Computer Engineering Commons^™

Designing Intelligent Energy Efficient Scheduling Algorithm To Support Massive Iot Communication In Lora Networks, Jui Mhatre

Master of Science in Computer Science Theses

We are about to enter a new world with sixth sense ability – “Network as a sensor -6G”. The driving force behind digital sensing abilities is IoT. Due to their capacity to work in high frequency, 6G devices have voracious energy demand. Hence there is a growing need to work on green solutions to support the underlying 6G network by making it more energy efficient. Low cost, low energy, and long-range communication capability make LoRa the most adopted and promising network for IoT devices. Since LoRaWAN uses ALOHA for multi-access of channels, collision management is an important task. Moreover, in …

Detection Of Crypto-Ransomware Attack Using Deep Learning, Muna Jemal

Master of Science in Computer Science Theses

The number one threat to the digital world is the exponential increase in ransomware attacks. Ransomware is malware that prevents victims from accessing their resources by locking or encrypting the data until a ransom is paid. With individuals and businesses growing dependencies on technology and the Internet, researchers in the cyber security field are looking for different measures to prevent malicious attackers from having a successful campaign. A new ransomware variant is being introduced daily, thus behavior-based analysis of detecting ransomware attacks is more effective than the traditional static analysis. This paper proposes a multi-variant classification to detect ransomware I/O …

Graph Based Management Of Temporal Data, Alex Fotso

Master of Science in Computer Science Theses

In recent decades, there has been a significant increase in the use of smart devices and sensors that led to high-volume temporal data generation. Temporal modeling and querying of this huge data have been essential for effective querying and retrieval. However, custom temporal models have the problem of generalizability, whereas the extended temporal models require users to adapt to new querying languages. In this thesis, we propose a method to improve the modeling and retrieval of temporal data using an existing graph database system (i.e., Neo4j) without extending with additional operators. Our work focuses on temporal data represented as intervals …

Efficient Yet Robust Privacy For Video Streaming, Luke Cranfill, Junggab Son

Master of Science in Computer Science Theses

MPEG-DASH is a video streaming standard that outlines protocols for sending audio and video content from a server to a client over HTTP. The standard has been widely utilized by the video streaming industry. However, it creates an opportunity for an adversary to invade users’ privacy. While a user is watching a video, information is leaked in the form of meta-data, the size and time that the server sent data to the user. This information is not protected by encryption and can be used to create a fingerprint for a video. Once the fingerprint is created, the adversary can use …

Optimal Order Assignment With Minimum Wage Consideration (Ooamwc), Hakem Alazmi

Master of Science in Computer Science Theses

While the application of crowdsourcing has increased over the years, the technology experiences various issues during implementation. Examples of some of the issues that affect crowdsourcing include task assignment, profit maximizations, as well as time window issues. In some instances addressing some of the issues results in the other issues being overlooked. An example is when assigning tasks to workers, the profits of the workers might not be considered and this ends up affecting the profit maximization aspect. Various algorithms have been proposed to address the task assignment, profit maximizations, and time window issues. However, these algorithms address the issues …

Rethinking The Weakness Of Stream Ciphers And Its Application To Encrypted Malware Detection, William T. Stone, Junggab Son

Master of Science in Computer Science Theses

Encryption key use is a critical component to the security of a stream cipher: because many implementations simply consist of a key scheduling algorithm and logical exclusive or (XOR), an attacker can completely break the cipher by XORing two ciphertexts encrypted under the same key, revealing the original plaintexts and the key itself. The research presented in this paper reinterprets this phenomenon, using repeated-key cryptanalysis for stream cipher identification. It has been found that a stream cipher executed under a fixed key generates patterns in each character of the ciphertexts it produces and that these patterns can be used to …

Leveraging Smart Contracts For Asynchronous Group Key Agreement In Internet Of Things, Victor Youdom Kemmoe, Junggab Son

Master of Science in Computer Science Theses

Group Key Agreement (GKA) mechanism plays a crucial role in the realization of various secure applications in various networks such as, but not limited to, sensor networks, Internet of Things (IoT), vehicular networks, social networks, and so on. To be suitable for IoT, GKA must satisfy several critical requirements. First, a GKA mechanism must be robust against a compromised device attack and satisfy essential secrecy definitions without the existence of a Trusted Third Party (TTP). TTP is often used by IoT devices in the establishment of ad hoc networks and usually, these devices are resource-constrained. Second, the GKA mechanism must …

Performance Of Malware Classification On Machine Learning Using Feature Selection, Nusrat Asrafi

Master of Science in Computer Science Theses

The exponential growth of malware has created a significant threat in our daily lives, which heavily rely on computers running all kinds of software. Malware writers create malicious software by creating new variants, new innovations, new infections and more obfuscated malware by using techniques such as packing and encrypting techniques. Malicious software classification and detection play an important role and a big challenge for cyber security research. Due to the increasing rate of false alarm, the accurate classification and detection of malware is a big necessity issue to be solved. In this research, eight malware family have been classifying according …

Document Layout Analysis And Recognition Systems, Sai Kosaraju

Master of Science in Computer Science Theses

Automatic extraction of relevant knowledge to domain-specific questions from Optical Character Recognition (OCR) documents is critical for developing intelligent systems, such as document search engines, sentiment analysis, and information retrieval, since hands-on knowledge extraction by a domain expert with a large volume of documents is intensive, unscalable, and time-consuming. There have been a number of studies that have automatically extracted relevant knowledge from OCR documents, such as ABBY and Sandford Natural Language Processing (NLP). Despite the progress, there are still limitations yet-to-be solved. For instance, NLP often fails to analyze a large document. In this thesis, we propose a knowledge …

Knn Optimization For Multi-Dimensional Data, Arialdis Japa

Master of Science in Computer Science Theses

The K-Nearest Neighbors (KNN) algorithm is a simple but powerful technique used in the field of data analytics. It uses a distance metric to identify existing samples in a dataset which are similar to a new sample. The new sample can then be classified via a class majority voting of its most similar samples, i.e. nearest neighbors. The KNN algorithm can be applied in many fields, such as recommender systems where it can be used to group related products or predict user preferences. In most cases, the performance of the KNN algorithm tends to suffer as the size of the …

A Constrained Box Algorithm For Imbalanced Data In Remote Sensing Images, Wajira Abeysinghe

Master of Science in Computer Science Theses

Imbalanced data is a common problem in machine learning where the number of observations that belong to one class is significantly lower than other classes. Due to the skewed distribution among the classes, most classification algorithms fail to classify minority instances effectively. The class imbalance problem can be found in many domains such as credit card fraud detection and rare diseases diagnosis.

Imbalanced data is a prominent issue also in remote sensing images (RSI) which are used to obtain information of earth resources and the surrounding environment. RSI are collected by special cameras that capture information from a specific wavelength …

Compliance Of Open Source Ehr Applications With Hipaa And Onc Security And Privacy Requirements, Maryam Farhadi, Hisham Haddad, Hossain Shahriar

Master of Science in Computer Science Theses

Electronic Health Records (EHRs) are digital versions of paper-based patient's health information. EHR applications are increasingly being adopted in many countries. They have resulted in improved quality in healthcare, convenient access to histories of patient medication and clinic visits, easier follow up of patient treatment plans, and precise medical decision-making process. EHR applications are guided by measures of the Health Insurance Portability and Accountability Act (HIPAA) to ensure confidentiality, integrity, and availability. However, there have been reported breaches of Protected Health Identifier (PHI) data stored by EHR applications. In many reported breaches, improper use of EHRs has resulted in disclosure …

American Sign Language Recognition Using Machine Learning And Computer Vision, Kshitij Bantupalli, Ying Xie

Master of Science in Computer Science Theses

Speech impairment is a disability which affects an individual’s ability to communicate using speech and hearing. People who are affected by this use other media of communication such as sign language. Although sign language is ubiquitous in recent times, there remains a challenge for non-sign language speakers to communicate with sign language speakers or signers. With recent advances in deep learning and computer vision there has been promising progress in the fields of motion and gesture recognition using deep learning and computer vision-based techniques. The focus of this work is to create a vision-based application which offers sign language translation …

Automatic Identification Of Animals In The Wild: A Comparative Study Between C-Capsule Networks And Deep Convolutional Neural Networks., Joel Kamdem Teto, Ying Xie

Master of Science in Computer Science Theses

The evolution of machine learning and computer vision in technology has driven a lot of

improvements and innovation into several domains. We see it being applied for credit decisions, insurance quotes, malware detection, fraud detection, email composition, and any other area having enough information to allow the machine to learn patterns. Over the years the number of sensors, cameras, and cognitive pieces of equipment placed in the wilderness has been growing exponentially. However, the resources (human) to leverage these data into something meaningful are not improving at the same rate. For instance, a team of scientist volunteers took 8.4 years, …

Implementation Of Secure Dnp3 Architecture Of Scada System For Smart Grids, Uday Bhaskar Boyanapalli

Master of Science in Computer Science Theses

With the recent advances in the power grid system connecting to the internet, data sharing, and networking enables space for hackers to maliciously attack them based on their vulnerabilities. Vital stations in the smart grid are the generation, transmission, distribution, and customer substations are connected and controlled remotely by the network. Every substation is controlled by a Supervisory Control and Data Acquisition (SCADA) system which communicates on DNP3 protocol on Internet/IP which has many security vulnerabilities. This research will focus on Distributed Network Protocol (DNP3) communication which is used in the smart grid to communicate between the controller devices. We …

An Iot System For Converting Handwritten Text To Editable Format Via Gesture Recognition, Nidhi Patel

Master of Science in Computer Science Theses

Evaluation of traditional classroom has led to electronic classroom i.e. e-learning. Growth of traditional classroom doesn’t stop at e-learning or distance learning. Next step to electronic classroom is a smart classroom. Most popular features of electronic classroom is capturing video/photos of lecture content and extracting handwriting for note-taking. Numerous techniques have been implemented in order to extract handwriting from video/photo of the lecture but still the deficiency of few techniques can be resolved, and which can turn electronic classroom into smart classroom.

In this thesis, we present a real-time IoT system to convert handwritten text into editable format by implementing …

Classification Of Images Based On Pixels That Represent A Small Part Of The Scene. A Case Applied To Microaneurysms In Fundus Retina Images, Pablo F. Ordonez, Pablo F. Ordonez

Master of Science in Computer Science Theses

Convolutional Neural Networks (CNNs), the state of the art in image classification, have proven to be as effective as an ophthalmologist, when detecting Referable Diabetic Retinopathy (RDR). Having a size of less than 1\% of the total image, microaneurysms are early lesions in DR that are difficult to classify. The purpose of this thesis is to improve the accuracy of detection of microaneurysms using a model that includes two CNNs with different input image sizes, 60x60 and 420x420 pixels. These models were trained using the Kaggle and Messidor datasets and tested independently against the Kaggle dataset, showing a sensitivity of …

Feature Selection And Improving Classification Performance For Malware Detection, Carlos A. Cepeda Mora

Master of Science in Computer Science Theses

The ubiquitous advance of technology has been conducive to the proliferation of cyber threats, resulting in attacks that have grown exponentially. Consequently, researchers have developed models based on machine learning algorithms for detecting malware. However, these methods require significant amount of extracted features for correct malware classification, making that feature extraction, training, and testing take significant time; even more, it has been unexplored which are the most important features for accomplish the correct classification.

In this Thesis, it is created and analyzed a dataset of malware and clean files (goodware) from the static and dynamic features provided by the online …

Comparative Study Of Dimension Reduction Approaches With Respect To Visualization In 3-Dimensional Space, Pooja Chenna

Master of Science in Computer Science Theses

In the present big data era, there is a need to process large amounts of unlabeled data and find some patterns in the data to use it further. If data has many dimensions, it is very hard to get any insight of it. It is possible to convert high-dimensional data to low-dimensional data using different techniques, this dimension reduction is important and makes tasks such as classification, visualization, communication and storage much easier. The loss of information should be less while mapping data from high-dimensional space to low-dimensional space. Dimension reduction has been a significant problem in many fields as …