Matching Items (138)
Description
Composite materials have gained interest in the aerospace, mechanical and civil engineering industries due to their desirable properties - high specific strength and modulus, and superior resistance to fatigue. Design engineers greatly benefit from a reliable predictive tool that can calculate the deformations, strains, and stresses of composites under uniaxial and multiaxial states of loading including damage and failure predictions. Obtaining this information from (laboratory) experimental testing is costly, time consuming, and sometimes, impractical. On the other hand, numerical modeling of composite materials provides a tool (virtual testing) that can be used as a supplemental and an alternate procedure to obtain data that either cannot be readily obtained via experiments or is not possible with the currently available experimental setup. In this study, a unidirectional composite (Toray T800-F3900) is modeled at the constituent level using repeated unit cells (RUC) so as to obtain homogenized response all the way from the unloaded state up until failure (defined as complete loss of load carrying capacity). The RUC-based model is first calibrated and validated against the principal material direction laboratory tests involving unidirectional loading states. Subsequently, the models are subjected to multi-directional states of loading to generate a point cloud failure data under in-plane and out-of-plane biaxial loading conditions. Failure surfaces thus generated are plotted and compared against analytical failure theories. Results indicate that the developed process and framework can be used to generate a reliable failure prediction procedure that can possibly be used for a variety of composite systems.
ContributorsKatusele, Daniel Mutahwa (Author) / Rajan, Subramaniam (Thesis advisor) / Mobasher, Barzin (Committee member) / Neithalath, Narayanan (Committee member) / Arizona State University (Publisher)
Created2021
Description
The omnipresent data, growing number of network devices, and evolving attack techniques have been challenging organizations’ security defenses over the past decade. With humongous volumes of logs generated by those network devices, looking for patterns of malicious activities and identifying them in time is growing beyond the capabilities of their defense systems. Deep Learning, a subset of Machine Learning (ML) and Artificial Intelligence (AI), fills in this gapwith its ability to learn from huge amounts of data, and improve its performance as the data it learns from increases. In this dissertation, I bring forward security issues pertaining to two top threats that most organizations fear, Advanced Persistent Threat (APT), and Distributed Denial of Service (DDoS), along with deep learning models built towards addressing those
security issues. First, I present a deep learning model, APT Detection, capable of detecting anomalous activities in a system. Evaluation of this model demonstrates how it can contribute to early detection of an APT attack with an Area Under the Curve (AUC) of up to 91% on a Receiver Operating Characteristic (ROC) curve. Second, I present DAPT2020, a first of its kind dataset capturing an APT attack exploiting web and system vulnerabilities in an emulated organization’s production network. Evaluation of the dataset using well known machine learning models demonstrates the need for better deep learning models to detect APT attacks. I then present DAPT2021, a semi-synthetic dataset capturing an APT attackexploiting human vulnerabilities, alongside 2 less skilled attacks. By emulating the normal behavior of the employees in a set target organization, DAPT2021 has been created to enable researchers study the causations and correlations among the captured data, a much-needed information to detect an underlying threat early. Finally, I present a distributed defense
framework, SmartDefense, that can detect and mitigate over 90% of DDoS traffic at the source and over 97.5% of the remaining DDoS traffic at the Internet Service Provider’s (ISP’s) edge network. Evaluation of this work shows how by using attributes sent by customer edge network, SmartDefense can further help ISPs prevent up to 51.95% of the DDoS traffic
from going to the destination.
ContributorsMyneni, Sowmya (Author) / Xue, Guoliang (Thesis advisor) / Doupe, Adam (Committee member) / Li, Baoxin (Committee member) / Baral, Chitta (Committee member) / Arizona State University (Publisher)
Created2022
Description
With the rapid development of both hardware and software, mobile devices with their advantages in mobility, interactivity, and privacy have enabled various applications, including social networking, mixed reality, entertainment, authentication, and etc.In diverse forms such as smartphones, glasses, and watches, the number of mobile devices is expected to increase by 1 billion per year in the future.
These devices not only generate and exchange small data such as GPS data, but also large data including videos and point clouds.
Such massive visual data presents many challenges for processing on mobile devices.
First, continuously capturing and processing high resolution visual data is energy-intensive, which can drain the battery of a mobile device very quickly.
Second, data offloading for edge or cloud computing is helpful, but users are afraid that their privacy can be exposed to malicious developers.
Third, interactivity and user experience is degraded if mobile devices cannot process large scale visual data in real-time such as off-device high precision point clouds. To deal with these challenges, this work presents three solutions towards fine-grained control of visual data in mobile systems, revolving around two core ideas, enabling resolution-based tradeoffs and adopting split-process to protect visual data.In particular, this work introduces: (1) Banner media framework to remove resolution reconfiguration latency in the operating system for enabling seamless dynamic resolution-based tradeoffs;
(2) LesnCap split-process application development framework to protect user's visual privacy against malicious data collection in cloud-based Augmented Reality (AR) applications by isolating the visual processing in a distinct process;
(3) A novel voxel grid schema to enable adaptive sampling at the edge device that can sample point clouds flexibly for interactive 3D vision use cases across mobile devices and mobile networks. The evaluation in several mobile environments demonstrates that, by controlling visual data at a fine granularity, energy efficiency can be improved by 49% switching between resolutions, visual privacy can be protected through split-process with negligible overhead, and point clouds can be delivered at a high throughput meeting various requirements.Thus, this work can enable more continuous mobile vision applications for the future of a new reality.
ContributorsHu, Jinhan (Author) / LiKamWa, Robert (Thesis advisor) / Wu, Carole-Jean (Committee member) / Doupe, Adam (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2022
Description
As computers and the Internet have become integral to daily life, the potential gains from exploiting these resources have increased significantly. The global landscape is now rife with highly skilled wrongdoers seeking to steal from and disrupt society. In order to safeguard society and its infrastructure, a comprehensive approach to research is essential. This work aims to enhance security from three unique viewpoints by expanding the resources available to educators, users, and analysts. For educators, a capture the flag as-a-service was developed to support cybersecurity education. This service minimizes the skill and time needed to establish the infrastructure for hands-on hacking experiences for cybersecurity students. For users, a tool called CloakX was created to improve online anonymity. CloakX prevents the identification of browser extensions by employing both static and dynamic rewriting techniques, thwarting contemporary methods of detecting installed extensions and thus protecting user identity. Lastly, for cybersecurity analysts, a tool named Witcher was developed to automate the process of crawling and exercising web applications while identifying web injection vulnerabilities. Overall, these contributions serve to strengthen security education, bolster privacy protection for users, and facilitate vulnerability discovery for cybersecurity analysts.
ContributorsTrickel, Erik (Author) / Doupe, Adam (Thesis advisor) / Shoshitaishvili, Yan (Thesis advisor) / Bao, Tiffany (Committee member) / Wang, Ruoyu (Committee member) / Arizona State University (Publisher)
Created2023
Description
Artificial Intelligence (AI) systems have achieved outstanding performance and have been found to be better than humans at various tasks, such as sentiment analysis, and face recognition. However, the majority of these state-of-the-art AI systems use complex Deep Learning (DL) methods which present challenges for human experts to design and evaluate such models with respect to privacy, fairness, and robustness. Recent examination of DL models reveals that representations may include information that could lead to privacy violations, unfairness, and robustness issues. This results in AI systems that are potentially untrustworthy from a socio-technical standpoint. Trustworthiness in AI is defined by a set of model properties such as non-discriminatory bias, protection of users’ sensitive attributes, and lawful decision-making. The characteristics of trustworthy AI can be grouped into three categories: Reliability, Resiliency, and Responsibility. Past research has shown that the successful integration of an AI model depends on its trustworthiness. Thus it is crucial for organizations and researchers to build trustworthy AI systems to facilitate the seamless integration and adoption of intelligent technologies. The main issue with existing AI systems is that they are primarily trained to improve technical measures such as accuracy on a specific task but are not considerate of socio-technical measures. The aim of this dissertation is to propose methods for improving the trustworthiness of AI systems through representation learning. DL models’ representations contain information about a given input and can be used for tasks such as detecting fake news on social media or predicting the sentiment of a review. The findings of this dissertation significantly expand the scope of trustworthy AI research and establish a new paradigm for modifying data representations to balance between properties of trustworthy AI. Specifically, this research investigates multiple techniques such as reinforcement learning for understanding trustworthiness in users’ privacy, fairness, and robustness in classification tasks like cyberbullying detection and fake news detection. Since most social measures in trustworthy AI cannot be used to fine-tune or train an AI model directly, the main contribution of this dissertation lies in using reinforcement learning to alter an AI system’s behavior based on non-differentiable social measures.
ContributorsMosallanezhad, Ahmadreza (Author) / Liu, Huan (Thesis advisor) / Mancenido, Michelle (Thesis advisor) / Doupe, Adam (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2023
Description
Alkali activated mine tailing-slag blends and mine tailing-cement blends containing mine tailings as the major binder constituent are evaluated for their setting time behavior,
reactivity properties, flow characteristics, and compressive strengths. Liquid sodium
silicate and sodium hydroxide are used as the activator solution. The effects of varying
alkali oxide-to-powder ratio (n value) and silicon oxide-to-alkali oxide ratio (Ms value) is
explored. The reactivity of all blends prepared in this study is studied using an isothermal
calorimeter. Mine tailing-cement blends show a higher initial heat release peak than mine
tailing-slag blends, whereas their cumulative heat release is comparable for higher n values
of 0.050 to 0.100. Compressive strength tests and rheological studies were done for the
refined blends selected based on setting time criterion. Setting times and compressive
strengths are found to depend significantly on the activator parameters and binder
compositions, allowing fine-tuning of the mix proportion parameters based on the intended
end applications. The compressive strength of the selected mine tailing-slag blends and
mine tailing-cement blends are in the range of 7-40 MPa and 4-11 MPa, respectively.
Higher compressive strength is generally achieved at lower Ms and higher n values for mine
tailing-slag blends, while a higher Ms yields better compressive strength in the case of mine
tailing-cement blends. Rheological studies indicate a decrease in yield stress and viscosity
with increase in the replacement ratio, while a higher activator concentration increase both.
Oscillatory shear studies were used to evaluate the storage modulus and loss modulus of
the mine tailing binders. The paste is seen to exhibit a more elastic behavior at n values of
0.05 and 0.075, however the viscous behavior is seen to dominate at higher n value of 0.1
at similar replacement ratios and Ms value. A higher Ms value is also seen to increase the
onset point of the drop in both the storage and loss modulus of the pastes. The studied also
investigated the potential use of mine tailing blends for coating applications. The pastes
with higher alkalinity showed a lesser crack percentage, with a 10% slag replacement ratio
having a better performance compared to 20% and 30% slag replacement ratios. Overall,
the study showed that the activation parameters and mine tailings replacement level have
a significant influence on the properties of both mine tailing-slag binders and mine tailing-cement binders, thereby allowing selection of suitable mix design for the desired end
application, allowing a sustainable approach to dispose the mine tailings waste
ContributorsRamasamy Jeyaprakash, Rijul Kanth (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniam (Committee member) / Mobasher, Barzin (Committee member) / Arizona State University (Publisher)
Created2023
Description
Concrete develops strength rapidly after mixing and is highly influenced by temperature and curing process. The material characteristics and the rate of property development, along with the exposure conditions influences volume change mechanisms in concrete, and the cracking propensity of the mixtures. Furthermore, the structure geometry (due to restraint as well as the surface area-to-volume ratio) also influences shrinkage and cracking. Thus, goal of this research is to better understand and predict shrinkage cracking in concrete slab systems under different curing conditions. In this research, different concrete mixtures are evaluated on their propensity to shrink based on free shrinkage and restrained shrinkage tests.Furthermore, from the data obtained from restrained ring test, a casted slab is measured for shrinkage. Effects of different orientation of restraints are studied and compared to better understand the shrinking behavior of the concrete mixtures. The results show that the maximum shrinkage is near the edges of the slab and decreases towards the center. Shrinkage near the edges with no restraint is found out to be more than the shrinkage towards the edges with restraining effects.
ContributorsNimbalkar, Atharwa Samir (Author) / Neithalath, Narayanan (Thesis advisor) / Mobasher, Barzin (Thesis advisor) / Rajan, Subramaniam (Committee member) / Arizona State University (Publisher)
Created2023
Description
This dissertation introduces a comprehensive framework aimed at reshaping applied cybersecurity education to significantly ease the learning curve, at scale, through three synergistic innovations. These methods address the daunting educational barriers in cybersecurity, enabling learners at all levels to understand complex security concepts more easily. The first innovation, the PWN methodology, redefines the traditional Capture The Flag (CTF) model by offering a structured series of modularized, self-guided challenges. This approach helps simplify complex topics into manageable units, each building on the last, which allows students to progress at their own pace. Over five years and with over 400 systems security challenges developed, this method has effectively helped students evolve from beginners to masters of advanced security exploits. The second component is the DOJO platform, an open-source learning environment that uses containerization technology to provide a pre-configured, browser-based interface. This platform reduces the setup complexities associated with applied cybersecurity and has already given over 10,000 students immediate access to practical learning scenarios, from vulnerability discovery to advanced debugging, in a unified, user-friendly environment. Its seamless integration allows educators to quickly launch new challenges and resources, ensuring a continuous and dynamic educational experience. The third component, the SENSAI tutor, is an AI-driven tutoring system that leverages Large Language Models to offer personalized, intelligent support. Integrated with the PWN methodology and DOJO platform, SENSAI serves as an on-demand mentor, providing tailored advice and problem-solving assistance. It adapts to individual student needs, offering specific guidance and theoretical support to enhance understanding and retention of complex concepts. Together, these three components create a powerful, integrated educational strategy that not only equips students with vital cybersecurity skills but also deepens their understanding of digital vulnerabilities and the strategic thinking needed to mitigate them. This strategy prepares a new generation of cybersecurity professionals to navigate the ever-evolving threats of the digital world.
ContributorsNelson, Connor David (Author) / Shoshitaishvili, Yan (Thesis advisor) / Doupe, Adam (Thesis advisor) / Wang, Ruoyu (Committee member) / Bao, Tiffany (Committee member) / Vigna, Giovanni (Committee member) / Arizona State University (Publisher)
Created2024
Description
Being a remarkably versatile and inexpensive building material, concrete has found tremendous use in development of modern infrastructure and is the most widely used material in the world. Extensive research in the field of concrete has led to the development of a wide array of concretes with applications ranging from building of skyscrapers to paving of highways. These varied applications require special cementitious composites which can satisfy the demand for enhanced functionalities such as high strength, high durability and improved thermal characteristics among others.
The current study focuses on the fundamental understanding of such functional composites, from their microstructural design to macro-scale application. More specifically, this study investigates three different categories of functional cementitious composites. First, it discusses the differences between cementitious systems containing interground and blended limestone with and without alumina. The interground systems are found to outperform the blended systems due to differential grinding of limestone. A novel approach to deduce the particle size distribution of limestone and cement in the interground systems is proposed. Secondly, the study delves into the realm of ultra-high performance concrete, a novel material which possesses extremely high compressive-, tensile- and flexural-strength and service life as compared to regular concrete. The study presents a novel first principles-based paradigm to design economical ultra-high performance concretes using locally available materials. In the final part, the study addresses the thermal benefits of a novel type of concrete containing phase change materials. A software package was designed to perform numerical simulations to analyze temperature profiles and thermal stresses in concrete structures containing PCMs.
The design of these materials is accompanied by material characterization of cementitious binders. This has been accomplished using techniques that involve measurement of heat evolution (isothermal calorimetry), determination and quantification of reaction products (thermo-gravimetric analysis, x-ray diffraction, micro-indentation, scanning electron microscopy, energy-dispersive x-ray spectroscopy) and evaluation of pore-size distribution (mercury intrusion porosimetry). In addition, macro-scale testing has been carried out to determine compression, flexure and durability response. Numerical simulations have been carried out to understand hydration of cementitious composites, determine optimum particle packing and determine the thermal performance of these composites.
The current study focuses on the fundamental understanding of such functional composites, from their microstructural design to macro-scale application. More specifically, this study investigates three different categories of functional cementitious composites. First, it discusses the differences between cementitious systems containing interground and blended limestone with and without alumina. The interground systems are found to outperform the blended systems due to differential grinding of limestone. A novel approach to deduce the particle size distribution of limestone and cement in the interground systems is proposed. Secondly, the study delves into the realm of ultra-high performance concrete, a novel material which possesses extremely high compressive-, tensile- and flexural-strength and service life as compared to regular concrete. The study presents a novel first principles-based paradigm to design economical ultra-high performance concretes using locally available materials. In the final part, the study addresses the thermal benefits of a novel type of concrete containing phase change materials. A software package was designed to perform numerical simulations to analyze temperature profiles and thermal stresses in concrete structures containing PCMs.
The design of these materials is accompanied by material characterization of cementitious binders. This has been accomplished using techniques that involve measurement of heat evolution (isothermal calorimetry), determination and quantification of reaction products (thermo-gravimetric analysis, x-ray diffraction, micro-indentation, scanning electron microscopy, energy-dispersive x-ray spectroscopy) and evaluation of pore-size distribution (mercury intrusion porosimetry). In addition, macro-scale testing has been carried out to determine compression, flexure and durability response. Numerical simulations have been carried out to understand hydration of cementitious composites, determine optimum particle packing and determine the thermal performance of these composites.
ContributorsArora, Aashay (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Mobasher, Barzin (Committee member) / Chawla, Nikhilesh (Committee member) / Hoover, Christian G (Committee member) / Arizona State University (Publisher)
Created2018
Description
Network Management is a critical process for an enterprise to configure and monitor the network devices using cost effective methods. It is imperative for it to be robust and free from adversarial or accidental security flaws. With the advent of cloud computing and increasing demands for centralized network control, conventional management protocols like Simple Network Management Protocol (SNMP) appear inadequate and newer techniques like Network Management Datastore Architecture (NMDA) design and Network Configuration (NETCONF) have been invented. However, unlike SNMP which underwent improvements concentrating on security, the new data management and storage techniques have not been scrutinized for the inherent security flaws.
In this thesis, I identify several vulnerabilities in the widely used critical infrastructures which leverage the NMDA design. Software Defined Networking (SDN), a proponent of NMDA, heavily relies on its datastores to program and manage the network. I base my research on the security challenges put forth by the existing datastore’s design as implemented by the SDN controllers. The vulnerabilities identified in this work have a direct impact on the controllers like OpenDayLight, Open Network Operating System and their proprietary implementations (by CISCO, Ericsson, RedHat, Brocade, Juniper, etc). Using the threat detection methodology, I demonstrate how the NMDA-based implementations are vulnerable to attacks which compromise availability, integrity, and confidentiality of the network. I finally propose defense measures to address the security threats in the existing design and discuss the challenges faced while employing these countermeasures.
In this thesis, I identify several vulnerabilities in the widely used critical infrastructures which leverage the NMDA design. Software Defined Networking (SDN), a proponent of NMDA, heavily relies on its datastores to program and manage the network. I base my research on the security challenges put forth by the existing datastore’s design as implemented by the SDN controllers. The vulnerabilities identified in this work have a direct impact on the controllers like OpenDayLight, Open Network Operating System and their proprietary implementations (by CISCO, Ericsson, RedHat, Brocade, Juniper, etc). Using the threat detection methodology, I demonstrate how the NMDA-based implementations are vulnerable to attacks which compromise availability, integrity, and confidentiality of the network. I finally propose defense measures to address the security threats in the existing design and discuss the challenges faced while employing these countermeasures.
ContributorsDixit, Vaibhav Hemant (Author) / Ahn, Gail-Joon (Thesis advisor) / Doupe, Adam (Thesis advisor) / Shoshitaishvili, Yan (Committee member) / Zhao, Ziming (Committee member) / Arizona State University (Publisher)
Created2018