Matching Items (137)
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- All Subjects: Computer Science
- Genre: Academic theses
- Creators: Davulcu, Hasan
- Member of: ASU Electronic Theses and Dissertations
Description
Emerging trends in cyber system security breaches in critical cloud infrastructures show that attackers have abundant resources (human and computing power), expertise and support of large organizations and possible foreign governments. In order to greatly improve the protection of critical cloud infrastructures, incorporation of human behavior is needed to predict potential security breaches in critical cloud infrastructures. To achieve such prediction, it is envisioned to develop a probabilistic modeling approach with the capability of accurately capturing system-wide causal relationship among the observed operational behaviors in the critical cloud infrastructure and accurately capturing probabilistic human (users’) behaviors on subsystems as the subsystems are directly interacting with humans. In our conceptual approach, the system-wide causal relationship can be captured by the Bayesian network, and the probabilistic human behavior in the subsystems can be captured by the Markov Decision Processes. The interactions between the dynamically changing state graphs of Markov Decision Processes and the dynamic causal relationships in Bayesian network are key components in such probabilistic modelling applications. In this thesis, two techniques are presented for supporting the above vision to prediction of potential security breaches in critical cloud infrastructures. The first technique is for evaluation of the conformance of the Bayesian network with the multiple MDPs. The second technique is to evaluate the dynamically changing Bayesian network structure for conformance with the rules of the Bayesian network using a graph checker algorithm. A case study and its simulation are presented to show how the two techniques support the specific parts in our conceptual approach to predicting system-wide security breaches in critical cloud infrastructures.
ContributorsNagaraja, Vinjith (Author) / Yau, Stephen S. (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2015
Description
The purpose of this research is to efficiently analyze certain data provided and to see if a useful trend can be observed as a result. This trend can be used to analyze certain probabilities. There are three main pieces of data which are being analyzed in this research: The value for δ of the call and put option, the %B value of the stock, and the amount of time until expiration of the stock option. The %B value is the most important. The purpose of analyzing the data is to see the relationship between the variables and, given certain values, what is the probability the trade makes money. This result will be used in finding the probability certain trades make money over a period of time.
Since options are so dependent on probability, this research specifically analyzes stock options rather than stocks themselves. Stock options have value like stocks except options are leveraged. The most common model used to calculate the value of an option is the Black-Scholes Model [1]. There are five main variables the Black-Scholes Model uses to calculate the overall value of an option. These variables are θ, δ, γ, v, and ρ. The variable, θ is the rate of change in price of the option due to time decay, δ is the rate of change of the option’s price due to the stock’s changing value, γ is the rate of change of δ, v represents the rate of change of the value of the option in relation to the stock’s volatility, and ρ represents the rate of change in value of the option in relation to the interest rate [2]. In this research, the %B value of the stock is analyzed along with the time until expiration of the option. All options have the same δ. This is due to the fact that all the options analyzed in this experiment are less than two months from expiration and the value of δ reveals how far in or out of the money an option is.
The machine learning technique used to analyze the data and the probability
is support vector machines. Support vector machines analyze data that can be classified in one of two or more groups and attempts to find a pattern in the data to develop a model, which reliably classifies similar, future data into the correct group. This is used to analyze the outcome of stock options.
ContributorsReeves, Michael (Author) / Richa, Andrea (Thesis advisor) / McCarville, Daniel R. (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2015
Description
One of the most remarkable outcomes resulting from the evolution of the web into Web 2.0, has been the propelling of blogging into a widely adopted and globally accepted phenomenon. While the unprecedented growth of the Blogosphere has added diversity and enriched the media, it has also added complexity. To cope with the relentless expansion, many enthusiastic bloggers have embarked on voluntarily writing, tagging, labeling, and cataloguing their posts in hopes of reaching the widest possible audience. Unbeknown to them, this reaching-for-others process triggers the generation of a new kind of collective wisdom, a result of shared collaboration, and the exchange of ideas, purpose, and objectives, through the formation of associations, links, and relations. Mastering an understanding of the Blogosphere can greatly help facilitate the needs of the ever growing number of these users, as well as producers, service providers, and advertisers into facilitation of the categorization and navigation of this vast environment. This work explores a novel method to leverage the collective wisdom from the infused label space for blog search and discovery. The work demonstrates that the wisdom space can provide a most unique and desirable framework to which to discover the highly sought after background information that could aid in the building of classifiers. This work incorporates this insight into the construction of a better clustering of blogs which boosts the performance of classifiers for identifying more relevant labels for blogs, and offers a mechanism that can be incorporated into replacing spurious labels and mislabels in a multi-labeled space.
ContributorsGalan, Magdiel F (Author) / Liu, Huan (Thesis advisor) / Davulcu, Hasan (Committee member) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2015
Description
In some scenarios, true temporal ordering is required to identify the actions occurring in a video. Recently a new synthetic dataset named CATER, was introduced containing 3D objects like sphere, cone, cylinder etc. which undergo simple movements such as slide, pick & place etc. The task defined in the dataset is to identify compositional actions with temporal ordering. In this thesis, a rule-based system and a window-based technique are proposed to identify individual actions (atomic) and multiple actions with temporal ordering (composite) on the CATER dataset. The rule-based system proposed here is a heuristic algorithm that evaluates the magnitude and direction of object movement across frames to determine the atomic action temporal windows and uses these windows to predict the composite actions in the videos. The performance of the rule-based system is validated using the frame-level object coordinates provided in the dataset and it outperforms the performance of the baseline models on the CATER dataset. A window-based training technique is proposed for identifying composite actions in the videos. A pre-trained deep neural network (I3D model) is used as a base network for action recognition. During inference, non-overlapping windows are passed through the I3D network to obtain the atomic action predictions and the predictions are passed through a rule-based system to determine the composite actions. The approach outperforms the state-of-the-art composite action recognition models by 13.37% (mAP 66.47% vs. mAP 53.1%).
ContributorsMaskara, Vivek Kumar (Author) / Venkateswara, Hemanth (Thesis advisor) / McDaniel, Troy (Thesis advisor) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2022
Description
Text classification, in the artificial intelligence domain, is an activity in which text documents are automatically classified into predefined categories using machine learning techniques. An example of this is classifying uncategorized news articles into different predefined categories such as "Business", "Politics", "Education", "Technology" , etc. In this thesis, supervised machine learning approach is followed, in which a module is first trained with pre-classified training data and then class of test data is predicted. Good feature extraction is an important step in the machine learning approach and hence the main component of this text classifier is semantic triplet based features in addition to traditional features like standard keyword based features and statistical features based on shallow-parsing (such as density of POS tags and named entities). Triplet {Subject, Verb, Object} in a sentence is defined as a relation between subject and object, the relation being the predicate (verb). Triplet extraction process, is a 5 step process which takes input corpus as a web text document(s), each consisting of one or many paragraphs, from RSS feeds to lists of extremist website. Input corpus feeds into the "Pronoun Resolution" step, which uses an heuristic approach to identify the noun phrases referenced by the pronouns. The next step "SRL Parser" is a shallow semantic parser and converts the incoming pronoun resolved paragraphs into annotated predicate argument format. The output of SRL parser is processed by "Triplet Extractor" algorithm which forms the triplet in the form {Subject, Verb, Object}. Generalization and reduction of triplet features is the next step. Reduced feature representation reduces computing time, yields better discriminatory behavior and handles curse of dimensionality phenomena. For training and testing, a ten- fold cross validation approach is followed. In each round SVM classifier is trained with 90% of labeled (training) data and in the testing phase, classes of remaining 10% unlabeled (testing) data are predicted. Concluding, this paper proposes a model with semantic triplet based features for story classification. The effectiveness of the model is demonstrated against other traditional features used in the literature for text classification tasks.
ContributorsKarad, Ravi Chandravadan (Author) / Davulcu, Hasan (Thesis advisor) / Corman, Steven (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2013
Description
The overall contribution of the Minerva Initiative at ASU is to map social organizations in a multidimensional space that provides a measure of their radical or counter radical influence over the demographics of a nation. This tool serves as a simple content management system to store and track project resources like documents, images, videos and web links. It provides centralized and secure access to email conversations among project team members. Conversations are categorized into one of the seven pre-defined categories. Each category is associated with a certain set of keywords and we follow a frequency based approach for matching email conversations with the categories. The interface is hosted as a web application which can be accessed by the project team.
ContributorsNair, Apurva Aravindakshan (Author) / Davulcu, Hasan (Thesis advisor) / Sen, Arunabha (Committee member) / Dasgupta, Partha (Committee member) / Arizona State University (Publisher)
Created2012
Description
Social media platforms provide a rich environment for analyzing user behavior. Recently, deep learning-based methods have been a mainstream approach for social media analysis models involving complex patterns. However, these methods are susceptible to biases in the training data, such as participation inequality. Basically, a mere 1% of users generate the majority of the content on social networking sites, while the remaining users, though engaged to varying degrees, tend to be less active in content creation and largely silent. These silent users consume and listen to information that is propagated on the platform.However, their voice, attitude, and interests are not reflected in the online content, making the decision of the current methods predisposed towards the opinion of the active users. So models can mistake the loudest users for the majority. To make the silent majority heard is to reveal the true landscape of the platform. In this dissertation, to compensate for this bias in the data, which is related to user-level data scarcity, I introduce three pieces of research work. Two of these proposed solutions deal with the data on hand while the other tries to augment the current data. Specifically, the first proposed approach modifies the weight of users' activity/interaction in the input space, while the second approach involves re-weighting the loss based on the users' activity levels during the downstream task training. Lastly, the third approach uses large language models (LLMs) and learns the user's writing behavior to expand the current data. In other words, by utilizing LLMs as a sophisticated knowledge base, this method aims to augment the silent user's data.
ContributorsKarami, Mansooreh (Author) / Liu, Huan (Thesis advisor) / Sen, Arunabha (Committee member) / Davulcu, Hasan (Committee member) / Mancenido, Michelle V. (Committee member) / Arizona State University (Publisher)
Created2023