Computer Sciences Commons^™

Malware Analysis With Auxiliary-Classifier Gan, Rakesh Nagaraju

Master's Projects

A generative adversarial network (GAN) is a powerful machine learning concept where both a generative and discriminative model are trained simultaneously. A recent trend in malware research consists of treating executables as images and employing image-based analysis techniques. In this research, we generate fake malware images using GANs, and we also consider the effectiveness of GANs for malware classification. Specifically, we consider auxiliary classifier GAN (AC-GAN), which enables us to work with multiclass data. We find that AC-GAN generates malware images that cannot be reliably distinguished from real malware images. In addition, we find that the detection capabilities of AC-GAN …

Presentation Attack Detection In Facial Biometric Authentication, Hardik Kumar

Master's Projects

Biometric systems are referred to those structures that enable recognizing an individual, or specifically a characteristic, using biometric data and mathematical algorithms. These are known to be widely employed in various organizations and companies, mostly as authentication systems. Biometric authentic systems are usually much more secure than a classic one, however they also have some loopholes. Presentation attacks indicate those attacks which spoof the biometric systems or sensors. The presentation attacks covered in this project are: photo attacks and deepfake attacks. In the case of photo attacks, it is observed that interactive action check like Eye Blinking proves efficient in …

Machine Learning To Detect Malware Evolution, Lolitha Sresta Tupadha

Master's Projects

Malware evolves over time and anti-virus must adapt to such evolution. Hence, it is critical to detect those points in time where malware has evolved so that appro-priate countermeasures can be undertaken. In this research, we perform a variety of experiments to determine when malware evolution is likely to have occurred. All of the evolution detection techniques that we consider are based on machine learning and can be fully automated—in particular, no reverse engineering or other labor-intensive manual analysis is required. Specifically, we consider analysis based on hidden Markov models and various word embedding techniques, among other machine learning based …

Computer-Aided Diagnosis Of Low Grade Endometrial Stromal Sarcoma (Lgess), Xinxin Yang

Master's Projects

Low grade endometrial stromal sarcoma (LGESS) is rare form of cancer, account- ing for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients are initially misdiagnosed with leiomyoma, which is a type of benign tumor that is also known as fibroids. In this research, uterine tissue biopsy images of potential LGESS patients are preprocessed using segmentation and staining normalization algorithms. A wide variety of classic machine learning and leading deep learning models are then applied to classify tissue images as either benign or cancerous. For classic techniques, the highest classification accuracy we attain is 85%, while our …

Hidden Markov Model-Based Clustering For Malware Classification, Shamli Singh

Master's Projects

Automated techniques to classify malware samples into their respective families are critical in cybersecurity. Previously research applied ��-means clustering to scores generated by hidden Markov models (HMM) as a means of dealing with the malware classification problem. In this research, we follow a somewhat similar approach, but instead of using HMMs to generate scores, we directly cluster the HMMs themselves. We obtain good results on a challenging malware dataset.

Data Augmentation With Malware As Images, Aditi Walia

Master's Projects

Machine learning and deep learning techniques for malware detection and classifi- cation play an important role in the mitigation of cybersecurity threats. However, such techniques are often limited by a lack of data. Previous research has shown promising classification results by treating malware executables as images. In this research, we consider data augmentation using noise addition, geometric transforma- tions, and Auxiliary Classifier Generative Adversarial Networks (AC-GAN) for data augmentation of malware images. We train convolution neural networks (CNN) to verify that our generated images accurately model the original malware samples.

Overlapping Community Detection In Social Networks, Akshar Panchal

Master's Projects

Social networking sites are important to connect with the world virtually. As the number of users accessing these sites increase, the data and information keeps on increasing. There are communities and groups which are formed virtually based on different factors. We can visualize these communities as networks of users or nodes and the relationships or connections between them as edges. This helps in evaluating and analyzing different factors that influence community formation in such a dense network. Community detection helps in revealing certain characteristics which makes these groups in the network unique and different from one another. We can use …

Fake News Analysis And Graph Classification On A Covid-19 Twitter Dataset, Kriti Gupta

Master's Projects

Earlier researches have showed that the spread of fake news through social media can have a huge impact to society and also to individuals in an extremely negative way. In this work we aim to study the spread of fake news compared to real news in a social network. We do that by performing classical social network analysis to discover various characteristics, and formulate the problem as a binary classification, where we have graphs modeling the spread of fake and real news. For our experiments we rely on how news are propagated through a popular social media services such as …

Classifying Illegal Advertisements On The Darknet Using Nlp, Karan Shashin Shah

Master's Projects

The Darknet has become a place to conduct various illegal activities like child labor, contract murder, drug selling while staying anonymous. Traditionally, international and government agencies try to control these activities, but most of those actions are manual and time-consuming. Recently, various researchers developed Machine Learning (ML) approaches trying to aid in the process of detecting illegal activities. The above problem can benefit by using different Natural Language Processing (NLP) techniques. More specifically, researchers have used various classical topic modeling techniques like bag of words, N-grams, Term Frequency, Term Frequency Inverse Document Frequency (TF-IDF) to represent features and train machine …

Fake Malware Classification With Cnn Via Image Conversion: A Game Theory Approach, Yash Sahasrabuddhe

Master's Projects

Improvements in malware detection techniques have grown significantly over the past decade. These improvements have resulted in better security for systems from various forms of malware attacks. However, it is also the reason for continuous evolution of malware which makes it harder for current security mechanisms to detect them. Hence, there is a need to understand different malwares and study classification techniques using the ever-evolving field of machine learning. The goal of this research project is to identify similarities between malware families and to improve on classification of malwares within different malware families by implementing Convolutional Neural Networks (CNNs) on …

Keystroke Dynamics For User Authentication With Fixed And Free Text, Jianwei Li

Master's Projects

YouTube videos often include captivating descriptions and intriguing thumbnails designed to increase the number of views, and thereby increase the revenue for the person who posted the video. This creates an incentive for people to post clickbait videos, in which the content might deviate significantly from the title, description, or thumbnail. In effect, users are tricked into clicking on clickbait videos. In this research, we consider the challenging problem of detecting clickbait YouTube videos. We experiment with multiple state of the art machine learning techniques and a variety of textual features.

Higher-Order Link Prediction Using Node And Subgraph Embeddings, Kalpnil Anjan

Master's Projects

Social media, academia collaborations, e-commerce websites, biological structures, and other real-world networks are modeled as graphs to represent their entities and relationships in an abstract way. Such graphs are becoming more complex and informative, and by analyzing them we can solve various problems and find hidden insights. Some applications include predicting relationships and potential links between nodes, classifying nodes, and finding the most influential nodes in the graph, etc.

A large amount of research is being done in the field of predicting links between two nodes. However, predicting a future relationship among three or more nodes in a graph is …

Cyberbullying Classification Based On Social Network Analysis, Anqi Wang

Master's Projects

With the popularity of social media platforms such as Facebook, Twitter, and Instagram, people widely share their opinions and comments over the Internet. Exten- sive use of social media has also caused a lot of problems. A representative problem is Cyberbullying, which is a serious social problem, mostly among teenagers. Cyber- bullying occurs when a social media user posts aggressive words or phrases to harass other users, and that leads to negatively affects on their mental and social well-being. Additionally, it may ruin the reputation of that media. We are considering the problem of detecting posts that are aggressive. Moreover, …

Wildfire Risk Prediction For A Smart City, Rekha Rani

Master's Projects

Wildfires are uncontrolled fires that may lead to the destruction of biodiversity, soil fertility, and human resources. There is a need for timely detection and prediction of wildfires to minimize their disastrous effects. In this research, we propose a wildfire prediction model that relies on multi-criteria decision making (MCDM) to explicitly evaluates multiple conflicting criteria in decision making and weave the wildfire risks into the city’s resiliency plan. We incorporate fuzzy set theory to handle imprecision and uncertainties. In the process, we create a new data set that includes California cities’ weather, vegetation, topography, and population density records. The model …

Image-Based Real Estate Appraisal Using Cnns And Ensemble Learning, Prathamesh Dnyanesh Kumkar

Master's Projects

Real Estate Appraisal is performed to evaluate properties during a range of activities like buying, selling, mortgaging, or insuring. Traditionally, this process is done by real estate brokers who consider factors like the location of a house, its area, the number of bedrooms and bathrooms, along with other amenities to assess the property. This approach is quite subjective since different brokers may arrive at a different quote for the same property depending on their analysis. The development in machine learning algorithms has given rise to several Automated Valuation Models (AVMs) to estimate real estate prices. Real estate websites use such …

Sound In Video Games: How Sound Is An Important Aspect Of The Virtual Experience, James Boen

ART 108: Introduction to Games Studies

This paper will take the form of an analysis, with video games as the medium/text that will be analysed. Although analysis is typically reserved for poems, books, short stories, or plays, video games are simply a form of conveying ideas and a form of text that is representative of the 21st century. Video games is a rare medium that has an interactive element, which can alter/enhance the experience an audience member can have, even if there were the same audio/visual components in a film or play. In most forms of media with an audio component, the analysis is done by …

Translating Natural Language Queries To Sparql, Shreya Satish Bhajikhaye

Master's Projects

The Semantic Web is an extensive knowledge base that contains facts in the form of RDF
triples. These facts are not easily accessible to the average user because to use them requires
an understanding of ontologies and a query language like SPARQL. Question answering systems
form a layer of abstraction on linked data to overcome these issues. These systems allow the
user to input a question in a natural language and receive the equivalent SPARQL query. The
user can then execute the query on the database to fetch the desired results. The standard
techniques involved in translating natural language questions …

Detecting And Predicting Visual Affordance Of Objects In A Given Environment, Bhumika Kaur Matharu

Master's Projects

The rapid growth of the development of autonomous robots is transforming the manufacturing and healthcare industry in many ways, but they still face many challenges. One of the challenges experienced by autonomous robots is their inability to manipulate an unknown object without human supervision. One way through which autonomous robots can manipulate an unknown object is affordance learning [1]. Affordance describes the action a user can perform on the object in given surroundings. This report describes our proposed model to detect and predict the affordance of an object from videos by leveraging the spatial-temporal feature extraction through ConvLSTM and Fully …

Machine Learning Using Serverless Computing, Vidish Naik

Master's Projects

Machine learning has been trending in the domain of computer science for quite some time. Newer and newer models and techniques are being developed every day. The adoption of cloud computing has only expedited the process of training machine learning. With its variety of services, cloud computing provides many options for training machine learning models. Leveraging these services is up to the user. Serverless computing is an important service offered by cloud service providers. It is useful for short tasks that are event-driven or periodic. Machine learning training can be divided into short tasks or batches to take advantage of …

A Hybrid Gaze Pointer With Voice Control, Indhuja Ravi

Master's Projects

Accessibility in technology has been a challenge since the beginning of the 1800s. Starting with building typewriters for the blind by Pellegrino Turri to the on-screen keyboard built by Microsoft, there have been several advancements towards assistive technologies. The basic tools necessary for anyone to operate a computer are to be able to navigate the device, input information, and perceive the output. All these three categories have been undergoing tremendous advancements over the years. Especially, with the internet boom, it has now become a necessity to point onto a computer screen. This has somewhat attracted research into this particular area. …

Automating Text Encapsulation Using Deep Learning, Anket Sah

Master's Projects

Data is an important aspect in any form be it communication, reviews, news articles, social media data, machine or real-time data. With the emergence of Covid-19, a pandemic seen like no other in recent times, information is being poured in from all directions on the internet. At times it is overwhelming to determine which data to read and follow. Another crucial aspect is separating factual data from distorted data that is being circulated widely. The title or short description of this data can play a key role. Many times, these descriptions can deceive a user with unwanted information. The user …

Defending Vehicles Against Cyberthreats: Challenges And A Detection-Based Solution, Qilin Liu

Master's Projects

The lack of concern with security when vehicular network protocols were designed some thirty years ago is about to take its toll as vehicles become more connected and smart. Today as demands for more functionality and connectivity on vehicles continue to grow, a plethora of Electronic Control Units (ECUs) that are able to communicate to external networks are added to the automobile networks. The proliferation of ECU and the increasing autonomy level give drivers more control over their vehicles and make driving easier, but at the same time they expand the attack surface, bringing more vulnerabilities to vehicles that might …

Using Oracle To Solve Zookeeper On Two-Replica Problems, Ching-Chan Lee

Master's Projects

The project introduces an Oracle, a failure detector, in Apache ZooKeeper and makes it fault-tolerant in a two-node system. The project demonstrates the Oracle authorizes the primary process to maintain the liveness when the majority’s rule becomes an obstacle to continue Apache ZooKeeper service. In addition to the property of accuracy and completeness from Chandra et al.’s research, the project proposes the property of see to avoid losing transactions and the property of mutual exclusion to avoid split-brain issues. The hybrid properties render not only more sounder flexibility in the implementation but also stronger guarantees on safety. Thus, the Oracle …

American Sign Language Assistant, Charulata Lodha

Master's Projects

Our implementation of a prototype computer vision system to help the deaf and mute
communicate in a shopping setting. Our system uses live video feeds to recognize American Sign Language (ASL) gestures and notify shop clerks of deaf and mute patrons’ intents. It generates a video dataset in the Unity Game Engine of 3D humanoid models in a shop setting performing ASL signs. Our system uses OpenPose to detect and recognize the bone points of the human body
from the live feed. The system then represents the motion sequences as high dimensional skeleton joint point trajectories followed by a time-warping …

Visual And Lingual Emotion Recognition Using Deep Learning Techniques, Akshay Kajale

Master's Projects

Emotion recognition has been an integral part of many applications like video games, cognitive computing, and human computer interaction. Emotion can be recognized by many sources including speech, facial expressions, hand gestures and textual attributes. We have developed a prototype emotion recognition system using computer vision and natural language processing techniques. Our goal hybrid system uses mobile camera frames and features abstracted from speech named Mel Frequency Cepstral Coefficient (MFCC) to recognize the emotion of a person. To acknowledge the emotions based on facial expressions, we have developed a Convolutional Neural Network (CNN) model, which has an accuracy of 68%. …

Has Excessive Violence In Video Games Gone Too Far?, Kyra Sycip

ART 108: Introduction to Games Studies

Numerous case studies and published research have led many gamers and non-gamers to wonder whether the excessive loads of violence found in video games is truly necessary for “fun” gameplay and entertainment. Controversies have been arising within famous video games such as the Grand Theft Auto series, Call Of Duty: Modern Warfare 2, and Six Days in Fallujah. These three games have been the subject of numerous present day debates and have sparked many arguments within the gaming community. As well as the debate of whether these games are indeed harmful to the player’s psychology and nature has yet to …

How This War Of Mine Creates Empathy For Virtual Characters, Cole Pergerson

ART 108: Introduction to Games Studies

Is it possible to empathize with virtual characters? Are game characters just a means for game and story progress or can we develop a strong emotional connection with them? In this research essay, I analysis This War of Mine, a game about surviving a worn torn city where food is limited, and the player must make difficult moral decisions survive. The player controls a small group of characters who all need to be feed, get enough sleep, may need medical care, and other human needs. To look how This War of Mine creates empathy for the virtual characters, I will …

Human/Artificial Intelligence Coordination In Video Games, Michael Rodriguez

ART 108: Introduction to Games Studies

The emergence of video games has led to widespread inventions to enhance the reality of the experience. As a result, Artificial Intelligence (A.I.) was developed to create virtual experiences and attract a variety of players of video games. This paper will discuss video games in the context of Human-A.I. interaction and the importance of human coordination in video games. Unprecedented errors have been a common challenge in this relationship. An excellent example of these algorithms include population-based training and self-play, which have gained a lot of interest in video games. A.I. technology has surpassed human ability because they are simply …

Real-Time Monitoring Of Fdm 3d Printer For Fault Detection Using Machine Learning: A Bibliometric Study, Vaibhav Kisan Kadam, Satish Kumar, Arunkumar Bongale

Library Philosophy and Practice (e-journal)

Additive Manufacturing has wide application range including healthcare, Fashion, Manufacturing, Prototypes, Tooling etc. AM techniques are subjected to various defects that may be printing defects or anomalies in machine. There is gap between current AM techniques and smart manufacturing since current AM lacks in build sensors necessary for process monitoring and fault detection. Both of these issues can be solved by incorporating real-time monitoring into AM. So the study is carried out to identify recent work done in AM to improve current system. For this bibliometric study Scopus database is used, study is kept limited to year 2010-2021 and English …

Asynchronous Validations Using Programming Contracts In Java, Rahul Shukla

Master's Projects

Design by Contract is a software development methodology based on the idea of having contracts between two software components. Programming contracts are invariants specified as pre-conditions and post-conditions. The client component must ensure that all the pre-conditions are satisfied before calling the server component. The server component must guarantee the post-conditions are met before the call returns to the client component. Current work in Design by Contract in Java focuses on writing shorthand contracts using annotations that are processed serially.

Modern software systems require a lot of business rules validations on complicated domain objects. Often, such validations are in the …