Skip to Main Content

Apply Now to the University of Wyoming apply now

CEDAR Symposium 2021

October 4, 2021

The Fall 2021 CEDAR Symposium will be held October 8th 10AM-2PM in the EERB Atrium.

Malware Analysis at Scale with ElasticSearch

Poster 1 YouTube Video
 Poster for Malware Analysis at Scale with ElasticSearch

Authors: Taylor McCampbell, Rafer Cooley

Project Lead: Rafer Cooley

Advisor: Dr. Mike Borowczak

AbstractWith the exponential increase of malware being released every day, an efficient and resilient scheme for malware indicators is needed. To store, analyze, and develop future indicators, an efficient data management system is needed that can handle the amount of information necessary to accurately test said indicator schemes. This project continues work on the ARTEMIS project which investigates researchers can base protection for digital systems off techniques used by biological immune system


Great Revolt: Attacking AI Models for Vulnerability Analysis

Poster 2 YouTube Video
 Poster for Great Revolt: Attacking AI Models for Vulnerability Analysis

Authors: Taylor McCampbell, Rafer Cooley

Project Lead: Rafer Cooley

Advisor: Dr. Mike Borowczak

AbstractGreat Revolt is a large-scale project investigating the security of Machine Learning systems and infrastructure. Instead of focusing on the models themselves, it examines the underlying libraries and code that’s running those models. There are two phases to the Great Revolt project. First, research is being conducted on the artificial intelligence and machine learning fields to determine what types of modes are popular, what libraries do industry leaders use, and what hardware is machine learning typically ran on. Then, different AI/ML models will be analyzed from an adversarial standpoint to discover and test different vulnerabilities in industry standard models.


Hermes: Developing Novel Attacks

Poster 3 YouTube Video
 Poster for Hermes: Developing Novel Attacks Against Cold Wallets

Authors: Clay Carper

Project Lead: Clay Carper

Advisor: Dr. Mike Borowczak

Abstract: The most secure way to currently store private keys associated with cryptocurrency wallets is utilizing a hardware wallet, also known as a cold wallet. There is a variety of devices available, ranging from open source to privately managed. Each device has a unique recovery phrase which allows for recovery of private keys, should the device be lost or damaged. Accessing this recovery phrase presents an interesting and highly lucrative attack target. This project examines past known attacks at a hardware level and works to develop novel attacks against the entire class of devices. Improving the developer accessibility to potential side-channel threats is also desirable.


Industrial Control System Outreach Training Development

Poster 4 YouTube Video
 Poster for Industrial Control System Outreach Training Development

Authors: Michael Pate, Taylor McCampbell, Taylor LaForce

Project Lead: Rafer Cooley

Advisor: Dr. Mike Borowczak

Abstract: Out-of-date and rarely updated computing devices control critical infrastructure systems such as power and water distribution. These devices often are complicated to update and patch for vulnerabilities since they control processes that are difficult or costly to stop. When connected to the internet, these devices pose a security risk which (if exploited) could harm the critical processes they are controlling. This research currently focuses on ICS outreach training development. Most training and education usually focus on regular cyber security skills. Our group is building modules that focus on training people for critical infrastructure security based on specific skill sets including awareness of everyday ICS and how they become compromised, programmable logic controllers and communication in control systems, and how to mitigate attacks on critical infrastructure.



Jangseung: Defense Against Adversarial Perturbations

Poster 5 YouTube Video
 Poster for Jangseung: Defense Against Adversarial Perterbations

Authors: Alicia Thoney, Shawna Wolf

Project Lead: Shaya Wolf

Advisor: Dr. Mike Borowczak

Abstract: Jangseung is a preprocessor that limits the effects of poisoning attacks without impeding on accuracy. Jangseung was created to guard support vector machines (SVMs) from poisoned data by utilizing anomaly detection algorithms. It was tested on a series of SVMs and protected them from basic poisoning attacks. Also, while Jangseung protects against adversaries actively attacking the accuracy of an SVM, it also defends against accidental false data that would otherwise go unnoticed and unintentionally poison an SVM. Current research is extending this to deep learning neural networks, different types of adversarial perturbations, and larger datasets for more complicated classification techniques.



Julia: A New Course with a New Language

Poster 6 YouTube Video
 Poster for Julia: A New Course with a New Language

Authors: Danny Radosevich

Project Lead: Danny Radosevich

Advisor: Dr. Mike Borowczak

Abstract: Universities across the country struggle to teach cohesive introductory computer science courses that both invite new students to the field as well as motivate the use of cultivated skills in other departments and majors. At the University of Wyoming, many different departments teach their own versions of “Intro to CS”; however, each class has low enrollment and/or outdated languages. The current approach to providing these crucial skills to students wastes resources on antiquated course designs that fail to teach students with the use of cutting-edge languages. This research develops a flexible and intuitive introductory computer science course based on educational standards as well as accessibility to students across many departments. This course was centered around the Julia programming language and expands on areas that students from many backgrounds can use moving forward.



Michelangelo: An AI Sculptor of Emergent Behavior

Poster 7 YouTube Video
 Poster for Michelangelo: An AI Sculptor of Emergent Behavior

Authors: Jenna Goodrich, Ally Hays, Taylor LaForce

Project Lead: Andey Robins

Advisor: Dr. Mike Borowczak

Abstract: Michelangelo is a project, still in the inception phase, that seeks to bring self-learning, genetic learning algorithms to distributed systems applications. These self-designing systems emulate previous behavior as discovered in SHARKS research before generalizing and creating more advanced and unresearched swarm systems. This project also has the goal of making the design outputs of machine learning systems human-readable through applied inductive program synthesis on a decentral-first domain-specific language. Currently, this research focuses on two central questions. First, can we use genetic learning and inductive program synthesis to develop an AI to sculpt intended emergent behavior in decentralized and distributed systems. Second, what methods of selection, crossover, and mutation should be used on the bytecode genes to create a genetic algorithm that teaches these desired behaviors.



Orpheus: Prudent Vocal Authentication

Poster 8 YouTube Video
 Poster for Orpheus: Prudent Vocal Authentication

Authors: Shawna Wolf, Natasha Miller, Quinn Clark

Project Lead: Shaya Wolf

Advisor: Dr. Mike Borowczak

Abstract: Orpheus is a prudent vocal authentication program that strives to preserve users’ privacy while storing vocal data for user verification. Current speaker recognition systems pose security threats because vocal feature data can be used to reproduce a person’s unique voice, which can compromise a system or expose a user’s private biometric data. Currently, the Orpheus group is building a privacy framework and constructing machine learning models for vocal authentication that measure the amount of biometric data leaked by each model. So far, scripts have been developed to record audio and extract features commonly used in vocal authentication models (utilizing the Librosa Python library). Next, models will be constructed and measured against our privacy metric to determine which models and datasets pose the least danger to user privacy.



CEDAR Summer Outreach Camps and Programs

Poster 9 YouTube Video
 Poster for CEDAR Summer Outreach Camps and Programs

Authors: Mason Johnson, Danny Radosevich, Andey Robins

Project Lead: Mason Johnson

Advisor: Dr. Mike Borowczak

Abstract: The development of a Cybersecurity and Computer Science pipeline begins in K-12 classrooms. The CEDAR center provides a variety of professional development and summer camp experiences funded by various federal agencies (e.g., NSF, NSA) and corporate sponsors (e.g., MilliporeSigma, Kraken). In 2021 these activities reached more than 150 K-12 teachers and students while also providing University of Wyoming students with relevant communication, teaching, and research experiences. You can find out more about the variety of opportunities at the University of Wyoming Computer Science Hub located online at



CEDAR PacMan: Maze Authentication Using Behavioral Biometrics

Poster 10 YouTube Video
 Poster for CEDAR PacMan: Maze Authentication Using Behavioral Biometrics

Authors: Colton Roach, Adeline Reichert, Jessa Gegax, Natasha Miller

Project Lead: Shaya Wolf, Mason Johnson, Andey Robins

Advisor: Dr. Mike Borowczak

Abstract: PacMan is an authentication mechanism built around how a user completes a puzzle, in this case a simple, randomly generated maze. Other game-based authentication research has shown around 80% accuracy using cognitive data, but higher accuracy is needed before such models could be utilized in real-world environments. Current attempts revolve around making puzzles/mazes that are complex enough to ensure user behavior is unique, but simple enough that it is not an arduous task. Our group has looked at two approaches for validating users with high accuracy. First, the MiniRocket model was considered given the models ability to solve runtime concerns and produce high accuracies; however, this model is not built for one-class classification problems. Second, cross-correlation statistics were used to see preliminary differences in how a user completes a maze versus how others attempt the same problem.




Secret-Agent: Behavioral-Biometric Continuous Authentication

Poster 12 YouTube Video
 Poster for Secret-Agent: Behavioral-Biometric Continuous Authentication

Authors: Jarek Brown, Faith Coslett, Adeline Reichert

Project Lead: Shaya Wolf, Danny Radosevich

Advisor: Dr. Mike Borowczak

Abstract: This research spoofs continuous authentication models using algorithms that utilize limited user data to mimic their biometrics. Spy Hunter, a continuous authentication mechanism uses keystroke dynamics to validate users over blocks of data. This easily incorporated periodic biometric authentication system validates genuine users and detects intruders using keystroke dynamics. Currently, SecretAgent uses keystroke timings gathered for the SpyHunter authenticator and runs a simple algorithm to reconstruct press and release times for the user. Then, a script takes an adversary’s typing pattern and fires the key presses at the cadence of the valid user to spoof the authenticator.


SHARKS Obstacle Avoidance

Poster 13 YouTube Video
 Poster for SHARKS Obstacle Avoidance

Authors: Ally Hays, Jarek Brown

Project Lead: Rafer Cooley, Shaya Wolf

Advisor: Dr. Mike Borowczak

Abstract: The Secure, Heterogeneous, Autonomous, and Rotational Knowledge for Swarms (SHARKS) protocol investigates distributed algorithms for swarm movement patterns. The drones in any given swarm have restrained compute resources and little memory, so developing efficient behaviors is necessary for the agents to work within these restrictions. In addition to positioning behaviors, this project investigates safety protocols to protect the swarm from adversarial swarms and environmental obstacles. Current research focuses on four types of stationary obstacles that cause the swarm to die off. Different avoidance techniques are tested and shown effective for maintaining the population; however, the stability of the swarm is diminished.



Privacy Inference Under Side Channel Power Attacks

Poster 14 YouTube Video
 Poster for Privacy Inference under Side Channel Power Attacks

Authors: Hui Hu, Shaya Wolf, Rafer Cooley, Jayden Parker Vap

Project Lead: Hui Hu

Advisor: Dr. Mike Borowczak

Abstract: This project aims to infer private user data by utilizing side-channel power attack techniques on machine learning models deployed on dedicated hardware. This research considers both model privacy and data privacy. Our previous studies have shown that side-channel power attacks are efficient in inferring private information in the modeling process such as model types, model structures, or model hyperparameters. This allows an adversary to determine finer details about a model that are generally considered private to protect the model from common machine learning attacks. Currently, we are looking into adversarial sample detection and privacy-preserving under side-channel power attacks. In other words, we consider attacks that allow us to infer sensitive user data and ask how we may protect models from both of these attack vectors without loss of model accuracy.



Offensive and Defensive Analysis of Behavioral Biometrics on Smart Wearables

Poster 15
 Poster for Offensive and Defensive Analysis of Behavioral Biometrics on Smart Wearables

Authors: Sindhu Reddy Kalathur Gopal

Project Lead: Sindhu Reddy Kalathur Gopal

Advisor: Dr. Diksha Shukla

AbstractComputing devices, IOT devices, and wearable devices have become integral part of our lives  and we use them day in and day out. The information stored in these devices includes sensitive  information such as personal information, contact information, financial transaction details, etc.  While the users are immersed in wearable devices and gadgets, the imposters attack their devices, thereby gaining unauthorized access to obtain the sensitive information. In order to address these shortcomings, behavioral biometrics based active authentication system is proposed.
Our research work has a two-fold impact in the area of Privacy and Security: (i) Analyzes the vulnerabilities in wearable devices by developing attack models against different devices both in mixed reality (virtual) environment and real environment, (ii) Develop easy-to-use, lightweight, inexpensive, and unobtrusive behavioral biometrics-based active authentication system to verify users' identity. This study focuses on behavioral biometrics, namely hand movement patterns and EEG signals which can be acquired using inertial motion sensors (accelerometer and gyroscope) and consumer grade EEG devices.


1000 E. University Ave. Laramie, WY 82071
UW Operators (307) 766-1121 | Contact Us | Download Adobe Reader

Accreditation | Virtual Tour | Emergency Preparedness | Employment at UW | Privacy Policy | Harassment & Discrimination | Accessibility Accessibility information icon