Computer Sciences Commons^™

A Solder-Defined Computer Architecture For Backdoor And Malware Resistance, Marc W. Abel

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This research is about securing control of those devices we most depend on for integrity and confidentiality. An emerging concern is that complex integrated circuits may be subject to exploitable defects or backdoors, and measures for inspection and audit of these chips are neither supported nor scalable. One approach for providing a “supply chain firewall” may be to forgo such components, and instead to build central processing units (CPUs) and other complex logic from simple, generic parts. This work investigates the capability and speed ceiling when open-source hardware methodologies are fused with maker-scale assembly tools and visible-scale final inspection.

The …

Few-Shot Malware Detection Using A Novel Adversarial Reprogramming Model, Ekula Praveen Kumar

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The increasing sophistication of malware has made detecting and defending against new strains a major challenge for cybersecurity. One promising approach to this problem is using machine learning techniques that extract representative features and train classification models to detect malware in an early stage. However, training such machine learning-based malware detection models represents a significant challenge that requires a large number of high-quality labeled data samples while it is very costly to obtain them in real-world scenarios. In other words, training machine learning models for malware detection requires the capability to learn from only a few labeled examples. To address …

Evaluating Similarity Of Cross-Architecture Basic Blocks, Elijah L. Meyer

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Vulnerabilities in source code can be compiled for multiple processor architectures and make their way into several different devices. Security researchers frequently have no way to obtain this source code to analyze for vulnerabilities. Therefore, the ability to effectively analyze binary code is essential. Similarity detection is one facet of binary code analysis. Because source code can be compiled for different architectures, the need can arise for detecting code similarity across architectures. This need is especially apparent when analyzing firmware from embedded computing environments such as Internet of Things devices, where the processor architecture is dependent on the product and …

Locality Analysis Of Patched Php Vulnerabilities, Luke N. Holt

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The size and complexity of modern software programs is constantly growing making it increasingly difficult to diligently find and diagnose security exploits. The ability to quickly and effectively release patches to prevent existing vulnerabilities significantly limits the exploitation of users and/or the company itself. Due to this it has become crucial to provide the capability of not only releasing a patched version, but also to do so quickly to mitigate the potential damage. In this thesis, we propose metrics for evaluating the locality between exploitable code and its corresponding sanitation API such that we can statistically determine the proximity of …

Sportiasts, Yuvraj Subedi

Williams Honors College, Honors Research Projects

Sportiasts is an online platform that connects sports enthusiasts. This platform explores the most recent back-end tool: Django and PostgreSQL to provide sports enthusiasts a platform to connect with each other. This platform is versatile and dynamic for the users to have their best experience connecting with sports communities. Anyone with sports interests can use this application to explore, connect, and create sports communities.

Reflecting On Recurring Failures In Iot Development, Dharun Anandayuvaraj, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

As IoT systems are given more responsibility and autonomy, they offer greater benefits, but also carry greater risks. We believe this trend invigorates an old challenge of software engineering: how to develop high-risk software-intensive systems safely and securely under market pressures? As a first step, we conducted a systematic analysis of recent IoT failures to identify engineering challenges. We collected and analyzed 22 news reports and studied the sources, impacts, and repair strategies of failures in IoT systems. We observed failure trends both within and across application domains. We also observed that failure themes have persisted over time. To alleviate …

Reflections On Software Failure Analysis, Paschal C. Amusuo, Aishwarya Sharma, Siddharth R. Rao, Abbey Vincent, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Failure studies are important in revealing the root causes, behaviors, and life cycle of defects in software systems. These studies either focus on understanding the characteristics of defects in specific classes of systems or the characteristics of a specific type of defect in the systems it manifests in. Failure studies have influenced various software engineering research directions, especially in the area of software evolution, defect detection, and program repair.

In this paper, we reflect on the conduct of failure studies in software engineering. We reviewed a sample of 52 failure study papers. We identified several recurring problems in these studies, …

Sok: Analysis Of Software Supply Chain Security By Establishing Secure Design Properties, Chinenye Okafor, Taylor R. Schorlemmer, Santiao Torres-Arias, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

This paper systematizes knowledge about secure software supply chain patterns. It identifies four stages of a software supply chain attack and proposes three security properties crucial for a secured supply chain: transparency, validity, and separation. The paper describes current security approaches and maps them to the proposed security properties, including research ideas and case studies of supply chains in practice. It discusses the strengths and weaknesses of current approaches relative to known attacks and details the various security frameworks put out to ensure the security of the software supply chain. Finally, the paper highlights potential gaps in actor and operation-centered …

Exploiting Input Sanitization For Regex Denial Of Service, Efe Barlas, Xin Du, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Web services use server-side input sanitization to guard against harmful input. Some web services publish their sanitization logic to make their client interface more usable, e.g., allowing clients to debug invalid requests locally. However, this usability practice poses a security risk. Specifically, services may share the regexes they use to sanitize input strings — and regex-based denial of service (ReDoS) is an emerging threat. Although prominent service outages caused by ReDoS have spurred interest in this topic, we know little about the degree to which live web services are vulnerable to ReDoS.

In this paper, we conduct the first black-box …

Discrepancies Among Pre-Trained Deep Neural Networks: A New Threat To Model Zoo Reliability, Diego Montes, Pongpatapee Peerapatanapokin, Jeff Schultz, Chengjun Guo, Wenxin Jiang, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Training deep neural networks (DNNs) takes significant time and resources. A practice for expedited deployment is to use pre-trained deep neural networks (PTNNs), often from model zoos.collections of PTNNs; yet, the reliability of model zoos remains unexamined. In the absence of an industry standard for the implementation and performance of PTNNs, engineers cannot confidently incorporate them into production systems. As a first step, discovering potential discrepancies between PTNNs across model zoos would reveal a threat to model zoo reliability. Prior works indicated existing variances in deep learning systems in terms of accuracy. However, broader measures of reliability for PTNNs from …

An Empirical Study On The Impact Of Deep Parameters On Mobile App Energy Usage, Qiang Xu, James C. Davis, Y Charlie Hu, Abhilash Jindal

Department of Electrical and Computer Engineering Faculty Publications

Improving software performance through configuration parameter tuning is a common activity during software maintenance. Beyond traditional performance metrics like latency, mobile app developers are interested in reducing app energy usage. Some mobile apps have centralized locations for parameter tuning, similar to databases and operating systems, but it is common for mobile apps to have hundreds of parameters scattered around the source code. The correlation between these "deep" parameters and app energy usage is unclear. Researchers have studied the energy effects of deep parameters in specific modules, but we lack a systematic understanding of the energy impact of mobile deep parameters. …

Data Processing In A Database Management System Using Parallel Processing, Stephen Shears

Williams Honors College, Honors Research Projects

This research project will be focused on parallel processing as it is used with database management systems to process data. Specifically, the goal is to see if creating a database management system with parallel processing at the forefront of its data processing can offer enough of an efficiency increase to warrant using it against a sequential database management system and is it possible to make that system just as reliable as those databases without parallel processing. A parallel processed database will be created with a focus on monitoring its data reliability and consistency. It will then be compared to two …

Resume Parser And Job Search, Stephan Gunawardana

Williams Honors College, Honors Research Projects

The basic goal of this project was to create a four-tier web application which would allow users to upload their personal resumes to the website, and it ultimately would parse the resume and look for keywords. Then using these keywords, it would use a job API to look for these values and ultimately display job information to the user based on their skills. While applications exist which would look for jobs based on skills, it was mostly manually inputted skills. However, with my application you can simply upload your resume to the website, and it will basically take care of …

Securing Big Data Scientific Workflows & Gpgpu Computation In The Commodity Cloud With Hardware-Assisted Trusted Execution Environments, Saeid Mofrad

Wayne State University Dissertations

Nowadays, big data analytics are essential tools for helping businesses, healthcare providers, and decision-makers in society, in making important and strategic choices that benefit their business, patients, and people. Processing big data requires massive amounts of computing and storage resources due to the nature of big data characteristics. Cloud providers with their low-cost, elastic, and enormous amounts of hardware and software resources are a suitable platform for deploying big data analytics systems. Also, the increasing demand for GPU accelerator instances from big data and machine learning applications encourages cloud providers to invest in GPU servers to enable the provisioning of …

Parallel Algorithms For Steiner Forest, Laleh Ghalami

Wayne State University Dissertations

The Steiner Forest Problem is one of the fundamental combinatorial optimization problemsin operations research and computer science. Its applications range from network design to computational biology. Given an undirected graph with non-negative weights for edges and a set of pairs of vertices called terminals, the Steiner Forest Problem is to find the minimum cost subgraph that connects each of the terminal pairs together. The Steiner Forest Problem is APX-hard and NP-hard to approximate within 96/95. Several heuristic and approximation algorithms, with different approximation guarantees, have been proposed for the Steiner Forest Problem. Despite the several research in designing sequential and …

Deep Learning As Native Scientific Workflows In The Modern Swfms - Dataview, Junwen Liu

Wayne State University Dissertations

Scientific workflow has become a common practice for scientists to effectively formalize and structure complex scientific processes, which in turn has accelerated scientific discoveries in numerous research fields. With the recent thriving of deep learning in broad scientific projects, there is a rising need for deep learning support in scientific workflow infrastructures SWFMSs. However, current GPU-enabled deep learning frameworks are developed separately, not suitable for direct exploitation in SWFMSs, which forces scientists to handle deep learning outside of SWFMSs and then integrate in workflows in an ad-hoc manner. What workflow users pressingly need today is a user-friendly and well-integrated SWFMS …

Adversarial Machine Learning For Advanced Medical Imaging Systems, Xin Li

Wayne State University Dissertations

Although deep neural networks (DNNs) have achieved significant advancement in various challenging tasks of computer vision, they are also known to be vulnerable to so-called adversarial attacks. With only imperceptibly small perturbations added to a clean image, adversarial samples can drastically change models’ prediction, resulting in a significant drop in DNN’s performance. This phenomenon poses a serious threat to security-critical applications of DNNs, such as medical imaging, autonomous driving, and surveillance systems. In this dissertation, we present adversarial machine learning approaches for natural image classification and advanced medical imaging systems.

We start by describing our advanced medical imaging systems to …

Behavioral Predictive Analytics Towards Personalization For Self-Management – A Use Case On Linking Health-Related Social Needs, Bon Sy, Michael Wassil, Helene Connelly, Alisha Hassan

Publications and Research

The objective of this research is to investigate the feasibility of applying behavioral predictive analytics to optimize patient engagement in diabetes self-management, and to gain insights on the potential of infusing a chatbot with NLP technology for discovering health-related social needs. In the U.S., less than 25% of patients actively engage in self-health management even though self-health management has been reported to associate with improved health outcomes and reduced healthcare costs. The proposed behavioral predictive analytics relies on manifold clustering to identify subpopulations segmented by behavior readiness characteristics that exhibit non-linear properties. For each subpopulation, an individualized auto-regression model and …

A Novel Tropical Geometry-Based Interpretable Machine Learning Method: Pilot Application To Delivery Of Advanced Heart Failure Therapies, Heming Yao, Harm Derkson, Jessica R. Golbus, Justin Zhang, Keith D. Aaronson, Jonathan Gryak, Kayvan Najarian

Publications and Research

Abstract—A model’s interpretability is essential to many practical applications such as clinical decision support systems. In this paper, a novel interpretable machine learning method is presented, which can model the relationship between input variables and responses in humanly understandable rules. The method is built by applying tropical geometry to fuzzy inference systems, wherein variable encoding functions and salient rules can be discovered by supervised learning. Experiments using synthetic datasets were conducted to demonstrate the performance and capacity of the proposed algorithm in classification and rule discovery. Furthermore, we present a pilot application in identifying heart failure patients that are eligible …

The State Of Innovation And Media Viability In East Africa: From Indepth Media House Surveys, Hesbon Hansen Owilla, Rose Kimani, Ann Hollifield, Julia Wegner, Dennis Reineck, Roland Schürhoff

Graduate School of Media and Communications

Media houses globally are grappling with how best to produce quality content while at the same time remaining financially viable in the wake of shrinking revenues, technological disruptions, the emergence of peripheral content creators, competition for advertisement revenues from big tech platforms, the COVID-19 pandemic, and a myriad of other changes in the ecosystem. Despite these challenges, it is in the interest of the public that news media organisations (NMOs) produce quality content and do so in a financially sustainable fashion. Media viability, that is, producing quality journalism in a financially sustainable way, is, therefore, a growing area of focus. …

A Crash Course In Good And Bad Controls, Andrew Forney, Carlos Cinelli, Judea Pearl

Computer Science Faculty Works

Many students of statistics and econometrics express frustration with the way a problem known as “bad control” is treated in the traditional literature. The issue arises when the addition of a variable to a regression equation produces an unintended discrepancy between the regression coefficient and the effect that the coefficient is intended to represent. Avoiding such discrepancies presents a challenge to all analysts in the data intensive sciences. This note describes graphical tools for understanding, visualizing, and resolving the problem through a series of illustrative examples. By making this “crash course” accessible to instructors and practitioners, we hope to avail …

The Utility Of Electroencephalography For User Input, Caleb Maurice

Williams Honors College, Honors Research Projects

The goal of this paper is to introduce the use of noninvasive brain-computer interfaces to prospective computer scientists. Electroencephalography is explained starting with how a user’s brain waves are measured and ending with how the data is parsed to software programs. To further expand on the ability to implement electroencephalography into software code, and example of a simple game is given. This game is an endless runner, meaning that it has no end and stops once the player’s game piece collides with an object. It is coded in the Python computer language.

Predicting Pair Success In A Pair Programming Eye Tracking Experiment Using Cross-Recurrence Quantification Analysis, Maureen M. Villamor, Maria Mercedes T. Rodrigo

Department of Information Systems & Computer Science Faculty Publications

Pair programming is a model of collaborative learning. It has become a well-known pedagogical practice in teaching introductory programming courses because of its potential benefits to students. This study aims to investigate pair patterns in the context of pair program tracing and debugging to determine what characterizes collaboration and how these patterns relate to success, where success is measured in terms of performance task scores. This research used eye-tracking methodologies and techniques such as cross-recurrence quantification analysis. The potential indicators for pair success were used to create a model for predicting pair success. Findings suggest that it is possible to …

The Uses Of A Dual-Band Corrugated Circularly Polarized Horn Antenna For 5g Systems, Chih-Kai Liu, Wei-Yuan Chiang, Pei-Zong Rao, Pei-Hsiu Hung, Shih-Hung Chen, Chiung-An Chen, Liang-Hung Wang, Patricia Angela R. Abu, Shih-Lun Chen

Department of Information Systems & Computer Science Faculty Publications

This paper presents the development of a wide-beam width, dual-band, omnidirectional antenna for the mm-wave band used in 5G communication systems for indoor coverage. The 5G indoor environment includes features of wide space and short range. Additionally, it needs to function well under a variety of circumstances in order to carry out its diverse set of network applications. The waveguide antenna has been designed to be small enough to meet the requirements of mm-wave band and utilizes a corrugated horn to produce a wide beam width. Additionally, it is small enough to integrate with 5G communication products and is easy …

Non-Parametric Stochastic Autoencoder Model For Anomaly Detection, Raphael B. Alampay, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Anomaly detection is a widely studied field in computer science with applications ranging from intrusion detection, fraud detection, medical diagnosis and quality assurance in manufacturing. The underlying premise is that an anomaly is an observation that does not conform to what is considered to be normal. This study addresses two major problems in the field. First, anomalies are defined in a local context, that is, being able to give quantitative measures as to how anomalies are categorized within its own problem domain and cannot be generalized to other domains. Commonly, anomalies are measured according to statistical probabilities relative to the …

Construction Of A Repeatable Framework For Prostate Cancer Lesion Binary Semantic Segmentation Using Convolutional Neural Networks, Ian Vincent O. Mirasol, Patricia Angela R. Abu, Rosula Sj Reyes

Department of Information Systems & Computer Science Faculty Publications

Prostate cancer is the 3rd most diagnosed cancer overall. Current screening methods such as the prostate-specific antigen test could result in overdiagonosis and overtreatment while other methods such as a transrectal ultrasonography are invasive. Recent medical advancements have allowed the use of multiparametric MRI — a noninvasive and reliable screening process for prostate cancer. However, assessment would still vary from different professionals introducing subjectivity. While con-volutional neural network has been used in multiple studies to ob-jectively segment prostate lesions, due to the sensitivity of datasets and varying ground-truth established used in these studies, it is not possible to reproduce and …

Service Contracting As A Policy Response For Public Transport Recovery During The Covid-19 Pandemic: A Preliminary Evaluation, Varsolo Sunio, Wilhansen Joseph Li, Joemier Pontawe, Albert Dizon, Joel Bienne Valderrama, Agnes Robang

Department of Information Systems & Computer Science Faculty Publications

We examine and assess the service contracting (SC) program implemented for the first time in Metro Manila, Philippines as a response to the impact of the pandemic on road-based public transport sector. We develop an evaluation framework, consisting of three indicators: social amelioration, increase in transport supply and performance improvement. These indicators are the purported objectives of SC. Using a mix of qualitative and quantitative methods, our evaluation suggests that although SC has brought positive impact in terms of the first two indicators, there is no robust evidence so far that may suggest that SC has improved the performance of …

Dynamic Task Allocation And Understanding Of Situation Awareness Under Different Levels Of Autonomy In Closed-Hatch Military Vehicles Using Virtual Reality, Jessie E. Cossitt

Link Foundation Modeling, Simulation and Training Fellowship Reports

The current state of the art in autonomous vehicles is partial autonomy, and to optimize the use of these vehicles, it is essential to understand the interactions between the vehicles and their operators. Partially autonomous vehicles require operators to perform parts of the driving task and to be alert and ready to take over full control of the vehicle at any point. Due to the requirements on the human operator, it is necessary to know how the operators' abilities are impacted by the amount of autonomy present in the system. There are known effects of autonomous systems on performance, cognitive …

Correlations And Reuse For Fast And Accurate Physically Based Light Transport, Benedikt Bitterli

Dartmouth College Ph.D Dissertations

Light transport is the study of the transfer of light between emitters, surfaces, media and sensors. Fast simulations of light transport play a pivotal role in photo-realistic image synthesis, and find many applications today, including predictive manufacturing, machine learning, scientific visualization and the movie industry. In order to accurately reproduce the appearance of real scenes, light transport must closely approximate the physical laws governing the flow of light. Physically based rendering is a set of principles for codifying these laws into a mathematical model, and is the predominant rendering methodology today.

The representational power of this model is limited to …

Building An Understanding Of Human Activities In First Person Video Using Fuzzy Inference, Bradley A. Schneider

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Activities of Daily Living (ADL’s) are the activities that people perform every day in their home as part of their typical routine. The in-home, automated monitoring of ADL’s has broad utility for intelligent systems that enable independent living for the elderly and mentally or physically disabled individuals. With rising interest in electronic health (e-Health) and mobile health (m-Health) technology, opportunities abound for the integration of activity monitoring systems into these newer forms of healthcare. In this dissertation we propose a novel system for describing ’s based on video collected from a wearable camera. Most in-home activities are naturally defined by …