Biola University
Governors State University
Gettysburg College
South Dakota State University
The University of Maine
University of Arkansas, Fayetteville
Self-Learning Algorithms For Intrusion Detection And Prevention Systems (Idps),
2023
Southern Methodist University
Self-Learning Algorithms For Intrusion Detection And Prevention Systems (Idps), Juan E. Nunez, Roger W. Tchegui Donfack, Rohit Rohit, Hayley Horn
SMU Data Science Review
Today, there is an increased risk to data privacy and information security due to cyberattacks that compromise data reliability and accessibility. New machine learning models are needed to detect and prevent these cyberattacks. One application of these models is cybersecurity threat detection and prevention systems that can create a baseline of a network's traffic patterns to detect anomalies without needing pre-labeled data; thus, enabling the identification of abnormal network events as threats. This research explored algorithms that can help automate anomaly detection on an enterprise network using Canadian Institute for Cybersecurity data. This study demonstrates that Neural Networks with Bayesian …
Codebase Relationship Visualizer: Visualizing Relationships Between Source Code Files,
2023
Southern Adventist University
Codebase Relationship Visualizer: Visualizing Relationships Between Source Code Files, Jesse Hines
MS in Computer Science Project Reports
Understanding relationships between files and their directory structure is a fundamental part of the software development process. However, it can be hard to grasp these relationships without a convenient way to visualize how files are connected and how they fit into the directory structure of the codebase. In this paper we describe CodeBase Relationship Visualizer (CBRV), a Visual Studio Code extension that interactively visualizes the relationships between files. CBRV displays the relationships between files as arrows superimposed over a diagram of the codebase's directory structure. CBRV comes bundled with visualizations of the stack trace path, a dependency graph for Python …
Integrated Organizational Machine Learning For Aviation Flight Data,
2023
Kansas State University
Integrated Organizational Machine Learning For Aviation Flight Data, Michael J. Pritchard, Paul Thomas, Eric Webb, Jon Martin, Austin Walden
National Training Aircraft Symposium (NTAS)
An increased availability of data and computing power has allowed organizations to apply machine learning techniques to various fleet monitoring activities. Additionally, our ability to acquire aircraft data has increased due to the miniaturization of small form factor computing machines. Aircraft data collection processes contain many data features in the form of multivariate time-series (continuous, discrete, categorical, etc.) which can be used to train machine learning models. Yet, three major challenges still face many flight organizations 1) integration and automation of data collection frameworks, 2) data cleanup and preparation, and 3) embedded machine learning framework. Data cleanup and preparation has …
Towards Hardware-Based Application Fingerprinting With Microarchitectural Signals For Zero Trust Environments,
2023
Air Force Institute of Technology
Towards Hardware-Based Application Fingerprinting With Microarchitectural Signals For Zero Trust Environments, Tor J. Langehaug, Scott R. Graham
Faculty Publications
The interactions between software and hardware are increasingly important to computer system security. This research collects sequences of microprocessor control signals to develop machine learning models that identify software tasks. The proposed approach considers software task identification in hardware as a general problem with attacks treated as a subset of software tasks. Two lines of effort are presented. First, a data collection approach is described to extract sequences of control signals labeled by task identity during real (i.e., non-simulated) system operation. Second, experimental design is used to select hardware and software configuration to train and evaluate machine learning models. The …
Hybrid Life Cycles In Software Development,
2022
Grand Valley State University
Hybrid Life Cycles In Software Development, Eric Vincent Schoenborn
Culminating Experience Projects
This project applied software specification gathering, architecture, work planning, and development to a real-world development effort for a local business. This project began with a feasibility meeting with the owner of Zeal Aerial Fitness. After feasibility was assessed the intended users, needed functionality, and expected user restrictions were identified with the stakeholders. A hybrid software lifecycle was selected to allow a focus on base functionality up front followed by an iterative development of expectations of the stakeholders. I was able to create various specification diagrams that express the end projects goals to both developers and non-tech individuals using a standard …
Secure Cloud-Based Iot Water Quality Gathering For Analysis And Visualization,
2022
Kennesaw State University
Secure Cloud-Based Iot Water Quality Gathering For Analysis And Visualization, Soin Abdoul Kassif Baba M Traore
Symposium of Student Scholars
Water quality refers to measurable water characteristics, including chemical, biological, physical, and radiological characteristics usually relative to human needs. Dumping waste and untreated sewage are the reasons for water pollution and several diseases to the living hood. The quality of water can also have a significant impact on animals and plant ecosystems. Therefore, keeping track of water quality is a substantial national interest. Much research has been done for measuring water quality using sensors to prevent water pollution. In summary, those systems are built based on online and reagent-free water monitoring SCADA systems in wired networks. However, centralized servers, transmission …
Context-Aware Collaborative Neuro-Symbolic Inference In Internet Of Battlefield Things,
2022
Army Cyber Institute, U.S. Military Academy
Context-Aware Collaborative Neuro-Symbolic Inference In Internet Of Battlefield Things, Tarek Abdelzaher, Nathaniel D. Bastian, Susmit Jha, Lance Kaplan, Mani Srivastava, Venugopal Veeravalli
ACI Journal Articles
IoBTs must feature collaborative, context-aware, multi-modal fusion for real-time, robust decision-making in adversarial environments. The integration of machine learning (ML) models into IoBTs has been successful at solving these problems at a small scale (e.g., AiTR), but state-of-the-art ML models grow exponentially with increasing temporal and spatial scale of modeled phenomena, and can thus become brittle, untrustworthy, and vulnerable when interpreting large-scale tactical edge data. To address this challenge, we need to develop principles and methodologies for uncertainty-quantified neuro-symbolic ML, where learning and inference exploit symbolic knowledge and reasoning, in addition to, multi-modal and multi-vantage sensor data. The approach features …
Detecting Selfish Mining Attacks Against A Blockchain Using Machine Learing,
2022
University of South Alabama
Detecting Selfish Mining Attacks Against A Blockchain Using Machine Learing, Matthew A. Peterson
Theses and Dissertations
Selfish mining is an attack against a blockchain where miners hide newly discovered blocks instead of publishing them to the rest of the network. Selfish mining has been a potential issue for blockchains since it was first discovered by Eyal and Sirer. It can be used by malicious miners to earn a disproportionate share of the mining rewards or in conjunction with other attacks to steal money from network users. Several of these attacks were launched in 2018, 2019, and 2020 with the attackers stealing as much as $18 Million. Developers made several different attempts to fix this issue, but …
Scalable Distributional Robustness In A Class Of Non Convex Optimization With Guarantees,
2022
Singapore Management University
Scalable Distributional Robustness In A Class Of Non Convex Optimization With Guarantees, Avinandan Bose, Arunesh Sinha, Tien Mai
Research Collection School Of Computing and Information Systems
Distributionally robust optimization (DRO) has shown lot of promise in providing robustness in learning as well as sample based optimization problems. We endeavor to provide DRO solutions for a class of sum of fractionals, non-convex optimization which is used for decision making in prominent areas such as facility location and security games. In contrast to previous work, we find it more tractable to optimize the equivalent variance regularized form of DRO rather than the minimax form. We transform the variance regularized form to a mixed-integer second order cone program (MISOCP), which, while guaranteeing near global optimality, does not scale enough …
Virtual Sensor Middleware: Managing Iot Data For The Fog-Cloud Platform,
2022
Western University
Virtual Sensor Middleware: Managing Iot Data For The Fog-Cloud Platform, Fadi Almahamid, Hanan Lutfiyya, Katarina Grolinger
Electrical and Computer Engineering Publications
This paper introduces the Virtual Sensor Middleware (VSM), which facilitates distributed sensor data processing on multiple fog nodes. VSM uses a Virtual Sensor as the core component of the middleware. The virtual sensor concept is redesigned to support functionality beyond sensor/device virtualization, such as deploying a set of virtual sensors to represent an IoT application and distributed sensor data processing across multiple fog nodes. Furthermore, the virtual sensor deals with the heterogeneous nature of IoT devices and the various communication protocols using different adapters to communicate with the IoT devices and the underlying protocol. VSM uses the publish-subscribe design pattern …
Algorithm-Based Fault Tolerance At Scale,
2022
University of Alabama in Huntsville
Algorithm-Based Fault Tolerance At Scale, Hayden Estes
Summer Community of Scholars Posters (RCEU and HCR Combined Programs)
No abstract provided.
Gpgpu Microbenchmarking For Irregular Application Optimization,
2022
Mississippi State University
Gpgpu Microbenchmarking For Irregular Application Optimization, Dalton R. Winans-Pruitt
Theses and Dissertations
Irregular applications, such as unstructured mesh operations, do not easily map onto the typical GPU programming paradigms endorsed by GPU manufacturers, which mostly focus on maximizing concurrency for latency hiding. In this work, we show how alternative techniques focused on latency amortization can be used to control overall latency while requiring less concurrency. We used a custom-built microbenchmarking framework to test several GPU kernels and show how the GPU behaves under relevant workloads. We demonstrate that coalescing is not required for efficacious performance; an uncoalesced access pattern can achieve high bandwidth - even over 80% of the theoretical global memory …
Holistic Performance Analysis And Optimization Of Unified Virtual Memory,
2022
Clemson University
Holistic Performance Analysis And Optimization Of Unified Virtual Memory, Tyler Allen
All Dissertations
The programming difficulty of creating GPU-accelerated high performance computing (HPC) codes has been greatly reduced by the advent of Unified Memory technologies that abstract the management of physical memory away from the developer. However, these systems incur substantial overhead that paradoxically grows for codes where these technologies are most useful. While these technologies are increasingly adopted for use in modern HPC frameworks and applications, the performance cost reduces the efficiency of these systems and turns away some developers from adoption entirely. These systems are naturally difficult to optimize due to the large number of interconnected hardware and software components that …
Parallel Algorithms For Scalable Graph Mining: Applications On Big Data And Machine Learning,
2022
University of New Orleans
Parallel Algorithms For Scalable Graph Mining: Applications On Big Data And Machine Learning, Naw Safrin Sattar
University of New Orleans Theses and Dissertations
Parallel computing plays a crucial role in processing large-scale graph data. Complex network analysis is an exciting area of research for many applications in different scientific domains e.g., sociology, biology, online media, recommendation systems and many more. Graph mining is an area of interest with diverse problems from different domains of our daily life. Due to the advancement of data and computing technologies, graph data is growing at an enormous rate, for example, the number of links in social networks is growing every millisecond. Machine/Deep learning plays a significant role for technological accomplishments to work with big data in modern …
A Recommendation System Approach To Tune A Qubo Solver,
2022
Singapore Management University
A Recommendation System Approach To Tune A Qubo Solver, Siong Thye Goh, Jianyuan Bo, Matthieu Parizy, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
There are two major challenges to solving constrained optimization problems using a QuadraticUnconstrained Binary Optimization or QUBO solver (QS). First, we need to tune both the underlyingproblem parameters and the algorithm parameters. Second, the solution returned from a QSmight not be feasible. While it is common to use automated tuners such as SMAC and Hyperopt totune the algorithm parameters, the initial search ranges input for the auto tuner affect the performanceof the QS. In this paper, we propose a framework that resembles the Algorithm Selection(AS) framework to tune algorithm parameters for an annealing-based QS. To cope with constraints,we focus on …
Aligning The Transit Industry And Their Vendors In The Face Of Increasing Cyber Risk: Recommendations For Identifying And Addressing Cybersecurity Challenges,
2022
Mineta Transportation Institute
Aligning The Transit Industry And Their Vendors In The Face Of Increasing Cyber Risk: Recommendations For Identifying And Addressing Cybersecurity Challenges, Scott Belcher, Terri Belcher, Kathryn Seckman, Brandon Thomas, Homayun Yaqub
Mineta Transportation Institute Publications
Public transit agencies in the United States depend on external vendors to help deliver and maintain many essential services and to provide critical technologies, from ticket purchases to scheduling to email management. While the integration of new, advanced technologies into the public transit industry brings important advancements to U.S. critical transportation infrastructure, the application of digital technologies also brings with it a new assortment of digital risks. Transit agencies of all sizes are finding themselves subject to cyber incidents—most notably ransomware attacks—like those experienced by larger, more prominent companies and critical infrastructure providers. The findings in this report focus on …
Using Graph Theoretical Methods And Traceroute To Visually Represent Hidden Networks,
2022
University of Nebraska at Omaha
Using Graph Theoretical Methods And Traceroute To Visually Represent Hidden Networks, Jordan M. Sahs
UNO Student Research and Creative Activity Fair
Within the scope of a Wide Area Network (WAN), a large geographical communication network in which a collection of networking devices communicate data to each other, an example being the spanning communication network, known as the Internet, around continents. Within WANs exists a collection of Routers that transfer network packets to other devices. An issue pertinent to WANs is their immeasurable size and density, as we are not sure of the amount, or the scope, of all the devices that exists within the network. By tracing the routes and transits of data that traverses within the WAN, we can identify …
Torsh: Obfuscating Consumer Internet-Of-Things Traffic With A Collaborative Smart-Home Router Network,
2022
Dartmouth College
Torsh: Obfuscating Consumer Internet-Of-Things Traffic With A Collaborative Smart-Home Router Network, Adam Vandenbussche
Dartmouth College Undergraduate Theses
When consumers install Internet-connected "smart devices" in their homes, metadata arising from the communications between these devices and their cloud-based service providers enables adversaries privy to this traffic to profile users, even when adequate encryption is used. Internet service providers (ISPs) are one potential adversary privy to users’ incom- ing and outgoing Internet traffic and either currently use this insight to assemble and sell consumer advertising profiles or may in the future do so. With existing defenses against such profiling falling short of meeting user preferences and abilities, there is a need for a novel solution that empowers consumers to …
Developing A Miniature Smart Boat For Marine Research,
2022
California Polytechnic State University, San Luis Obispo
Developing A Miniature Smart Boat For Marine Research, Michael Isaac Eirinberg
Computer Engineering
This project examines the development of a smart boat which could serve as a possible marine research apparatus. The smart boat consists of a miniature vessel containing a low-cost microcontroller to live stream a camera feed, GPS telemetry, and compass data through its own WiFi access point. The smart boat also has the potential for autonomous navigation. My project captivated the interest of several members of California Polytechnic State University, San Luis Obispo’s (Cal Poly SLO) Marine Science Department faculty, who proposed a variety of fascinating and valuable smart boat applications.
Out-Of-Core Gpu Path Tracing On Large Instanced Scenes Via Geometry Streaming,
2022
California Polytechnic State University, San Luis Obispo
Out-Of-Core Gpu Path Tracing On Large Instanced Scenes Via Geometry Streaming, Jeremy Berchtold
Master's Theses
We present a technique for out-of-core GPU path tracing of arbitrarily large scenes that is compatible with hardware-accelerated ray-tracing. Our technique improves upon previous works by subdividing the scene spatially into streamable chunks that are loaded using a priority system that maximizes ray throughput and minimizes GPU memory usage. This allows for arbitrarily large scaling of scene complexity. Our system required under 19 minutes to render a solid color version of Disney's Moana Island scene (39.3 million instances, 261.1 million unique quads, and 82.4 billion instanced quads at a resolution of 1024x429 and 1024spp on an RTX 5000 (24GB memory …
