Digital Commons Network^™

Application Of Adversarial Attacks On Malware Detection Models, Vaishnavi Nagireddy

Master's Projects

Malware detection is vital as it ensures that a computer is safe from any kind of malicious software that puts users at risk. Too many variants of these malicious software are being introduced everyday at increased speed. Thus, to guarantee security of computer systems, huge advancements in the field of malware detection are made and one such approach is to use machine learning for malware detection. Even though machine learning is very powerful, it is prone to adversarial attacks. In this project, we will try to apply adversarial attacks on malware detection models. To perform these attacks, fake samples that …

Real Time Panoramic Image Processing, Matthew Gerlits

Master's Projects

Image stitching algorithms are able to join sets of images together and provide a wider field of a vision when compared with an image from a single standard camera. Traditional techniques for accomplishing this are able to adequately produce a stitch for a static set of images, but suffer when differing lighting conditions exist between the two images. Additionally, traditional techniques suffer from processing times that are too slow for real time use cases. We propose a solution which resolves the issues encountered by traditional image stitching techniques. To resolve the issues with lighting difference, two blending schemes have been …

Image-Based Classification Of Malware Using T-Sne Images, Vincent Stowbunenko

Master's Projects

This Master’s project proposes a novel technique for classifying malware using image-based methods. The approach involves generating t-SNE images from the EMBER dataset, which contains one million samples of both malware and benign files, each represented by over 2,000 features. The t-SNE technique is well-suited for capturing intricate patterns in complex datasets because it effectively maintains the local structure. These t-SNE images are then used as inputs to train two lightweight image classification models, SqueezeNet and MobileNet. Additionally, to provide a benchmark for comparison, a non-image classification model using LightGBM is also explored.

As part of the investigation, the project …

A Natural Language Processing Approach To Malware Classification, Ritik Mehta

Master's Projects

Many different machine learning and deep learning techniques have been successfully employed for malware detection and classification. Examples of popular learning techniques in the malware domain include Hidden Markov Models (HMM), Random Forests (RF), Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Recurrent Neural Networks (RNN) such as Long Short-Term Memory (LSTM) networks. In this research, we consider a hybrid architecture, where HMMs are trained on opcode sequences, and the resulting hidden states of these trained HMMs are used as feature vectors in various classifiers. In this context, extracting the HMM hidden state sequences can be viewed as a …

On The Memory Scalability Of Spectral Clustering Algorithms, Ran Li

Master's Projects

Spectral clustering has lots of advantages compared to previous more traditional clustering methods, such as k-means and Gaussian Mixture Models (GMM), and is popular since it was introduced. However, there are two major challenges, speed scalability and memory scalability, that impede the wide applications of spectral clustering. The first challenge has been addressed recently by Chen [1] [2] in the special setting of sparse or low dimensional data sets. In this work, we will first review the recent study by Chen that speeds up spectral clustering. Then we will propose three new computational methods for the same special setting of …

Efficient Video Qoe Prediction In Intelligent O-Rans, Aditya Kulkarni

Master's Projects

Open Radio Access Network (O-RAN) is a platform developed by a collaboration between wireless operators, infrastructure vendors, and service providers for deploying mobile fronthaul and midhaul networks, built entirely on cloud-native principles. The vision of O-RAN lies in the virtualization of traditional wireless infrastructure components, like Central Units (CU), Radio Units (RU), and Distributed Units (DU). O-RAN decouples the above-mentioned wireless infrastructure components into opensource elements, operating consistently with other elements of different vendors in the network. Quality of Experience (QoE) deals with a user’s subjective measure of satisfaction. RAN Intelligent Controller (RIC) in O-RAN provides flexibility to intelligently program …

Gender Classification Via Human Joints Using Convolutional Neural Network, Cheng-En Sung

Master's Projects

With the growing demand for gender-related data on diverse applications, including security systems for ascertaining an individual’s identity for border crossing, as well as marketing purposes of digging the potential customer and tailoring special discounts for them, gender classification has become an essential task within the field of computer vision and deep learning. There has been extensive research conducted on classifying human gender using facial expression, exterior appearance (e.g., hair, clothes), or gait movement. However, within the scope of our research, none have specifically focused gender classification on two-dimensional body joints. Knowing this, we believe that a new prediction pipeline …

Dynamic Predictions Of Thermal Heating And Cooling Of Silicon Wafer, Hitesh Kumar

Master's Projects

Neural Networks are now emerging in every industry. All the industries are trying their best to exploit the benefits of neural networks and deep learning to make predictions or simulate their ongoing process with the use of their generated data. The purpose of this report is to study the heating pattern of a silicon wafer and make predictions using various machine learning techniques. The heating of the silicon wafer involves various factors ranging from number of lamps, wafer properties and points taken in consideration to capture the heating temperature. This process involves dynamic inputs which facilitates the heating of the …

Security And Routing In A Disconnected Delay Tolerant Network, Anirudh Kariyatil Chandakara

Master's Projects

Providing internet access in disaster-affected areas where there is little to no internet connectivity is extremely difficult. This paper proposes an architecture that utilizes existing hardware and mobile applications to enable users to access the Internet while maintaining a high level of security. The system comprises a client application, a transport application, and a server running on the cloud. The client combines data from all supported applications into a single bundle, which is encrypted using an end-to-end encryption technique and sent to the transport. The transport physically moves the bundles to a connected area and forwards them to the server. …

Deep Learning Neural Machine Translation Conversational Agent, Abhishek Vaid

Master's Projects

Neural Machine Translation (NMT) is a prominent natural language processing technique that is being used to develop conversational AI technology. However, most chatbots do not provide live API features and have list-based scripted responses. Most chatbots are majorly restricted by the training data on which they were trained on and have no knowledge of current events. This research project intends to research and develop an approach to providing live information. We experiment with various techniques in terms of the type of data being used to harness live capabilities. We optimize the hyperparameters that are needed for a Conversational AI agent …

Job Tailored Resume Content Generation, Sumedh Kale

Master's Projects

Generally candidates apply to multiple jobs with a single resume and do not tend to customize their resume to match the job description. This hampers their chances of getting a resume shortlisted for the job. The project aims to help such candidates build job tailored resumes that help them create a customized and targeted resume for a specific job or industry. The tool specifically targets candidates’ employment history, for resume content generation. We then use natural language processing

(NLP) techniques to extract and organize this data into a structured format for the dataset. We experiment with multiple variations of the …

Image-Based Malware Detection Using Convolutional Neural Network Techniques, Brandon Palomino

Master's Projects

In this study, we delve into the realm of malware detection and classification, leveraging the capabilities of different Convolutional Neural Networks (CNNs). Our approach involves transforming executable files into image formats and subsequently applying advanced CNN techniques for image recognition. The study emphasizes the use of two distinct CNN architectures: a traditional CNN model and a modified CNN variant known as a convolutional recurrent neural network (CRNN), each with unique structural and functional attributes. To effectively train these models, we adopt a transfer learning strategy, utilizing pre-existing CNN models that have been extensively trained on large-scale image datasets. This methodology …

Advances In Robustness Of Image-Based Malware Detection, Rishika Pamanji

Master's Projects

In recent years, deep learning has emerged as a powerful tool for image classification tasks. However, the performance of individual deep learning models can be limited by their architecture and training data. In this project, various Convolutional Neural Network (CNN) architectures are proposed to train the malware data for feature extraction for various color coordinates such as L, CMYK, RGB, RGBA, and YCbCr. Different optimization techniques like Stochastic Gradient Descent, Root Mean Square Propagation, Ada Delta, Adam, and Adaptive Gradient are used to minimize errors in the trained data, leading to enhanced accuracy. The proposed ensemble deep learning model for …

Spartandark: Anonymity Model Integration With A Blockchain Network Using Spartangold, Nishanth Uchil

Master's Projects

Demand for blockchain ecosystems has seen exponential growth in recent times due to its decentralized nature and trustless verification process for the transactions involved. However, transaction data needs to be leveraged for verification, which coupled with the transparent nature of the blockchain ledger, provides sufficient data for malicious entities to reveal identities and even financial history of users. Data masking techniques have been employed over the years to make blockchain transactions anonymous, making them resistant to identity analysis, a key set of methods being zero-knowledge proof (zk-proof) protocols that guarantee zero data leak. In this research, we develop SpartanDark, a …

Pygrapherconnect, Shubham Jain

Master's Projects

The evolving landscape of backend computational systems especially in biomedical research involving heavy data operations which have a gap of not being used properly. It is due to the lack of communication standard between the frontend and backend. This gap presents a problem to researchers who need to use the frontend for visualizing and manipulating their data but also want to do complex analysis. CAPRI a python-based backend system specializing in analyzing Evidential Reasoning data also has the same issue. This project offers a solution PyGrapherConnect module acting as a data conversion layer between CAPRI and PyGrapher, its frontend interface. …

Estimating Air Pollution Levels Using Machine Learning, Srujay Rao Devaraneni

Master's Projects

Air pollution has emerged as a substantial concern, especially in developing countries worldwide. An important aspect of this issue is the presence of PM2.5. Air pollutants with a diameter of 2.5 or less micrometers are known as PM2.5. Due to their size, these particles are a serious health risk and can quickly infiltrate the lungs, leading to a variety of health problems. Due to growing concerns about air pollution, technology like automatic air quality measurement can offer beneficial assistance for both personal and business decisions. This research suggests an ensemble machine learning model that can efficiently replace the standard air …

An Incentivization Mechanism Towards Network-Slice As A Service, Yosha Mundhra

Master's Projects

Next-generation communication networks are expected to support many different types of services. Network slicing virtually divides one physical network into several virtual networks in order to accommodate diverse demands for network connections. Assuming multiple network domains are stitched together to provide end-to-end network slices for such diverse services, it is critical to consider the economic behaviors and revenue models of the stakeholders. This project analyzes the economic behaviors from the perspective of a Global Slice Coordinator (GSC) that receives a slice request and coordinates the end-to-end resource allocations across multiple network domains. The primary role of the GSC is to …

Xai-Driven Cnn For Diabetic Retinopathy Detection, Vikas Shenoy Pete

Master's Projects

Diabetes, a chronic metabolic disorder, poses a significant health threat with potentially severe consequences, including diabetic retinopathy, a leading cause of blindness. In this project, we tackle this threat by developing a Convolutional Neural Network (CNN) to support the diagnosis based on eye images. The aim is early detection and intervention to mitigate the effects of diabetes on eye health. To enhance transparency and interpretability, we incorporate explainable AI techniques. This research not only contributes to the early diagnosis of diabetic eye disease but also advances our understanding of how deep learning models arrive at their decisions, fostering trust and …

On Measuring The Degree Of Resource Isolation In Elastic Network Slicing, Nitin Datta Movva

Master's Projects

With the growing popularity of 5G networks and their vast use cases, users must be provided with the services they request. Network slicing is a technique for establishing numerous distinct logical and virtualized networks over a shared multi-domain infrastructure. In particular, the emerging elastic slicing, which allows users to scale up or down the reserved amount of resources during the slice life cycle, is a promising technique to accommodate diverse network services more affordably. However, users may sometimes need more resources, which is impossible to provide due to the abundance of users and lack of additional resources. To our knowledge, …

Web Traffic Time Series Forecasting, Summanth Redde Mulkkalla

Master's Projects

Online web traffic forecasting is one of the most crucial elements of maintaining and improving websites and digital platforms. Traffic patterns usually predict future online traffic, including page views, unique visitors, session duration, and bounce rates. However, it is challenging to forecast non-stationary online web traffic, particularly when the data has spikes or irregular patterns. This non-stationary property demands a more advanced forecasting technique. In this study, we provide a neural networkbased method, Spiking Neural Networks (SNNs), for dealing with the data spikes and irregular patterns in non-stationary data. In our study, we compared the forecasting results of SNNs with …

When She Speaks: A Black Feminist Autoethnography Exploring Joy And Positionality In Higher Education, Leslye M. Tinson

Dissertations

This dissertation examines the intersections of race, class, and gender of a Black woman as an educational leader and doctoral student. Through autoethnography, I explore self-meaning in cultural contexts and focus on how my positionality contributes to my attempts to find joy in academe. Chapter I discusses the gender and racial gaps in the American professoriate and introduces three research questions. Chapter II interprets extant literature and identifies a conceptual framework that utilizes Black feminist thought, critical race feminism, intersectionality theory, and multicultural feminist perspectives to analyze my lived experiences. Chapter III outlines the research methodology of autoethnography to address …

Vehicle-Based Disconnected Data Distribution, Aditya Singhania

Master's Projects

The world today is highly connected and there is an immense dependency on this connectivity to accomplish basic everyday tasks. However much of the world lacks connectivity. Even in well-connected locations, natural disasters can cause infrastructure disruption. To combat these situations, Delay Tolerant Networks

(DTNs) employ to store and forward techniques along with intermittently connected transports to provide data connectivity. DTNs focus on intermittently connected networks however what if the regions are never connected? For example, Region A - is never connected to the internet, and Region B – has internet connectivity. Using a vehicle that travels between the two …

A Novel Efficient Deep Learning Framework For Facial Inpainting - Face Reconstruction From Masked Images, Akshay Ravi

Master's Projects

The use of face masks due to the covid-19 pandemic has made surveillance of people very difficult. Since a mask covers most of the facial components, security cameras are rendered of little to no use in the identification of criminals. In order to realize what a face looks like behind a mask, we have to construct the facial features in the masked region. On a higher level, this falls under the field of image inpainting, i.e. filling missing regions of images or correcting irregularities in images. Current research on image inpainting shows promising results on images that have missing/incorrect patches …

Eye Movements Behaviors In A Driving Simulator During Simple And Complex Distractions, Pradeep Narayana

Master's Projects

Road accidents occur frequently due to driving distractions all around the world. A driving simulator has been created to explore the cognitive effects of distractions while driving in order to address this problem. The purpose of this study is to discover the distraction-causing elements and how they affect driving performance. The simulator offers a secure and regulated setting for carrying out tests while being distracted by different visual distractions, such as solving mathematical equations and number memorizations.

Several trials have been conducted in the studies, which were carried out under varied circumstances like varying driving sceneries and by displaying different …

Twitter Bot Detection Using Nlp And Graph Classification, Warada Jayant Kulkarni

Master's Projects

Social media platforms are one of the primary resources for information as it is easily accessible, low in cost, and provides a high rate of information spread. Online social media (OSM) have become the main source of news information around the world, but because of the distributed nature of the web, it has increased the risk of fake news spread. Fake news is misleading information that is published as real news. Therefore, identifying fake news and flagging them as such, as well as detecting sources that generate them is an ongoing task for researchers and OSM companies. Bots are artificial …

Teaching Health Justice And Reimagining Narratives Of Place Through Community-Driven Science Practices, Tammie Visintainer

Faculty Research, Scholarly, and Creative Activity

This study explores how a biology teacher from a summer science program engaged high school students of color in a three-week science unit exploring community health at the intersection of history, race, place, and power. The goal of this study is to better understand what community-driven science looks like in a science classroom when a health equity unit is guided by a biology teacher who engages the socio-historical, political, and relational aspects of community structure and agency. Using student and instructor interviews, program observations, and student artifacts, I examine how the instructor's positioning as a Black woman scientist shaped her …

Evaluation Of Left Shoulder As Part-Time Travel Lane Design Alternatives And Transportation Management Center Staff Training Module Development, Zhuohang Deng, Zhiliang Luo, Neil Hockaday, Ahmed Farid, Anurag Pande

Mineta Transportation Institute

Permanent capacity expansion, such as adding new lanes, is no longer a viable strategy to address traffic congestion in California; hence, ITS (Intelligent Transportation System) strategies, such as part-time use of the shoulder as a travel lane, need to be explored. The use of the shoulder as a travel lane during peak traffic hours has limited applications in the US, and most use the right shoulder as a part-time travel lane even though either the right or left shoulder (but not both) may be used. Caltrans District 5 is exploring the use of Left Shoulder as a Part-time Travel Lane …

Maximizing Technology Acceptance Model In Accessing The Attitude Of Rural Farmers Using Ict Tools In Farming To Enhance Productivity, Francis Yao Anyan Dr., Michael Oppong Mr., Stephanie O. Ansah Mrs., Millicent Yengkangyi Ms., Akosua Boatemaa Adarkwa Mrs

Library Philosophy and Practice (e-journal)

The study was conducted using a multi-stage sampling technique involving simple random sampling, a probability sampling method, purposive sampling, and snowball sampling, which are non-probability methods. Two regions, namely, the Greater Accra and Eastern were purposively selected for the study. In the Greater Accra Region, Ada East District was selected while in the Eastern Region Asuogyaman District. In Greater Accra, the study was conducted in six farming villages namely: Big Ada, Dogo, Kasseh, Addokope, Korlekope, and Bedeku. In Eastern regions, the study was conducted in Asogyaman, where Tortibo, Sappor, Yenease, Adina Donor, and Ankyease. A snowball strategy was relied on …

Shifting Mediates Gendered Racial Microaggressions And Perceived Racism Among Asian American Women, Glenn Gamst, Christine Ma-Kellams, Lawrence S. Meyers, Leticia Arellano-Morales

Faculty Research, Scholarly, and Creative Activity

Introduction: Microaggressive attacks on Asian American women increased during the COVID-19 pandemic. The present study tested whether Asian American women's shifting, a coping strategy employed by some women of color to alter their self-presentation in response to perceived racism, mediated the association between gendered racial microaggressions and self-perceived subtle and blatant racism. Methods: A convenience sample of 253 Asian American adult women completed the gendered racial microaggressions scale for Asian American women (GRMSAAW), the Asian American women's shifting scale (AsAWSS), and the subtle and blatant racism scale for Asian American college students (SABR-A2). Results: Results from a structural equation model …

Influence Of Advocacy And Awareness Programmes On Use Of Digital Library Resources And Services By Postgraduate Students In Three Federal Universities In South-West, Nigeria, Temitope Abegunde, Olalekan Abiola Awujoola, Mary Love Bamidele

Library Philosophy and Practice (e-journal)

Digital Library Resources and Services (DLRS) are the modern advancement in information technology. These resources help to get precise and current information, especially for postgraduates who depend largely on digital resources for information to enhance their leaning, research as well as to collaborate with other scholars all over the globe for intellectual advancement. Many federal universities do not organise effective awareness and advocacy programmes to get enough digital library resources by postgraduates. Thus, this study investigates advocacy and awareness programmes on use of digital library resources and services by postgraduates in federal universities in South-west, Nigeria. The descriptive survey design …