Computer Science Doctoral Work

Permanent URI for this collectionhdl:10365/32551

Browse

Now showing 1 - 20 of 67

Adaptive Differential Evolution and its Application to Machine Vision
(North Dakota State University, 2016) Dawar, Deepak
Over recent years, Evolutionary Algorithms (EA) have emerged as a practical approach for solving hard optimization problems ubiquitously presented in real life. The inherent advantage of EA over other types of numerical optimization methods lies in the fact that they require much less or no prior knowledge of the objective function. Differential Evolution (DE) has emerged as a highly competitive and powerful real parameter optimizer in the diverse community of evolutionary algorithms. The study of this dissertation is focused on two main approaches. The first approach focuses on studying and improving DE by creating its variants that aim at altering/adapting its control parameters and mutation strategies during the course of the search. The performance of DE depends largely upon the mutation strategy used, its control parameters namely the scale factor F, the crossover rate Cr, and the population size NP, and is quite sensitive to their appropriate settings. A simple and effective technique that alters F in stages, first through random perturbations and then through the application of an annealing schedule, is proposed. After that, the impact and efficacy of adapting mutation strategies with or without adapting the control parameters is investigated. The second approach is concerned with the application side of DE which is used as an optimizer either as the primary algorithm or as a surrogate to improve the performance of the overall system. The focus area is video based vehicle classification. A DE based vehicle classification system is proposed. The system in its essence, aims to classify a vehicle, based on the number of circles (axles) in an image using Hough Transform which is a popular parameter based feature detection method. Differential Evolution (DE) is coupled with Hough Transform to improve the overall accuracy of the classification system. DE is further employed as an optimizer in an extension of the previous vehicle detector and classifier. This system has a novel appearance based model utilizing pixel color information and is capable of classifying multi-lane moving vehicles into seven different classes. Five different variants of DE on varied videos are tested, and a performance profile of all the variants is provided.
Adaptive Regression Testing Strategies for Cost-Effective Regression Testing
(North Dakota State University, 2013) Schwartz, Amanda Jo
Regression testing is an important but expensive part of the software development life-cycle. Many different techniques have been proposed for reducing the cost of regression testing. To date, much research has been performed comparing regression testing techniques, but very little research has been performed to aid practitioners and researchers in choosing the most cost-effective technique for a particular regression testing session. One recent study investigated this problem and proposed Adaptive Regression Testing (ART) strategies to aid practitioners in choosing the most cost-effective technique for a specific version of a software system. The results of this study showed that the techniques chosen by the ART strategy were more cost-effective than techniques that did not consider system lifetime and testing processes. This work has several limitations, however. First, it only considers one ART strategy. There are many other strategies which could be developed and studied that could be more cost-effective. Second, the ART strategy used the Analytical Hierarchy Process (AHP). The AHP method is subjective to the weights made by the decision maker. Also, the AHP method is very time consuming because it requires many pairwise comparisons. Pairwise comparisons also limit the scalability of the approach and are often found to be inconsistent. This work proposes three new ART strategies to address these limitations. One strategy utilizing the fuzzy AHP method is proposed to address imprecision in the judgment made by the decision maker. A second strategy utilizing a fuzzy expert system is proposed to reduce the time required by the decision maker, eliminate inconsistencies due to pairwise comparisons, and increase scalability. A third strategy utilizing the Weighted Sum Model is proposed to study the performance of a simple, low cost strategy. Then, a series of empirical studies are performed to evaluate the new strategies. The results of the studies show that the strategies proposed in this work are more cost-effective than the strategy presented in the previous study.
Addressing Challenges in Data Privacy and Security: Various Approaches to Secure Data
(North Dakota State University, 2021) Pattanayak, Sayantica
Emerging neural networks based machine learning techniques such as deep learning and its variants have shown tremendous potential in many application domains. However, the neural network models raise serious privacy concerns due to the risk of leakage of highly privacy-sensitive data. In this dissertation, we propose various techniques to hide the sensitive information and also evaluate the performance and efficacy of our proposed models. In our first research work we propose a model, which can both encrypt and decrypt a ciphertext. Our model is based on symmetric key encryption and back propagation neural network. Our model takes the decimal values and converts them to ciphertext and then again to decimal values. In our second research work, we propose a remote password authentication scheme using neural network. In this model, we have shown how an user can communicate securely with more than one server. A user registers himself / herself with a trusted authority and gets a user id and a password. The user uses the password and the user id to login to one or multiple servers. The servers can validate the legitimacy of the user. Our experiments use different classifiers to evaluate the accuracy and the efficiency of our proposed model. In our third research paper, we develop a technique to securely send patient information to different organizations. Our technique used different fuzzy membership functions to hide the sensitive information about patients. In our fourth research paper, we introduced an approach to substitute the sensitive attributes with the non-sensitive attributes. We divide the data set into three different subsets: desired, sensitive and non-sensitive subsets. The output of the denoising autoencoder will only be the desired and non-sensitive subsets. The sensitive subsets are hidden by the non-sensitive subsets. We evaluate the efficacy of our predictive model using three different flavors of autoencoders. We measure the F1-score of our model against each of the three autoencoders. As our predictive model is based on privacy, we have also used a Generative Adversarial Neural Network (GAN), which is used to show to what extend our model is secure.
Addressing Off-Nominal Behaviors in Requirements for Embedded Systems
(North Dakota State University, 2015) Aceituna, Daniel
System requirements are typically specified on the assumption that the system's operating environment will behave in what is considered to be an expected and nominal manner. When gathering requirements, one concern is whether the requirements are too ambiguous to account for every possible, unintended, Off-Nominal Behavior (ONB) that the operating environment can create, which results in an undesired system state. In this dissertation, we present two automated approaches which can expose, within a set of embedded requirements, whether an ONB can result in an undesired system state. Both approaches employ a modeling technique developed as part of this dissertation called the Causal Component Model (CCM). The first approach described, uses model checking as the means of property checking requirements using temporal logic properties specifically created to oppose ONBs. To facilitate the use of model checking by requirements engineers and non-technical stakeholders who are the system domain experts, a framework for the model checker interface was developed using the CCM. The CCM serves as both a cognitive friendly input and output to the model checker. The second approach extends the CCM into a dedicated ONB property checker, which overcomes the limitations of the model checker, by not only exposing ONBs but also facilitating the correction of those ONBs. We demonstrate how both approaches can expose and help correct potential Off-Nominal Behavior problems using requirements that represent real-world products. Our case studies show that both approaches can expose a system’s susceptibility to ONBs and provide enough information to correct the potential problems that can be caused by those ONBs.
AI approaches in personalized meal planning for a multi criteria problem
(North Dakota State University, 2024) Amiri, Maryam
Food is one of the necessities of life. The food we consume every day provides us with the nutrition we need to have energy. However, food plays a more significant role in life. There is a relationship between food, culture, family, and society [1]. Since ancient civilization, people have realized the correlation between food and healthiness. Earlier, Physicians were treating people by prescribing special recipes. Last century, assorted studies investigated the impact meals have on human nutritional intake and the different diseases connected to it. There have been numerous other studies that focused on the required nutritional intake to ensure a good amount of energy for well-being in humans. A person who advises individuals on their food and nutrition is known as a dietarian and nutritionist. Nowadays nutritionists are experts in the use of food and nutrition to promote health and manage disease. They suggest several diet rules and food recommendations to assist people in living a healthy life. Due to technological advancements, previous time-consuming issues that required human attention are now being solved by utilizing automated procedures machines. Meal planning is one of the attractive domains that recently has received great notation by researchers who are using machine learning techniques in it. In general, those studies were performed to use extracted nutrition knowledge and food information for designing an automated meal planning system. However, in the majority of published research, the user’s preferences were an ignored feature. In this research, my journey through developing automated meal planning systems unfolds across distinct projects, each building upon the insights and advancements of its predecessors. Starting with a focus on incorporating user preferences, the exploration evolved through successive iterations, seeking to mirror the complexities of real-world decision-making more accurately. This progression led to the integration of advanced methodologies spanning artificial intelligence, optimization, multi-criteria decision making, and fuzzy logic. The ultimate aim was to refine and enhance the systems to not only align with users’ dietary restrictions and preferences but also to adapt to user feedback, thereby continually improving their efficacy and personalization. Through this comprehensive approach, the research endeavors to contribute novel solutions to the nuanced challenges of personalized meal planning.
Analyses of People’s Perceptions Toward Risks in Genetically Modified Organisms
(North Dakota State University, 2016) Dass, Pranav
This research aims to analyze people’s perceptions about the potential risks associated with the presence of genetically modified organisms (GMOs) in food products. We formulated research questions and hypotheses based on parameters, including age, gender, state of residence, and more to analyze these perceptions. We conducted an online nationwide survey across the United States and recruited participants from the general population to understand their perceptions about risks for GMOs and GM foods. We formulated a set of questions regarding the effects of GMOs on food products (including both the pre- and post-study questions) and investigated the changes in people’s perceptions after reading selected news releases about GMOs. The survey responses were collected and categorized according to the research parameters and statistical assessments were conducted to test the hypotheses. Additionally, we introduced a novel approach to analyze the responses by creating a mind-map framework for both the pre- and post-study responses. We found that people residing in the southern region of the United States responded more positively toward GMOs when compared to individuals residing in the northeast, west and mid-west regions. We also deduced that people’s perceptions about GMOs were not significantly different from each other whether they resided in states with Republican or Democrat/non-partisan party affiliations. Further, we observed that the male participants responded more negatively compared to the female participants across the nation. We compared the results obtained from respondents in the general population to those from a group of Computer Science students at North Dakota State University who completed the same survey. We found that students considered GMOs less risky when compared to the general population. A third research study compared participants from the general population to a group of participants who were recruited from the general population. The second group didn’t read the news releases that separated the survey’s pre- and post-study questions. We observed that the news releases impacted the participants from the first group and, eventually, changed the individuals’ perceptions about GMOs when compared to the participants from the second group who possessed no or fewer perception changes.
Assessment of Engineering Methodologies for Increasing CubeSat Mission Success Rates
(North Dakota State University, 2021) Alanazi, Abdulaziz
In the last twenty years, CubeSat Systems have gained popularity in educational institutions and commercial industries. CubeSats have attracted educators and manufacturers due to their ability to be quickly produced and their low cost, and small sizes and masses. However, while developers can swiftly design and build their CubeSats, with a team of students from different disciplines using COTS parts, this does not guarantee that the CubeSat mission will be successful. Statistics show that mission failure is frequent. For example, out of 270 “university-class” CubeSats, 139 failed in their mission between 2002 and 2016 [1]. Statistics also show that the average failure rate of CubeSat missions is higher in academic and research institutions than in commercial or government organizations. Reasons for failure include power issues, mechanical, communications and system design issues. Some researchers have suggested that the problem lies within the design and development process itself, in that CubeSat developers mainly focus on system and component level designs, while neglecting requirements elicitation and other key system engineering activities [2]. To increase the success rate of CubeSat missions, systems engineering steps and processes need to be implemented in the development cycle. Using these processes can also help CubeSat designs and systems to become more secure, reusable, and modular. This research identifies multiple independent variables and measures their effectiveness for driving CubeSat systems’ mission success. It seeks to increase the CubeSat mission success rate by developing systems engineering methodologies and tools. It also evaluates the benefits of applying systems engineering methodologies and practices, which can be applied at different stages of CubeSat project lifecycle and across different CubeSat missions.
Automated Framework to Improve User’s Awareness and to Categorize Friends on Online Social Networks
(North Dakota State University, 2015) Barakat, Rahaf
The popularity of online social networks has brought up new privacy threats. These threats often arise after users willingly, but unwittingly reveal their information to a wider group of people than they actually intended. Moreover, the well adapted “friends-based” privacy control has proven to be ill-equipped to prevent dynamic information disclosure, such as in user text posts. Ironically, it fails to capture the dynamic nature of this data by reducing the problem to manual privacy management which is time-consuming, tiresome and error-prone task. This dissertation identifies an important problem with posting on social networks and proposes a unique two phase approach to the problem. First, we suggest an additional layer of security be added to social networking sites. This layer includes a framework for natural language to automatically check texts to be posted by the user and detect dangerous information disclosure so it warns the user. A set of detection rules have been developed for this purpose and tested with over 16,000 Facebook posts to confirm the detection quality. The results showed that our approach has an 85% detection rate which outperforms other existing approaches. Second, we propose utilizing trust between friends as currency to access dangerous posts. The unique feature of our approach is that the trust value is related to the absence of interaction on the given topic. To approach our goal, we defined trust metrics that can be used to determine trustworthy friends in terms of the given topic. In addition, we built a tool which calculates the metrics automatically, and then generates a list of trusted friends. Our experiments show that our approach has reasonably acceptable performance in terms of predicting friends’ interactions for the given posts. Finally, we performed some data analysis on a small set of user interaction records on Facebook to show that friends’ interaction could be triggered by certain topics.
Blockchain-Based Trust Model: Alleviating the Threat of Malicious Cyber-Attacks
(North Dakota State University, 2020) Bugalwi, Ahmed Youssef
Online communities provide a unique environment where interactions performed among its subscribers who have shared interest. Members of these virtual communities are typically classified as trustworthy and untrustworthy. Trust and reputation became indispensable properties due to the rapid growth of uncertainty and risk. This risk is a result of cyber-attacks carried out by untrustworthy actors. A malicious attack may produce misleading information making the community unreliable. Trust mechanism is a substantial instrument for empowering safe functioning within a community. Most virtual communities are centralized, which implies that they own, manage, and control trust information without given permission from the legitimate owner. The problem of ownership arises as actors may lose their reputations if the community decided to shut down its business. Sharing information is another valuable feature that aids lessening the impact of dishonest behavior. A new trust model called “TrustMe” was developed in this research as a reliable mechanism that generates precise trust information for virtual communities. TrustMe consists of several factors that aim to confuse untrustworthy actors, and to make the generated trust score is hardly reversed. A blockchain-based trust model is also developed to address the problem of ownership as well as offering a decentralized information sharing mechanism through a distributed application called “DATTC.” The efficiency of the proposed models was identified by conducting various analytic experimental studies. An unsupervised machine learning method (density-based clustering) was applied using two different datasets. Also, graph analysis was conducted to study the evolvement of communities and trust by finding connections between graph metrics and trust scores generated by TrustMe. Finally, a set of simulations using stochastic models to evaluate the accuracy and success rates of TrustMe, and a simulation set mimicked the blockchain-model in alleviating the influence of Sybil attack. The relationships among actors were hypothesized as actors divided into trustworthy and untrustworthy performing cooperative and malicious attacks. The results of the study prove that TrustMe can be promising and support the first hypothesis as TrustMe outperformed other trust models. Additionally, the results confirm that the blockchain-based trust model efficiently mitigates malicious cyber-attack by employing cross-community trust and preserves ownership property.
Computational Methods for Bulk and Single-cell Chromatin Interaction Data
(North Dakota State University, 2024) Bulathsinghalage, Chanaka
Chromatin interactions occur when the physical regions of chromatin in close proximity interact with each other inside the nucleus. Analyzing chromatin interactions plays a crucial role in deciphering the spatial organization of the genome. Identifying the significant interactions and their functionalities reveals great insights on gene expressions, gene regulations and genetic diseases such as cancer. In addition, single cell chromatin interaction data is important to understand the chromatin structure changes, diversity among individual cells, and the genomics differences between different cell types. In recent years, Hi-C, chromosome conformation capture with high throughput sequencing, has gained widespread popularity for its ability to map genome-wide chromatin interactions in a single experiment and it is capable of extracting both single cell and bulk chromatin interaction data. With the evolution of experimental methods like Hi-C, computational tools are essential to efficiently and accurately process the vast amount of genomic data. Since the experiment costs are notably higher, optimized computational tools and methods are needed to extract most possible information from the data. Moreover, processing single cell Hi-C data imposes number of challenges due to its sparseness and limited interaction counts. So the development of computational methods and tools to process data from both single cell Hi-C and bulk Hi-C technologies are focused in this work and those are proven to be enhancing the efficiency and accuracy of Hi-C data processing pipelines. In this dissertation, each chapter consists of a single individual method or a tool to enhance chromatin interaction processing pipelines and the final chapter focuses on the interplay between epigenetic data and chromatin interactions data. The studies that are focused on building computational methods include increasing data read accuracy for bulk Hi-C, identifying statistically significant interactions at single cell Hi-C data, and imputation of single cell Hi-C data to improve quality and quantity of raw reads. It is anticipated that the utilization of the tools and methods outlined in these studies will significantly enhance the workflows of future research on chromatin organization and its correlation with cellular functions and genetic diseases.
Computational Methods for Predicting Protein-Nucleic Acids Interaction
(North Dakota State University, 2015) Cheng, Wen
Since the inception of various proteomic projects, protein structures with unknown functions have been discovered at a fast speed. The proteins regulate many important biological processes by interacting with nucleic acids that include DNA and RNA. Traditional wet-lab methods for protein function discovery are too slow to handle this rapid increase of data. Therefore, there is a need for computational methods that can predict the interaction between proteins and nucleic acids. There are two related problems when predicting protein-nucleic interactions. One problem is to identify nucleic acid-binding sites on the protein structures, and the other problem is to predict the 3-D structure of the complex that protein and nucleic acids form during interaction. The second problem can be further divided into two steps. The first step is to generate potential structures for the protein-nucleic acids complex. The second step is to assign scores to the poses generated in the first step. This dissertation presents two computational methods that we developed to predict the protein-nucleic acids interaction. The first method is a scoring function that can discriminate native structures of protein-DNA complexes from non-native poses, which are also known as docking decoys. We analyze the distribution of protein atoms around each structural component of the DNA and develop spatial-specific scoring matrices (SSSMs) based on the observed distribution. We show that the SSSMs could be used as a knowledge-based energy function to discriminate native protein-DNA structures and various decoys. Our second method discovers the graphs that are enriched on the protein-nucleic acids interfaces and then uses the sub-graphs to predict RNA-binding sites on protein structures and to assign scores to protein-RNA poses. First, the interface area of each RNA-binding protein is represented as a graph, where each node represents an interface residue. Then, common sub-graphs being abundant in these graphs are identified. The method is able to identify RNA-binding sites on the protein surface with high accuracy. We also demonstrate that the common sub-graphs can be used as a scoring function to rank the protein-RNA poses. Our method is simple in computation, while its results are easier to interpret in biological contexts.
Contributing Factors Promoting Success for Females in Computing: A Comparative Study
(North Dakota State University, 2022) Gronneberg, Bethlehem
Despite the growing global demand for Computer Science (CS) professionals, their high earning potential, and diversified career paths (U.S. BLS 2021, UNESCO 2017), a critical gap exists between enrollment and graduation rates among female students in computing fields across the world (Raigoza 2017, Hailu 2018, UNESCO 2017, Bennedsen and Caspersen 2007). The largest dropout point occurs during the first two years of their CS studies (Giannakos, et al., 2017). The purpose of this parallelly convergent mixed-methods research was to comparatively investigate, describe and analyze factors correlated to the experiences and perceptions of female undergraduates as it relates to their persistence in CS/Software Engineering (SE) degrees, conducted in two public universities in the U.S. & Ethiopia. Anchored in Tinto’s theory of retention, the quantitative part of the study examined three possible predictive factors of success for students who were enrolled in the first two CS/SE courses and evaluated differences between genders and institutions on those factors. Pearson’s correlation coefficient tests were applied to test the hypothesis that the perceptions of Degree’s Usefulness (DU), Previously Acquired Knowledge (PAK) and Cognitive Attitude (CA) correlate to the decision to persist for the research participants. The results showed a statistically significant positive correlation between perceptions of DU, the influence of PAK, and the decision to persist. Two sample t-tests revealed gender and institutional differences exhibited in the influence of PAK and CA. The qualitative part of the study reported 12 contributing factors of success for graduating class of females in CS/SE using a unique approach of sentiment analysis and topic modeling from the domain of Natural Language Processing (NLP) through the interpretation of auto transcribed interview responses.
A Data Mining Approach to Radiation Hybrid Mapping
(North Dakota State University, 2014) Seetan, Raed
The task of mapping markers from Radiation Hybrid (RH) mapping experiments is typically viewed as equivalent to the traveling-salesman problem, which has combinatorial complexity. As an additional problem, experiments commonly result in some unreliable markers that reduce the overall map quality. Due to the large numbers of markers in current radiation hybrid populations, the use of the data mining techniques becomes increasingly important for reducing both the computational complexity and the impact of noise of the original data. In this dissertation, a clustering-based approach is proposed for addressing both the problem of filtering unreliable markers (framework maps) and the problem of mapping large numbers of markers (comprehensive maps) efficiently. Traditional approaches for eliminating unreliable markers use resampling of the full data set, which has an even higher computational complexity than the original mapping problem. In contrast, the proposed algorithms use a divide-and-conquer strategy to construct framework maps based on clusters that exclude unreliable markers. The clusters of markers are ordered using parallel processing and are then combined to form the complete map. Three algorithms are presented that explore the trade-off between the number of markers included in the framework map and placement accuracy. Since the mapping problem is susceptible to noise, it is often beneficial to remove markers that are not trustworthy. Traditional mapping techniques for building comprehensive maps process all markers together, including unreliable markers, in a single-iteration approach. The accuracy of the constructed maps may be reduced. In this research work, two-stage algorithms are proposed to mapping most markers by first creating a framework map of the reliable markers, and then incrementally adding the remaining markers to construct high quality comprehensive maps. All proposed algorithms have been evaluated on several human chromosomes using radiation hybrid datasets with varying sizes, and also the performance of our proposed algorithms is compared with state-of-the-art RH mapping softwares. Overall, the proposed algorithms are not only much faster than the comparative approaches, but that the quality of the resulting maps is also much higher.
Deception in Cyberspace: Con-Man Attack in Cloud Services
(North Dakota State University, 2018) Chowdhury, Md. Minhaz
A con-man deception appears in services from cyberspace, e.g., in cloud services. A cloud-service provider deceives by repeatedly providing less service than promised and deliberately avoids service monitoring. Such a repeated shortfall is beneficial for the cloud-service provider but victimizes the service’s legitimate consumers. This deception is called a con-man deception. A con-man-resistant trust algorithm is used as a proactive measure against such deception, reducing the deception’s severity on the victim’s end. This trust algorithm detects a con-man deception by evaluating a cloud service’s expected versus actual behavior. This detection application reveals the con-man-resistant trust algorithm’s previously veiled properties. With this dissertation, a study of these properties reveals some necessary enhancements for this algorithm. The previous con-man-resistant trust-algorithm applications only considered the pattern of service-shortfall repetition. However, for cloud applications, the service-shortfall magnitude at each repetition is also important. Hence, an exponential growth-function-based extension of this algorithm is proposed and implemented. The algorithm’s initial parameter configuration has a significant influence on the con-deception detection pace. Some consumers tolerate intense repetition of service shortfall, and some consumers can tolerate mild repetition. Hence, the deception-detection pace has a correlation with the consumer’s perspective. A machine-learning extension of the con-man-resistant trust algorithm can ascertain a consumer’s perspective by analyzing that consumer’s historical usage of the same cloud service. The result of this learning is a parameter configuration that reflects the consumer’s perspective. The loss associated with a con deception is significant on the consumer’s side. Hence, the work presented in this dissertation contributes to cybersecurity by attempting to minimize such deception in cyberspace.
Decision-Making for Self-Replicating 3D Printed Robot Systems
(North Dakota State University, 2021) Jones, Andrew Burkhard
This work addresses decision-making for robot systems that can self-replicate. With the advent of 3D printing technology, the development of self-replicating robot systems is more feasible to implement than it was previously. This opens the door to various opportunities in this area of robotics. A major benefit of having robots that are able to make more robots is that the survivability of the multi-robot system increases dramatically. A single surviving robot that has the necessary capabilities to self-replicate could prospectively repopulate an entire ‘colony’ of robots, given sufficient resources and time. This gives robots an opportunity to take more risks in trying to accomplish an objective in missions where robots must be used instead of humans due to distance, environmental, safety and other concerns. Autonomy is key to maximizing the efficacy of this functionality (or allowing this functionality in a communication limited/denied environment) for this type of robotic system. A challenge of analyzing self-replicating robot systems, and the decision-making algorithms for those systems, is that there isn’t currently a standard means to simulate these systems. Thus, for the purpose of this work, a simulation system was developed to do just this. Experiments were conducted using this simulation system and the results are presented. In this dissertation, the configuration and decision-making of self-replicating 3D printed robot systems are analyzed. First, an introduction to the concepts and topics is provided. Second, relevant background information is reviewed. Third, a simulation, used to model self-replicating robot systems to perform the experiments in later chapters, is detailed. Then, experiments are conducted utilizing this simulation model. These include the analysis of the impact of replication categories on system efficacy, the analysis of the comparative performance of multiple decision-making algorithms, and cybersecurity threats for self-replicating robot systems. For each, data is presented and analyzed, and conclusions are drawn. Finally, this dissertation concludes with a summary of the results presented throughout the document and a discussion of the broader findings from the experiments.
Detecting Insider and Masquerade Attacks by Identifying Malicious User Behavior and Evaluating Trust in Cloud Computing and IoT Devices
(North Dakota State University, 2019) Kambhampaty, Krishna Kanth
There are a variety of communication mediums or devices for interaction. Users hop from one medium to another frequently. Though the increase in the number of devices brings convenience, it also raises security concerns. Provision of platform to users is as much important as its security. In this dissertation we propose a security approach that captures user behavior for identifying malicious activities. System users exhibit certain behavioral patterns while utilizing the resources. User behaviors such as device location, accessing certain files in a server, using a designated or specific user account etc. If this behavior is captured and compared with normal users’ behavior, anomalies can be detected. In our model, we have identified malicious users and have assigned trust value to each user accessing the system. When a user accesses new files on the servers that have not been previously accessed, accessing multiple accounts from the same device etc., these users are considered suspicious. If this behavior continues, they are categorized as ingenuine. A trust value is assigned to users. This value determines the trustworthiness of a user. Genuine users get higher trust value and ingenuine users get a lower trust value. The range of trust value varies from zero to one, with one being the highest trustworthiness and zero being the lowest. In our model, we have sixteen different features to track user behavior. These features evaluate users’ activities. From the time users’ log in to the system till they log out, users are monitored based on these sixteen features. These features determine whether the user is malicious. For instance, features such as accessing too many accounts, using proxy servers, too many incorrect logins attribute to suspicious activity. Higher the number of these features, more suspicious is the user. More such additional features contribute to lower trust value. Identifying malicious users could prevent and/or mitigate the attacks. This will enable in taking timely action against these users from performing any unauthorized or illegal actions. This could prevent insider and masquerade attacks. This application could be utilized in mobile, cloud and pervasive computing platforms.
Developing and Validating Active Learning Engagement Strategies to Improve Students' Understanding of Programming and Software Engineering Concepts
(North Dakota State University, 2020) Brown, Tamaike Mariane
Introductory computer programming course is one of the fundamental courses in computer science. Students enrolled in computer science courses at the college or university have been reported to lack motivation, and engagement when learning introductory programming (CS1). Traditional classrooms with lecture-based delivery of content do not meet the needs of the students that are being exposed to programming courses for the first time. Students enrolled in first year programming courses are better served with a platform that can provide them with a self-paced learning environment, quicker feedback, easier access to information and different level of learning content/assessment that can keep them motivated and engaged. Introductory programming courses (hereafter referred to as CS1 and CS2 courses) also include students from non-STEM majors who struggle at learning basic programming concepts. Studies report that CS1 courses nationally have high dropout rates, ranging from anywhere between 30-40% on an average. Some of the reasons cited by researchers for high dropout rate are lack of resource support, motivation, lack of engagement, lack of motivation, lack of practice and feedback, and confidence. Although the interest to address these issues in computing is expanding, the dropout rate for CS1/CS2 courses remains high. The software engineering industry often believes that the academic community is missing the mark in the education of computer science students. Employers recognize that students entering the workforce directly from university training often do not have the complete set of software development skills that they will need to be productive, especially in large software development companies.
Development and Validation of Feedback-Based Testing Tutor Tool to Support Software Testing Pedagogy
(North Dakota State University, 2020) Cordova, Lucas Pascual
Current testing education tools provide coverage deficiency feedback that either mimics industry code coverage tools or enumerates through the associated instructor tests that were absent from the student’s test suite. While useful, these types of feedback mechanisms are akin to revealing the solution and can inadvertently lead a student down a trial-and-error path, rather than using a systematic approach. In addition to an inferior learning experience, a student may become dependent on the presence of this feedback in the future. Considering these drawbacks, there exists an opportunity to develop and investigate alternative feedback mechanisms that promote positive reinforcement of testing concepts. We believe that using an inquiry-based learning approach is a better alternative (to simply providing the answers) where students can construct and reconstruct their knowledge through discovery and guided learning techniques. To facilitate this, we present Testing Tutor, a web-based assignment submission platform to support different levels of testing pedagogy via a customizable feedback engine. This dissertation is based on the experiences of using Testing Tutor at different levels of the curriculum. The results indicate that the groups using conceptual feedback produced higher-quality test suites (achieved higher average code coverage, fewer redundant tests, and higher rates of improvement) than the groups that received traditional code coverage feedback. Furthermore, students also produced higher quality test suites when the conceptual feedback was tailored to task-level for lower division student groups and self-regulating-level for upper division student groups. We plan to perform additional studies with the following objectives: 1) improve the feedback mechanisms; 2) understand the effectiveness of Testing Tutor’s feedback mechanisms at different levels of the curriculum; and 3) understand how Testing Tutor can be used as a tool for instructors to gauge learning and determine whether intervention is necessary to improve students’ learning.
A Distributed Linear Programming Model in a Smart Grid
(North Dakota State University, 2013) Ranganathan, Prakash
Advances in computing and communication have resulted in large-scale distributed environments in recent years. They are capable of storing large volumes of data and, often, have multiple compute nodes. However, the inherent heterogeneity of data components, the dynamic nature of distributed systems, the need for information synchronization and data fusion over a network, and security and access-control issues makes the problem of resource management and monitoring a tremendous challenge in the context of a Smart grid. Unfortunately, the concept of cloud computing and the deployment of distributed algorithms have been overlooked in the electric grid sector. In particular, centralized methods for managing resources and data may not be sufficient to monitor a complex electric grid. Most of the electric grid management that includes generation, transmission, and distribution is, by and large, managed at a centralized control. In this dissertation, I present a distributed algorithm for resource management which builds on the traditional simplex algorithm used for solving large-scale linear optimization problems. The distributed algorithm is exact, meaning its results are identical if run in a centralized setting. More specifically in this dissertation, I discuss a distributed decision model, where a large-scale electric grid is decomposed into many sub models that can support the resource assignment, communication, computation, and control functions necessary to provide robustness and to prevent incidents such as cascading blackouts. The key contribution of this dissertation is to design, develop, and test a resource-allocation process through a decomposition principle in a Smart grid. I have implemented and tested the Dantzig-Wolfe decomposition process in standard IEEE 14-bus and 30-bus systems. The dissertation provides details about how to formulate, implement, and test such an LP-based design to study the dynamic behavior and impact of an electrical network while considering its failure and repair rates. The computational benefits of the Dantzig-Wolfe approach to find an optimal solution and its applicability to IEEE bus systems are presented.
Domain Ontology Based Detection Approach to Identify Effect Types of Security Requirements upon Functional Requirements
(North Dakota State University, 2015) Al-Ahmad, Bilal Ibrahim
Requirements engineering is a subfield of software engineering that is concerned with analyzing software requirements specifications. An important process of requirement engineering is tracing requirements to investigate relationships between requirements and other software artifacts (i.e., source code, test cases, etc.). Requirements traceability is mostly manual because of difficulties automating the process. A specific mode of tracing is inter-requirements traceability, which focuses on tracing requirements with other requirements. Investigating inter-requirements traceability is very important because it has significant influence on many activities of software engineering such as requirements implementation, consistency checking, and requirements impact change management. Several studies used different approaches to identify three types of relationships: cooperative, conflicting, and irrelevant. However, the current solutions have several shortcomings: (1) only applicable to fuzzy requirements, user requirements, and technical requirements, (2) ignoring the syntactic and semantic aspects of software requirements, and (3) little attention was given to show the influence of security requirements on functional requirements. Furthermore, several traceability tools have a lack of using predefined rules to identify relationships.

Browse

Browsing Computer Science Doctoral Work by browse.metadata.department "Computer Science"