Physical Sciences and Mathematics Commons^™

Malware Classification Using Api Call Information And Word Embeddings, Sahil Aggarwal

Master's Projects

Malware classification is the process of classifying malware into recognizable categories and is an integral part of implementing computer security. In recent times, machine learning has emerged as one of the most suitable techniques to perform this task. Models can be trained on various malware features such as opcodes, and API calls among many others to deduce information that would be helpful in the classification.

Word embeddings are a key part of natural language processing and can be seen as a representation of text wherein similar words will have closer representations. These embeddings can be used to discover a quantifiable …

Analysis Of Public Sentiment Of Covid-19 Pandemic, Vaccines, And Lockdowns, Devinesh Singh

Master's Projects

CoV-2 pandemic prompted lockdown measures to be implemented worldwide; these directives were implemented nationwide to stunt the spread of the infection. Throughout the lockdowns, millions of individuals resorted to social media for entertainment, communicate with friends and family, and express their opinions about the pandemic. Simultaneously, social media aided in the dissemination of misinformation, which has proven to be a threat to global health. Sentiment analysis, a technique used to analyze textual data, can be used to gain an overview of public opinion behind CoV-2 from Twitter and TikTok. The primary focus of the project is to build a deep …

Low Power Mobilenets Acceleration In Cuda And Opencl, Nikhil Lahoti

Master's Projects

Convolutional Neural Network (CNN) has been used widely for the tasks of object recognition and facial recognition because of their remarkable results on these common visual tasks. In order to evaluate the performance of CNN for embedded devices effectively, it is essential to provide a comprehensive benchmark evaluation environment. Even though there are many benchmark suites available for use, but these benchmark suites require installation of various packages and proprietary libraries. This creates a bottleneck in using them in applications which are executed on resource constraint devices like embedded devices.

In this paper, we propose an evaluation platform which can …

Detecting Cars In A Parking Lot Using Deep Learning, Samuel Ordonia

Master's Projects

Detection of cars in a parking lot with deep learning involves locating all objects of interest in a parking lot image and classifying the contents of all bounding boxes as cars. Because of the variety of shape, color, contrast, pose, and occlusion, a deep neural net was chosen to encompass all the significant features required by the detector to differentiate cars from not cars. In this project, car detection was accomplished with a convolutional neural net (CNN) based on the You Only Look Once (YOLO) model architectures. An application was built to train and validate a car detection CNN as …

Classification Of Malware Models, Akriti Sethi

Master's Projects

Automatically classifying similar malware families is a challenging problem. In this research, we attempt to classify malware families by applying machine learning to machine learning models. Specifically, we train hidden Markov models (HMM) for each malware family in our dataset. The resulting models are then compared in two ways. First, we treat the HMM matrices as images and experiment with convolutional neural networks (CNN) for image classification. Second, we apply support vector machines (SVM) to classify the HMMs. We analyze the results and discuss the relative advantages and disadvantages of each approach.