Engineering Commons^™

Machine Learning For Intrusion Detection Into Unmanned Aerial System 6g Networks, Faisal Alrefaei

Doctoral Dissertations and Master's Theses

Progress in the development of wireless network technology has played a crucial role in the evolution of societies and provided remarkable services over the past decades. It remotely offers the ability to execute critical missions and effective services that meet the user's needs. This advanced technology integrates cyber and physical layers to form cyber-physical systems (CPS), such as the Unmanned Aerial System (UAS), which consists of an Unmanned Aerial Vehicle (UAV), ground network infrastructure, communication link, etc. Furthermore, it plays a crucial role in connecting objects to create and develop the Internet of Things (IoT) technology. Therefore, the emergence of …

Artificial Intelligence-Assisted Inertial Geomagnetic Passive Navigation, Andrei Cuenca

Doctoral Dissertations and Master's Theses

In recent years, the integration of machine learning techniques into navigation systems has garnered significant interest due to their potential to improve estimation accuracy and system robustness. This doctoral dissertation investigates the use of Deep Learning combined with a Rao-Blackwellized Particle Filter for enhancing geomagnetic navigation in airborne simulated missions.

A simulation framework is developed to facilitate the evaluation of the proposed navigation system. This framework includes a detailed aircraft model, a mathematical representation of the Earth's magnetic field, and the incorporation of real-world magnetic field data obtained from online databases. The setup allows an accurate assessment of the performance …

Predicting Dynamic Fragmentation Characteristics From High-Impact Energy Events Utilizing Terrestrial Static Arena Test Data And Machine Learning, Katharine Larsen, Riccardo Bevilacqua, Omkar S. Mulekar, Elisabetta L. Jerome, Thomas J. Hatch-Aguilar

Student Works

To continue space operations with the increasing space debris, accurate characterization of fragment fly-out properties from hypervelocity impacts is essential. However, with limited realistic experimentation and the need for data, available static arena test data, collected utilizing a novel stereoscopic imaging technique, is the primary dataset for this paper. This research leverages machine learning methodologies to predict fragmentation characteristics using combined data from this imaging technique and simulations, produced considering dynamic impact conditions. Gaussian mixture models (GMMs), fit via expectation maximization (EM), are used to model fragment track intersections on a defined surface of intersection. After modeling the fragment distributions, …

Neural Network Models For Generating Synthetic Flight Data, Nathaniel Sisson

Doctoral Dissertations and Master's Theses

Flight test data is a valuable resource used in many aerospace applications. However, procuring a sufficiently large database of flight test data poses several challenges. Nominal flight tests can be expensive and time-consuming and require much post-processing depending on the availability of sensors and the quality of the sensor output. Flight test performed outside of nominal flight conditions, or flight tests in which failures are introduced, add to the inherent risk and danger associated with flight tests. The most popular alternative to flight test, numerical simulations, may fail to fully capture all non-linear behavior. While flight tests will always be …

Machine Learning To Predict Warhead Fragmentation In-Flight Behavior From Static Data, Katharine Larsen

Doctoral Dissertations and Master's Theses

Accurate characterization of fragment fly-out properties from high-speed warhead detonations is essential for estimation of collateral damage and lethality for a given weapon. Real warhead dynamic detonation tests are rare, costly, and often unrealizable with current technology, leaving fragmentation experiments limited to static arena tests and numerical simulations. Stereoscopic imaging techniques can now provide static arena tests with time-dependent tracks of individual fragments, each with characteristics such as fragment IDs and their respective position vector. Simulation methods can account for the dynamic case but can exclude relevant dynamics experienced in real-life warhead detonations. This research leverages machine learning methodologies to …

Supporting The Discovery, Reuse, And Validation Of Cybersecurity Requirements At The Early Stages Of The Software Development Lifecycle, Jessica Antonia Steinmann

Doctoral Dissertations and Master's Theses

The focus of this research is to develop an approach that enhances the elicitation and specification of reusable cybersecurity requirements. Cybersecurity has become a global concern as cyber-attacks are projected to cost damages totaling more than $10.5 trillion dollars by 2025. Cybersecurity requirements are more challenging to elicit than other requirements because they are nonfunctional requirements that requires cybersecurity expertise and knowledge of the proposed system. The goal of this research is to generate cybersecurity requirements based on knowledge acquired from requirements elicitation and analysis activities, to provide cybersecurity specifications without requiring the specialized knowledge of a cybersecurity expert, and …

Computational Models To Detect Radiation In Urban Environments: An Application Of Signal Processing Techniques And Neural Networks To Radiation Data Analysis, Jose Nicolas Gachancipa

Beyond: Undergraduate Research Journal

Radioactive sources, such as uranium-235, are nuclides that emit ionizing radiation, and which can be used to build nuclear weapons. In public areas, the presence of a radioactive nuclide can present a risk to the population, and therefore, it is imperative that threats are identified by radiological search and response teams in a timely and effective manner. In urban environments, such as densely populated cities, radioactive sources may be more difficult to detect, since background radiation produced by surrounding objects and structures (e.g., buildings, cars) can hinder the effective detection of unnatural radioactive material. This article presents a computational model …

Air Passenger Demand Forecast Through The Use Of Artificial Neural Network Algorithms, Juan Gerardo Muros Anguita, Oscar Díaz Olariaga

International Journal of Aviation, Aeronautics, and Aerospace

Airport planning depends to a large extent on the levels of activity that are anticipated. To plan the facilities and infrastructures of an airport system and to be able to satisfy future needs, it is essential to predict the level and distribution of demand. This document presents a short- and medium-term forecast of the demand for air passengers carried out through a specific case study (Colombia), in which the impact of the pandemic period due to COVID-19 on air traffic was taken into account. To make the forecast, an algorithm that implements techniques based on Artificial Neural Networks (ANN) (Machine …

Classifıcation Of Survivor/Non-Survivor Passengers In Fatal Aviation Accidents: A Machine Learning Approach, Tüzün Tolga İnan Dr.

International Journal of Aviation, Aeronautics, and Aerospace

The safety concept primarily examines the most fatal (resulting in dead passengers) accidents of aviation history in this study. The primary causes of most fatal accidents are; human, technical, and sabotage/terrorism factors. Although the aviation industry started with the first engine flight in 1903, the safety concept has been examined since the 1950s. The safety concept firstly examined the technical factors, and in the late 1970s, human factors started to analyze. Despite these primary causes, there have different factors that affect accidents. So, the study aims to determine the affecting factors of the most fatal accidents to classify the survivor/non-survivor …

Air Passenger Demand Forecast Through The Use Of Artificial Neural Network Algorithms, Juan Gerardo Muros Anguita, Oscar Díaz Olariaga

International Journal of Aviation, Aeronautics, and Aerospace

Airport planning depends to a large extent on the levels of activity that are anticipated. In order to plan facilities and infrastructures of an airport system and to be able to satisfy future needs, it is essential to predict the level and distribution of demand. This document presents a short- and medium-term forecast of the demand for air passengers carried out through a specific case study (Colombia), in which the impact of the pandemic period due to COVID-19 on air traffic was taken into account. To make the forecast, an algorithm that implements techniques based on Artificial Neural Networks (ANN) …

Machine Learning For Unmanned Aerial System (Uas) Networking, Jian Wang

Doctoral Dissertations and Master's Theses

Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex …

Rf Fingerprinting Unmanned Aerial Vehicles, Norah Ondus

Doctoral Dissertations and Master's Theses

As unmanned aerial vehicles (UAVs) continue to become more readily available, their use in civil, military, and commercial applications is growing significantly. From aerial surveillance to search-and-rescue to package delivery the use cases of UAVs are accelerating. This accelerating popularity gives rise to numerous attack possibilities for example impersonation attacks in drone-based delivery, in a UAV swarm, etc. In order to ensure drone security, in this project we propose an authentication system based on RF fingerprinting. Specifically, we extract and use the device-specific hardware impairments embedded in the transmitted RF signal to separate the identity of each UAV. To achieve …

Learning To Detect: A Data-Driven Approach For Network Intrusion Detection, Zachary Tauscher, Yushan Jiang, Kai Zhang, Jian Wang, Houbing Song

Publications

With massive data being generated daily and the ever-increasing interconnectivity of the world’s Internet infrastructures, a machine learning based intrusion detection system (IDS) has become a vital component to protect our economic and national security. In this paper, we perform a comprehensive study on NSL-KDD, a network traffic dataset, by visualizing patterns and employing different learning-based models to detect cyber attacks. Unlike previous shallow learning and deep learning models that use the single learning model approach for intrusion detection, we adopt a hierarchy strategy, in which the intrusion and normal behavior are classified firstly, and then the specific types of …

Implementing Artificial Intelligence And Machine Learning Into Advanced Qualification Programs, Jennifer R. Herr

Journal of Aviation/Aerospace Education & Research

Since its start, the Advanced Qualification Program (AQP) has encouraged new and innovative strategies for training airline crewmembers. The foundation of AQP is to train crew the way they fly and to find new and innovative ways to increase safety through training. By using data collected through the AQP process, training methods can be refined and improved. New technologies, such as artificial intelligence (AI) and machine learning can make data analysis and training more effective and efficient. This paper will explore these concepts and how AI and machine learning could be implemented in the AQP process to make training more …

Predictability Improvement Of Scheduled Flights Departure Time Variation Using Supervised Machine Learning, Deepudev Sahadevan, Palanisamy Ponnusamy Dr, Manjunath K. Nelli Mr, Varun P. Gopi Dr

International Journal of Aviation, Aeronautics, and Aerospace

The departure time uncertainty exacerbates the inaccuracy of arrival time estimation and demand for arrival slots, particularly for movements to capacity constrained airports. The Estimated Take-Off Time (ETOT) or Estimated Departure Time(ETD) for each individual flight is currently derived from Air Traffic Flow Management System (ATFMS), which are solely determined based on individual flight plan Estimated Off Block Time(EOBT) or subsequent delays updated by Airline. Even if normal weather conditions prevail, aircraft departure times will differ from ETOTs determined by the ATFMS due to a number of factors such as congestion, early/delayed inbound flight (linked flights), reactionary delays and air …

Automatic Gaze Classification For Aviators: Using Multi-Task Convolutional Networks As A Proxy For Flight Instructor Observation, Justin Wilson, Sandro Scielzo, Sukumaran Nair, Eric C. Larson

International Journal of Aviation, Aeronautics, and Aerospace

In this work, we investigate how flight instructors observe aviator scan patterns and assign quality to an aviator's gaze. We first establish the reliability of instructors to assign similar quality to an aviator's scan patterns, and then investigate methods to automate this quality using machine learning. In particular, we focus on the classification of gaze for aviators in a mixed-reality flight simulation. We create and evaluate two machine learning models for classifying gaze quality of aviators: a task-agnostic model and a multi-task model. Both models use deep convolutional neural networks to classify the quality of pilot gaze patterns for 40 …