Computer Sciences Commons^™

Improving Adversarial Attacks Against Malconv, Justin Burr

Masters Theses & Doctoral Dissertations

This dissertation proposes several improvements to existing adversarial attacks against MalConv, a raw-byte malware classifier for Windows PE files. The included contributions greatly improve the success rates and performance of gradient-based file overlay attacks. All improvements are included in a new open-source attack utility called BitCamo.

Several new payload initialization strategies for use with gradient-based attacks are proposed and evaluated as potential replacements for the randomized initialization method used by current attacks. An algorithm for determining the optimal payload size is also proposed. The resulting improvements achieve a 100% evasion rate against eligible target executables using an average payload size …

Multi-Dimensional Security Integrity Analysis Of Broad Market Internet-Connected Cameras, Mark A. Stanislav

Masters Theses & Doctoral Dissertations

This study used a quantitative approach with a cross-sectional, descriptive analysis survey design to examine the adherence of 40 internet-connected cameras against three IoT security frameworks to determine their overall security posture. Relevant literature was reviewed showing that prior studies in a similar regard had limitations, such as a small sample population, singular market segment focus, and/or a lack of validation against formalized frameworks. This study resulted in a uniform and multi-dimensional set of findings with supporting evidence, leading to a mapping against selected IoT security frameworks that was then quantitatively analyzed for their relative adherence as individual cameras, across …

Viral Pneumonia Screening On Chest X-Rays Using Confidence-Aware Anomaly Detection, Jianpeng Zhang, Yutong Xie, Guansong Pang, Zhibin Liao, Johan Verjans, Wenxing Li, Zongji Sun, Jian He, Yi Li, Chunhua Shen, Yong Xia

Research Collection School Of Computing and Information Systems

Clusters of viral pneumonia occurrences over a short period may be a harbinger of an outbreak or pandemic. Rapid and accurate detection of viral pneumonia using chest X-rays can be of significant value for large-scale screening and epidemic prevention, particularly when other more sophisticated imaging modalities are not readily accessible. However, the emergence of novel mutated viruses causes a substantial dataset shift, which can greatly limit the performance of classification-based approaches. In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into a one-class classification-based anomaly detection problem. We therefore propose the confidence-aware …

Generating Music With Emotions, Chunhui Bao, Qianru Sun

Research Collection School Of Computing and Information Systems

We focus on the music generation conditional on human emotions, specifically the positive and negative emotions. There is no existing large-scale music datasets with the annotation of human emotion labels. It is thus not intuitive how to generate music conditioned on emotion labels. In this paper, we propose an annotation-free method to build a new dataset where each sample is a triplet of lyric, melody and emotion label (without requiring any labours). Specifically, we first train the automated emotion recognition model using the BERT (pre-trained on GoEmotions dataset) on Edmonds Dance dataset. We use it to automatically ‘`label’' the music …

Analyzing Offline Social Engagements: An Empirical Study Of Meetup Events Related To Software Development, Abhishek Sharma, Gede Artha Azriadi Prana, Anamika Sawhney, Nachiappan Nagappan, David Lo

Research Collection School Of Computing and Information Systems

Software developers use a variety of social mediachannels and tools in order to keep themselves up to date,collaborate with other developers, and find projects to contributeto. Meetup is one of such social media used by softwaredevelopers to organize community gatherings. We in this work,investigate the dynamics of Meetup groups and events relatedto software development. Our work is different from previouswork as we focus on the actual event and group data that wascollected using Meetup API.In this work, we performed an empirical study of eventsand groups present on Meetup which are related to softwaredevelopment. First, we identified 6,327 Meetup groups related …

Automated Reconstructions For The Digital Forensic Examiner Workflow, Ryan P. Montgomery

Theses and Dissertations

One product of a digital forensics examination is a reconstruction of events recorded in the media. A reconstruction places all of the case relevant trace into temporal, identity and associative relationships. Creating this reconstruction is a manual and time consuming process for the examiner. This thesis presents AIER. AIER integrates automation, abstraction and visualization into the Autopsy forensic software to improve the reconstruction process. The integration utilizes a custom Autopsy ingest module to extract and abstract artifact data and an interactive graph-based timeline visualization module. These improvements to the forensic examiner workflow are evaluated through a series of use cases.

Performance Of Heterogeneous Multi-Agent Systems With Applications In Combined Arms, Robert J. Wilson

Theses and Dissertations

Multi-agent systems show great potential for solving problems in complex and dynamic domains. Such systems comprise multiple individual entities called agents. Agents possessing the same behavior or physical form are called homogeneous while agents which differ in these respects are termed heterogeneous. The overall behavior of the system emerges from the many interactions of its component agents. Most multi-agent systems research to date focuses on systems of homogeneous agents, but recent work suggests that heterogeneous agents may improve system performance in certain tasks. This research examines the impact of heterogeneity on multi-agent system effectiveness and investigates the application of multi-agent …

Identifying Characteristics For Success Of Robotic Process Automations, Charles M. Unkrich

Theses and Dissertations

In the pursuit of digital transformation, the Air Force creates digital airmen. Digital airmen are robotic process automations designed to eliminate the repetitive high-volume low-cognitive tasks that absorb so much of our Airmen's time. The automation product results in more time to focus on tasks that machines cannot sufficiently perform data analytics and improving the Air Force's informed decision-making. This research investigates the assessment of potential automation cases to ensure that we choose viable tasks for automation and applies multivariate analysis to determine which factors indicate successful projects. The data is insufficient to provide significant insights.

Securing Infiniband Networks With End-Point Encryption, Noah B. Diamond

Theses and Dissertations

The NVIDIA-Mellanox Bluefield-2 is a 100 Gbps high-performance network interface which offers hardware offload and acceleration features that can operate directly on network traffic without routine involvement from the ARM CPU. This allows the ARM multi-core CPU to orchestrate the hardware to perform operations on both Ethernet and RDMA traffic at high rates rather than processing all the traffic directly. A testbed called TNAP was created for performance testing and a MiTM verification process called MiTMVMP is used to ensure proper network configuration. The hardware accelerators of the Bluefield-2 support a throughput of nearly 86 Gbps when using IPsec to …

Dds-Cerberus: Improving Security In Dds Middleware Using Kerberos Tickets, Andrew T. Park

Theses and Dissertations

The military deploys many IoT in battlefield operations to provide information on terrain and enemy combatants. It also deploys automated robots or UAVs where securing and trusting collected data is essential. Choosing the middleware that handles this message transfer is crucial for real-time operations. Networks with multiple entities, including IoT devices, UAVs, and small computers, require robust middleware facilitating message sending in real-time. Ideally, the middleware would provide QoS to handle lost packets and retransmissions in lossy environments, especially between low-power machines. DDS is a middleware that implements real-time and QoS capabilities by sending messages, not based on endpoints but …

Exploring Learning Classifier System Behaviors In Multi-Action, Turn-Based Wargames, Garth J.S. Terlizzi Iii

Theses and Dissertations

State of the art game-playing Artificial Intelligence research focuses heavily on non-symbolic learning methods. These methods offer little explainable insight into their decision-making processes. Learning Classifier Systems (LCSs) provide an alternative. LCSs use rule-based learning, guided by a Genetic Algorithm (GA), to produce a human-readable rule-set. This thesis explores LCS usefulness in game-playing agents for multi-agent wargames. Several Multi-Agent Learning Classifier System (MALCS) variants are implemented in the wargame Stratagem MIST: a Zeroeth-Level Classifier System (ZCS), an extended Classifier System (XCS), and an Adaptive Pittsburgh Classifier System (APCS). These algorithms were tested against baseline agents as well as the Online …

Incorporating Armed Escorts To The Military Medical Evacuation Dispatching Problem Via Stochastic Optimization And Reinforcement Learning, Andrew G. Gelbard

Theses and Dissertations

The military medical evacuation (MEDEVAC) dispatching problem seeks to determine high-quality dispatching policies to maximize the survivability of casualties within contingency operations. This research leverages applied operations research and machine learning techniques to solve the MEDEVAC dispatching problem and evaluate system performance. More specifically, we develop an infinite-horizon, continuous-time Markov decision process (MDP) model and approximate dynamic programming (ADP) solution approach to generate high-quality policies. The ADP solution approach utilizes an approximate value iteration algorithm strategy incorporating gradient descent Q-learning to approximate the value function. A notional, synthetically-generated scenario in Africa based around the capital city of Niger, Niamey is …

An Entity-Component System Based, Ieee Dis Interoperability Interface, Noah W. Scott

Theses and Dissertations

In practice, there are several different methods of organizing data within a given software to fulfil its function. The method known as the Entity-Component System (ECS) is a software architecture where data components define entities. These components are stored as organized lists which are operated upon by systems to inject the system's desired behavior. Data is sent across the networks to communicate between simulation nodes as Protocol Data Units (PDUs). When sending PDUs across a network protocol, each simulation represents a common understanding of the world at the desired level of detail. DIS-compliant simulations are commonly written using an Object-Oriented …

Bayesian Convolutional Neural Network With Prediction Smoothing And Adversarial Class Thresholds, Noah M. Miller

Theses and Dissertations

Using convolutional neural networks (CNNs) for image classification for each frame in a video is a very common technique. Unfortunately, CNNs are very brittle and have a tendency to be over confident in their predictions. This can lead to what we will refer to as “flickering,” which is when the predictions between frames jump back and forth between classes. In this paper, new methods are proposed to combat these shortcomings. This paper utilizes a Bayesian CNN which allows for a distribution of outputs on each data point instead of just a point estimate. These distributions are then smoothed over multiple …

Using Generative Adversarial Networks To Augment Unmanned Aerial Vehicle Image Classification Training Sets, Benjamin J. Mccloskey

Theses and Dissertations

A challenging task in computer vision is finding techniques to improve the object detection and classification capabilities of ML models used for processing images acquired by moving aerial platforms. This research explores if GAN augmented UAV training sets can increase the generalizability of a detection model trained on said data. To answer this question, the YOLOv4-Tiny Object Detection Model was trained with aerial image training sets depicting rural environments. The salient objects within the frames were recreated using various GAN architectures, placed back into the original frames, and the augmented frames appended to the original training sets. GAN augmentation on …

Team Air Combat Using Model-Based Reinforcement Learning, David A. Mottice

Theses and Dissertations

We formulate the first generalized air combat maneuvering problem (ACMP), called the MvN ACMP, wherein M friendly AUCAVs engage against N enemy AUCAVs, developing a Markov decision process (MDP) model to control the team of M Blue AUCAVs. The MDP model leverages a 5-degree-of-freedom aircraft state transition model and formulates a directed energy weapon capability. Instead, a model-based reinforcement learning approach is adopted wherein an approximate policy iteration algorithmic strategy is implemented to attain high-quality approximate policies relative to a high performing benchmark policy. The ADP algorithm utilizes a multi-layer neural network for the value function approximation regression mechanism. One-versus-one …

The Impact Of Visual Feedback And Control Configuration On Pilot-Aircraft Interface Using Head Tracking Technology, Christopher M. Arnold

Theses and Dissertations

Traditional control mechanisms restrict human input on the displays in 5th generation aircraft. This research explored methods for enhancing pilot interaction with large, information dense cockpit displays; specifically, the effects of visual feedback and control button configuration when augmenting cursor control with head tracking technology. Previous studies demonstrated that head tracking can be combined with traditional cursor control to decrease selection times but can increase pilot mental and physical workload. A human subject experiment was performed to evaluate two control button configurations and three visual feedback conditions. A Fitts Law analysis was performed to create predictive models of selection time …

A Thematic And Reference Analysis Of Touchless Technologies, Eric R. Curia

Theses and Dissertations

The purpose of this research is to explore the utility and current state of touchless technologies. Five categories of technologies are identified as a result of collecting and reviewing literature: facial/biometric recognition, gesture recognition, touchless sensing, personal devices, and voice recognition. A thematic analysis was conducted to evaluate the advantages and disadvantages of the five categories. A reference analysis was also conducted to determine the similarities between articles in each category. Touchless sensing showed to have the most advantages and least similar references. Gesture recognition was the opposite. Comparing analyses shows more reliable technology types are more beneficial and diverse.

The Application Of Virtual Reality In Firefighting Training, Dylan A. Gagnon

Theses and Dissertations

Immersive simulations such as virtual reality is becoming more prevalent for use in training environments for many professions. United States Air Force firefighters may benefit from incorporating VR technology into their training program to increase organizational commitment, job satisfaction, self-efficacy, and job performance. With implementing a new training platform, it is also important to understand the relationship between these variables and the perceived benefits and efficacy of the VR training, which has not yet been studied in previous research. This study addresses this issue by gathering data from fire departments currently fielding a VR fire training platform.

Monocular Pose Estimation For Automated Aerial Refueling Via Perspective-N-Point, James C. Lynch

Theses and Dissertations

Any Automated Aerial Refueling (AAR) solution requires the quick and precise estimation of the relative position and rotation of the two aircraft involved. This is currently accomplished using stereo vision techniques augmented by Iterative Closest Point (ICP), but requires post-processing to account for environmental factors such as boom occlusion. This paper proposes a monocular solution, combining a custom-trained single-shot object detection Convolutional Neural Network (CNN) and Perspective-n-Point (PnP) estimation to calculate a pose estimate with a single image. This solution is capable of pose estimation at contact point (22m) within 7cm of error and a rate of 10Hz, regardless of …

Wifitrace: Network-Based Contact Tracing For Infectious Diseases Using Passive Wifi Sensing, Amee Trivedi, Camellia Zakaria, Rajesh Krishna Balan, Ann Becker, George Corey, Prashant Shenoy

Research Collection School Of Computing and Information Systems

Contact tracing is a well-established and effective approach for the containment of the spread of infectious diseases. While Bluetooth-based contact tracing method using phones has become popular recently, these approaches suffer from the need for a critical mass adoption to be effective. In this paper, we present WiFiTrace, a network-centric approach for contact tracing that relies on passive WiFi sensing with no client-side involvement. Our approach exploits WiFi network logs gathered by enterprise networks for performance and security monitoring, and utilizes them for reconstructing device trajectories for contact tracing. Our approach is specifically designed to enhance the efficacy of traditional …

Delaunay Walk For Fast Nearest Neighbor: Accelerating Correspondence Matching For Icp, James D. Anderson, Ryan M. Raettig, Josh Larson, Scott L. Nykl, Clark N. Taylor, Thomas Wischgoll

Computer Science and Engineering 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 …

Microwave Hyperthermia Application With Bioimplant Single Slot Coaxial Antennadesign For Mouse Breast Cancer Treatment, Ahmet Ri̇fat Görgün, Cem Baytöre, Selçuk Çömlekçi̇, Mehmet İbrahi̇m Tuğlu, Adnan Kaya

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, a novel animal model for the breast cancer treatment which contains hyperthermia is proposed. For this main purpose a low cost, interstitial, bioimplant antenna by short ended single slot design is proposed to heat the cancerous tissues. Both the theoretical background of the proposed system and the simulation and measurement results of antenna design are presented. An artificial tissue phantom model has been created under laboratory conditions and then the utility of the proposed antenna has been tested on this model. Artificial tissues have been heated by 25W to (41-44 ? C) in a short time like …

Critical Speed Calculation Of A Refurbishment Of 11mw Hydro Power Plant Unit, Ahmet Seli̇m Pehli̇van, Dario Kraljevic, Ivan Triplat, Beste Bahçeci̇

Turkish Journal of Electrical Engineering and Computer Sciences

Hydro generator design is a significant issue in terms of safety, efficiency, and energy production sustainability. One of the most crucial issues about design criteria is to satisfy the needs of the project?s critical speed. In this work, the critical speed calculation of an 11 MW hydro power plant was investigated with several design steps. Numerical solution methodologies were implemented using ARMD?. A generator design was developed, and the implementation of the rotor was carried out in Antalya, Turkey. Vibration and displacements of the hydro generator are adequate. The generator unit is active for 3 years and have never encountered …

Identification And Mitigation Of Non-Line-Of-Sight Path Effect Using Repeater Forhybrid Ultra-Wideband Positioning And Networking System, Gwo Chin Chung, Mohd. Aqmal Syafiq Kamarudin, It Ee Lee, Soo Fun Tan

Turkish Journal of Electrical Engineering and Computer Sciences

At least two decades ago, various applications have been proposed for the implementation of ultra-wideband (UWB) technology, but only a few of them are being realised such as radar detection, home networking, and indoor positioning. Although UWB positioning offers precise locality tracking, the accuracy of the estimation is greatly affected by the non-line-of-sight (NLOS) path effect. In this paper, we propose a hybrid indoor UWB positioning and networking system that utilises the existing repeater of the data network to eliminate the NLOS paths. A switching algorithm is written to identify the existence of NLOS paths based on received signal strength …

A Factor Graph Optimization Mapping Based On Normaldistributions Transform, Kedi Zhong, Yuansheng Liu, Jiansuo Yang, Ming Lu, Jun Zhang

Turkish Journal of Electrical Engineering and Computer Sciences

This paper aims to achieve highly accurate mapping results and real time pose estimation of autonomous vehicle by using the normal distribution transform (NDT) algoritm. A factor graph optimization algorithm (FGO-NDT) is proposed to address the poor real-time performance and pose drift errors of the NDT algorithm. Smooth point cloud data are obtained by multisensor calibration and data preprocessing. NDT registration is then used for lidar odometry and feature matching. The global navigation satellite system (GNSS) data and loop detection results are added to the factor graph framework as the pose constraint factors to optimize the pose trajectory and eliminate …

Broadband Internet Access: Equal Access And Opportunity For All, Erin Kotten, Emily Louks, Matt Frank, Marissa Mammenga

Master of Social Work Student Policy Advocacy Briefs

The internet has become the primary way households gain access to daily services, including education, healthcare, employment resources, and transportation. In 2016, the United Nations General Assembly designated internet access a basic human right. Millions of Americans are living in the digital divide. Low-wage workers, people of color, children, older adults, individuals with disabilities, the less educated, rural residents, and limited English-speaking households are among those most affected by lack of broadband access in Minnesota. Those without access experience social and economic inequalities. The COVID-19 pandemic heightened the need for broadband access. Minnesota must end the digital divide that limits …

Perceptions Of Violations By Artificial And Human Actors Across Moral Foundations, Timothy Maninger, Daniel Burton Shank

Psychological Science Faculty Research & Creative Works

Artificial agents such as robots, chatbots, and artificial intelligence systems can be the perpetrators of a range of moral violations traditionally limited to human actors. This paper explores how people perceive the same moral violations differently for artificial agent and human perpetrators by addressing three research questions: How wrong are moral foundation violations by artificial agents compared to human perpetrators? Which moral foundations do artificial agents violate compared to human perpetrators? What leads to increased blame for moral foundation violations by artificial agents compared to human perpetrators? We adapt 18 human-perpetrated moral violation scenarios that differ by the moral foundation …

Machine Learning Methods For Generating High Dimensional Discrete Datasets, Giuseppe Manco, Ettore Ritacco, Antonino Rullo, Domenico Saccà, Edoardo Serra

Computer Science Faculty Publications and Presentations

The development of platforms and techniques for emerging Big Data and Machine Learning applications requires the availability of real-life datasets. A possible solution is to synthesize datasets that reflect patterns of real ones using a two-step approach: first, a real dataset X is analyzed to derive relevant patterns Z and, then, to use such patterns for reconstructing a new dataset X' that preserves the main characteristics of X. This survey explores two possible approaches: (1) Constraint-based generation and (2) probabilistic generative modeling. The former is devised using inverse mining (IFM) techniques, and consists of generating a dataset satisfying given …

Removing The Veil: Shining Light On The Lack Of Inclusivity In Cybersecurity Education For Students With Disabilities, Felicia Hellems, Sajal Bhatia

School of Computer Science & Engineering Faculty Publications

There are currently over one billion people living with some form of disability worldwide. The continuous increase in new technologies in today's society comes with an increased risk in security. A fundamental knowledge of cybersecurity should be a basic right available to all users of technology. A review of literature in the fields of cybersecurity, STEM, and computer science (CS) has revealed existent gaps regarding educational methods for teaching cybersecurity to students with disabilities (SWD's). To date, SWD's are largely left without equitable access to cybersecurity education. Our goal is to identify current educational methods being used to teach SWD's …