprinting Across Multiple Frequency Channels, Jose A. Gutierrez Del Arroyo, Brett J. Borghetti, Michael A. Temple
Faculty Publications
Radio Frequency Fingerprinting (RFF) is often proposed as an authentication mechanism for wireless device security, but application of existing techniques in multi-channel scenarios is limited because prior models were created and evaluated using bursts from a single frequency channel without considering the effects of multi-channel operation. Our research evaluated the multi-channel performance of four single-channel models with increasing complexity, to include a simple discriminant analysis model and three neural networks. Performance characterization using the multi-class Matthews Correlation Coefficient (MCC) revealed that using frequency channels other than those used to train the models can lead to a deterioration in performance from …
Delaunay Walk For Fast Nearest Neighbor: Accelerating Correspondence Matching For Icp, James D. Anderson, Ryan M. Raettig, Joshua Larson, Clark N. Taylor, Thomas Wischgoll
Delaunay Walk For Fast Nearest Neighbor: Accelerating Correspondence Matching For Icp, James D. Anderson, Ryan M. Raettig, Joshua Larson, Clark N. Taylor, Thomas Wischgoll
Faculty Publications
Point set registration algorithms such as Iterative Closest Point (ICP) are commonly utilized in time-constrained environments like robotics. Finding the nearest neighbor of a point in a reference 3D point set is a common operation in ICP and frequently consumes at least 90% of the computation time. We introduce a novel approach to performing the distance-based nearest neighbor step based on Delaunay triangulation. This greedy algorithm finds the nearest neighbor of a query point by traversing the edges of the Delaunay triangulation created from a reference 3D point set. Our work integrates the Delaunay traversal into the correspondences search of …
Robust Error Estimation Based On Factor-Graph Models For Non-Line-Of-Sight Localization, O. Arda Vanli, Clark N. Taylor
Robust Error Estimation Based On Factor-Graph Models For Non-Line-Of-Sight Localization, O. Arda Vanli, Clark N. Taylor
Faculty Publications
This paper presents a method to estimate the covariances of the inputs in a factor-graph formulation for localization under non-line-of-sight conditions. A general solution based on covariance estimation and M-estimators in linear regression problems, is presented that is shown to give unbiased estimators of multiple variances and are robust against outliers. An iteratively re-weighted least squares algorithm is proposed to jointly compute the proposed variance estimators and the state estimates for the nonlinear factor graph optimization. The efficacy of the method is illustrated in a simulation study using a robot localization problem under various process and measurement models and measurement …
Effect Of Connection State & Transport/Application Protocol On The Machine Learning Outlier Detection Of Network Intrusions, George Yuchi [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals
Effect Of Connection State & Transport/Application Protocol On The Machine Learning Outlier Detection Of Network Intrusions, George Yuchi [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals
Faculty Publications
The majority of cyber infiltration & exfiltration intrusions leave a network footprint, and due to the multi-faceted nature of detecting network intrusions, it is often difficult to detect. In this work a Zeek-processed PCAP dataset containing the metadata of 36,667 network packets was modeled with several machine learning algorithms to classify normal vs. anomalous network activity. Principal component analysis with a 10% contamination factor was used to identify anomalous behavior. Models were created using recursive feature elimination on logistic regression and XGBClassifier algorithms, and also using Bayesian and bandit optimization of neural network hyperparameters. These models were trained on a …