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Item Parallel Particle Swarm Optimization(North Dakota State University, 2016) Manne, PriyankaPSO is a population based evolutionary algorithm and is motivated from the simulation of social behavior, which differs from the natural selection scheme of genetic algorithms. It is an optimization technique based on swarm intelligence, which simulates the bio-inspired behavior. PSO is a popular global search method and the algorithm is being widely used in conjunction with several other algorithms in different fields of study. Modern day computational problems demand highly capable processing machines and improved optimization techniques. Since it is being widely used, it is important to search for ways to speed up the process of PSO, as the complexity of the problems increase. The paper describes a way to improve it via parallelization. The parallel PSO algorithm’s robustness and efficiency is demonstrated. This paper evaluates the parallelized version of the PSO algorithm with the use of Parallel Computing Toolbox available in Matlab.Item Protein Functional Site Prediction Using the Shortest-Path Graph Kernel Method(North Dakota State University, 2013) Benaragama Vidanelage, Malinda Vikum SanjakaOver the past decade Structural Genomics projects have accumulated structural data for over 75,000 proteins, but the function of most of them are unknown due to limitation of laboratory approaches for discovering the functionality of proteins. Computational methods play key roles to minimize this gap. Graphs are often used to describe and analyze the geometry and physicochemical composition of bimolecular structures such as, chemical compounds and protein functional sites. In this study, we developed an innovative graph method to represent protein surface based on how amino acid residues contact with each other. Further, we implemented a shortest-path graph kernel method to calculate similarities between the graphs. The nearest-neighbor method was used to compare the similarity of kernel values and predict functional sites of protein structures. The proposed approach achieved accuracy as high as 77.1% and would provide a useful tool for functional site prediction.Item Evaluating the Usefulness of Requirement Error Taxonomy as a Defect Prevention Technique: An Empirical Investigation(North Dakota State University, 2014) Rehman, SanaDefect prevention techniques can be used during the creation of software artifacts to help developers create high-quality artifacts. The Requirement Error Taxonomy developed by Walia et al. [22, 23] helps focus developer’s attention on common errors that can occur during requirements engineering. This paper investigates the usefulness of the Requirement Error Taxonomy as a defect prevention technique. The goal was to determine if making requirements engineers’ familiar with the Requirement Error Taxonomy would reduce the likelihood that they commit errors while developing a requirements document. We conducted an empirical study in which the participants used the Requirement Error Taxonomy during inspection of a requirements document. Then, in teams, they developed their own requirements document which was evaluated by other students. The hypothesis was that participants who find more errors during the inspection of a requirements document would make fewer errors when creating their own requirements document. The overall result supports this hypothesis.Item A Java Class Analysis Program Development By ASM Library(North Dakota State University, 2017) Qiu, ChengxiangClass analysis is useful technique that can be used in many situations, from the syntaxes parsing, potential bugs finding, and unused code detecting to reverse engineer coding. In this paper, I write a small program classasm analysis the java class by calling the methods in ASM library. Here, the ASM name does not mean anything: it is just a reference to the “_asm_” keyword in C, which allows some functions to be implemented in assembly language. In Java, the ASM provides methods to read write and transform such byte arrays by using higher level concepts than bytes, that mean through the API in the ASM library we can analysis the class without reading the source code. ASM supply method to attract information from the compiled class file. In the research I use the simple company classes and the disconf-core-2.6.35.jar as examples, to show the results.Item Analyzing Access Logs Data using Stream Based Architecture(North Dakota State University, 2018) Gautam, NitendraWithin the past decades, the enterprise-level IT infrastructure in many businesses have grown from a few to thousands of servers, increasing the digital footprints they produce. These digital footprints include access logs that contain information about different events such as activity related to usage patterns, networks and any hostile activity affecting the network. Apache Hadoop has been one of the most standardized frameworks and is used by many Information Technology (IT) companies for analyzing these log files in distributed batch mode using MapReduce programming model. As these access logs include important information related to security and usage patterns, companies are now looking for an architecture that allows analyzing these logs in real time. To overcome the limitations of the MapReduce based architecture of Hadoop, this paper proposes a new and more efficient data processing architecture using Apache Spark, Kafka and other technologies that can handle both real-time and batch-based data.Item Automatic Method for Testing Struts-Based Application(North Dakota State University, 2013) Tiwari, ShwetaModel based testing is a very popular and widely used in industry and academia. There are many tools developed to support model based development and testing, however, the benefits of model based testing requires tools that can automate the testing process. The paper propose an automatic method for model-based testing to test the web application created using Strut based frameworks and an effort to further reduce the level of human intervention require to create a state based model and test the application taking into account that all the test coverage criteria are met. A methodology is implemented to test applications developed with strut based framework by creating a real-time online shopping web application and using the test coverage criteria along with automated testing tool. This implementation will demonstrate feasibility of the proposed method.Item Classification Algorithms Applied to a Brain Computer Interface System Based On P300(North Dakota State University, 2017) Gaur, HimanshuA BCI or Brain Computer Interface is defined as a method of communication that converts neural activities generated by brain of living being (without the use of peripheral muscles and nerves) into computer commands or other device commands. BCI systems are useful for people with severe disability who have no reliable control over their muscles in order to interact with their surrounding environment. The BCI system used in this paper has used P300 evoked potential and three classifiers namely Logistic Regression (LR), Neural Network (NN), and Support Vector Machine (SVM). The system is tested with four people with severe disability and two able-bodied people. Classification accuracies obtained from LR, NN, SVM classifiers is then compared with Bayesian Linear Discriminant Analysis (BLDA) classifier and with each other. The relevant factors required for obtaining good classification accuracy in P300 evoked potential based BCI systems is also being explored and discussed.Item Evaluation of Convolutional Neural Networks Against Deepfakes Using Transfer Learning(North Dakota State University, 2023) Krishan, SiddharthThe main objective of this paper is to evaluate ResNets, DenseNet, Inception and VGG, against deepfake images, to answer the question: How effectively these Convolutional Neural Network can distinguish between deepfake images and real images. The dataset was acquired from FaceForensics++ and CelebA datasets for manipulated and unmanipulated images respectively. A custom script using Python and OpenCV was applied to create the final dataset for modelling. Transfer learning is a technique of applying the learned features by a network to a similar approach. It is employed to save time and resources in training, as it does not require a large dataset to allow the network to learn effectively. The Convolutional Neural Networks are tested against different deep fakes and the networks are evaluated using metrics like precision, recall, accuracy, loss, and f-1 score. It was observed that all the networks used in the experiment performed exceptionally well, but Inception network was slightly better than the other networks in separating the real and fake images.Item Threat Identification from Access Logs Using Elastic Stack(North Dakota State University, 2020) Vethanayagam, SuhanthanAccess log management is an essential part of cybersecurity. Lack of insight into user authentication patterns can hinder readiness and reaction to the growing threat of cyberattacks. Central Authentication Service (CAS) log is underutilized in threat detection due to its detailed and complex logging nature. This paper investigates the feasibility of turning unfriendly CAS logs into helpful datapoints utilizing Elastic Stack (Filebeat, Logstash, Elasticsearch and Kibana) to detect anomalies. CAS logs are collected by Filebeat and forwarded to Logstash. The deployment of a custom Grok filter in Logstash facilitates the normalization of complex CAS logs and the resulting data is indexed in Elasticsearch. A Python program using Elasticsearch’s aggregate function was developed to query the indexed data and compare password and multi-factor submission counts. This mechanism was found to have potential in detecting threats. Finally, Kibana’s rich visualization capabilities allow for exploring and shaping of data in innovative ways.Item Analyzing Three Different Tuning Strategies for Random Forest Hyperparameters for Fraud Detection(North Dakota State University, 2021) Sarao, Kulwinder KaurTechnology is advancing rapidly, and more tasks are becoming online than ever. Along with the benefits comes the disadvantages of this great advancement. While online services relieve from the struggle of in person activities, it also puts you on the risk of getting deceived by the fraudsters. This paper aims to detect the fraudulent transactions made online from a bank using a synthetically produced dataset. A random forest model has been trained to predict the fraudulent transactions. To achieve the best performance, the hyperparameters of the model have been tuned using three different tuning methods. As it turns out, grid search proved to be the best tuning strategy in terms of the mean cv score, precision, recall, f1-score and accuracy. It only lacked in providing the best run time, where Bayesian Optimization scored well than the others.