Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 101 - 110 of 110
Description
Driving is the coordinated operation of mind and body for movement of a vehicle, such as a car, or a bus. Driving, being considered an everyday activity for many people, still has an issue of safety. Driver distraction is becoming a critical safety problem. Speed, drunk driving as well as distracted driving are the three leading factors in the fatal car crashes. Distraction, which is defined as an excessive workload and limited attention, is the main paradigm that guides this research area. Driver behavior analysis can be used to address the distraction problem and provide an intelligent adaptive agent to work closely with the driver, fay beyond traditional algorithmic computational models. A variety of machine learning approaches has been proposed to estimate or predict drivers’ fatigue level using car data, driver status or a combination of them.
Three important features of intelligence and cognition are perception, attention and sensory memory. In this thesis, I focused on memory and attention as essential parts of highly intelligent systems. Without memory, systems will only show limited intelligence since their response would be exclusively based on spontaneous decision without considering the effect of previous events. I proposed a memory-based sequence to predict the driver behavior and distraction level using neural network. The work started with a large-scale experiment to collect data and make an artificial intelligence-friendly dataset. After that, the data was used to train a deep neural network to estimate the driver behavior. With a focus on memory by using Long Short Term Memory (LSTM) network to increase the level of intelligence in two dimensions: Forgiveness of minor glitches, and accumulation of anomalous behavior., I reduced the model error and computational expense by adding attention mechanism on the top of LSTM models. This system can be generalized to build and train highly intelligent agents in other domains.
Three important features of intelligence and cognition are perception, attention and sensory memory. In this thesis, I focused on memory and attention as essential parts of highly intelligent systems. Without memory, systems will only show limited intelligence since their response would be exclusively based on spontaneous decision without considering the effect of previous events. I proposed a memory-based sequence to predict the driver behavior and distraction level using neural network. The work started with a large-scale experiment to collect data and make an artificial intelligence-friendly dataset. After that, the data was used to train a deep neural network to estimate the driver behavior. With a focus on memory by using Long Short Term Memory (LSTM) network to increase the level of intelligence in two dimensions: Forgiveness of minor glitches, and accumulation of anomalous behavior., I reduced the model error and computational expense by adding attention mechanism on the top of LSTM models. This system can be generalized to build and train highly intelligent agents in other domains.
ContributorsMonjezi Kouchak, Shokoufeh (Author) / Gaffar, Ashraf (Thesis advisor) / Doupe, Adam (Committee member) / Ben Amor, Hani (Committee member) / Cheeks, Loretta (Committee member) / Arizona State University (Publisher)
Created2020
Description
Due to the increase in computer and database dependency, the damage caused by malicious codes increases. Moreover, gravity and the magnitude of malicious attacks by hackers grow at an unprecedented rate. A key challenge lies on detecting such malicious attacks and codes in real-time by the use of existing methods, such as a signature-based detection approach. To this end, computer scientists have attempted to classify heterogeneous types of malware on the basis of their observable characteristics. Existing literature focuses on classifying binary codes, due to the greater accessibility of malware binary than source code. Also, for the improved speed and scalability, machine learning-based approaches are widely used. Despite such merits, the machine learning-based approach critically lacks the interpretability of its outcome, thus restricts understandings of why a given code belongs to a particular type of malicious malware and, importantly, why some portions of a code are reused very often by hackers. In this light, this study aims to enhance understanding of malware by directly investigating reused codes and uncovering their characteristics.
To examine reused codes in malware, both malware with source code and malware with binary code are considered in this thesis. For malware with source code, reused code chunks in the Mirai botnet. This study lists frequently reused code chunks and analyzes the characteristics and location of the code. For malware with binary code, this study performs reverse engineering on the binary code for human readers to comprehend, visually inspects reused codes in binary ransomware code, and illustrates the functionality of the reused codes on the basis of similar behaviors and tactics.
This study makes a novel contribution to the literature by directly investigating the characteristics of reused code in malware. The findings of the study can help cybersecurity practitioners and scholars increase the performance of malware classification.
To examine reused codes in malware, both malware with source code and malware with binary code are considered in this thesis. For malware with source code, reused code chunks in the Mirai botnet. This study lists frequently reused code chunks and analyzes the characteristics and location of the code. For malware with binary code, this study performs reverse engineering on the binary code for human readers to comprehend, visually inspects reused codes in binary ransomware code, and illustrates the functionality of the reused codes on the basis of similar behaviors and tactics.
This study makes a novel contribution to the literature by directly investigating the characteristics of reused code in malware. The findings of the study can help cybersecurity practitioners and scholars increase the performance of malware classification.
ContributorsLEe, Yeonjung (Author) / Bao, Youzhi (Thesis advisor) / Doupe, Adam (Committee member) / Shoshitaishvili, Yan (Committee member) / Arizona State University (Publisher)
Created2020
Description
The high levels of pollution associated with mining activities necessitate more efficient methods of treating mining effluent before it is released into the environment. Phosphate -mining wastewater contains high concentrations of sulfate that can be removed and recovered as elemental sulfur (S0), which is a useful resource. The Membrane Biofilm Reactor (MBfR) uses gas-transfer membranes for the delivery of gases to microorganisms that carry out oxidation-reduction reactions that lead to the breakdown of contaminants. The two main microorganisms involved in the treatment of sulfate wastewater using the MBfR are sulfate-reducing bacteria (SRB) for the reduction of sulfate into sulfide and sulfur-oxidizing bacteria (SOB) for the oxidation of sulfide into S0. In this work, the kinetic processes involved in sulfate reduction and sulfide oxidation for SRB and SOB were modeled using the steady-state biofilm model and mass balances on a completely mixed biofilm reactor. The model results identified trends of substrate removal, biofilm accumulation, and hydraulic retention time (HRT) for the design of the sulfate-treatment system. The HRT required for 97.5% sulfate removal was about 0.1 d and that for 97.5% sulfide removal about 0.2 d. Higher levels of biofilm accumulation occurred with sulfide oxidation due to the larger biomass yield of the SOB. The needed delivery of H2 gas required for sulfate reduction and O2 gas for sulfide oxidation, as well as the alkalinity changes, also were determined based on the removal levels.
ContributorsAppiah Nsiah, Gloria (Author) / Rittmann, Bruce BER (Thesis advisor) / Abbaszadegan, Morteza (Committee member) / Fox, Peter (Committee member) / Arizona State University (Publisher)
Created2021
Description
Malicious hackers utilize the World Wide Web to share knowledge. Previous work has demonstrated that information mined from online hacking communities can be used as precursors to cyber-attacks. In a threatening scenario, where security alert systems are facing high false positive rates, understanding the people behind cyber incidents can help reduce the risk of attacks. However, the rapidly evolving nature of those communities leads to limitations still largely unexplored, such as: who are the skilled and influential individuals forming those groups, how they self-organize along the lines of technical expertise, how ideas propagate within them, and which internal patterns can signal imminent cyber offensives? In this dissertation, I have studied four key parts of this complex problem set. Initially, I leverage content, social network, and seniority analysis to mine key-hackers on darkweb forums, identifying skilled and influential individuals who are likely to succeed in their cybercriminal goals. Next, as hackers often use Web platforms to advertise and recruit collaborators, I analyze how social influence contributes to user engagement online. On social media, two time constraints are proposed to extend standard influence measures, which increases their correlation with adoption probability and consequently improves hashtag adoption prediction. On darkweb forums, the prediction of where and when hackers will post a message in the near future is accomplished by analyzing their recurrent interactions with other hackers. After that, I demonstrate how vendors of malware and malicious exploits organically form hidden organizations on darkweb marketplaces, obtaining significant consistency across the vendors’ communities extracted using the similarity of their products in different networks. Finally, I predict imminent cyber-attacks correlating malicious hacking activity on darkweb forums with real-world cyber incidents, evidencing how social indicators are crucial for the performance of the proposed model. This research is a hybrid of social network analysis (SNA), machine learning (ML), evolutionary computation (EC), and temporal logic (TL), presenting expressive contributions to empower cyber defense.
ContributorsSantana Marin, Ericsson (Author) / Shakarian, Paulo (Thesis advisor) / Doupe, Adam (Committee member) / Liu, Huan (Committee member) / Ferrara, Emilio (Committee member) / Arizona State University (Publisher)
Created2020
Description
Many residences from student apartment units to family homes use a range of smart devices to make the day-to-day lives of the residents safer and more convenient. The ability to remotely access these devices has further increased their convenience, but it comes with the increased risk of vulnerable devices being exploited to achieve unauthorized access or to conduct surveillance on the users. This highlights the need for an access control system to securely restrict home device access to authorized users only. Existing approaches for securing smart homes use less secure authentication methods, do not allow for data ownership or fine-grained access control, and do not reliably store credential modification records, access records, or access policy modification records. These records can be a valuable resource to have available in the case of a security incident.In this thesis, a secure and efficient remote mutual authentication system with fine-grained access control integrating blockchain and digital signatures to authenticate users, authenticate the home gateway, and provide reliable auditing of the credential modifications, access history, and access policy modifications of the devices is presented. The immutability and verifiability properties of blockchain make it useful for securely storing these records. In this approach, a smart contract is created in the blockchain to keep track of authorized users, manage the access policy, and record requests for access or control of the home devices. A private blockchain is used to provide trust and privacy, which is necessary for a smart home system. Elliptic curve digital signatures are used to verify identities because the shorter key sizes and signature times are more adapted to Internet of Things contexts. The approach presented in this thesis is better than existing approaches because it provides fine-grained access control, and reliably stores credential modification records, access records, and access policy modification records. The approach was implemented and evaluated using Hyperledger, a private open-source blockchain, and the results show that this approach has significant additional security benefits with negligible additional overhead cost.
ContributorsVuong, Anna (Author) / Yau, Stephen S (Thesis advisor) / Doupe, Adam (Committee member) / Ghayekhloo, Samira (Committee member) / Arizona State University (Publisher)
Created2021
Description
De facto potable reuse (DFR) occurs when surface water sources at drinking water treatment plants (DWTPs) contain treated effluents from upstream wastewater treatment plants (WWTPs). Contaminants of emerging concerns (CECs) originate from treated effluents (e.g., unregulated disinfection by-products, pathogenic microorganisms as Cryptosporidium oocyst, Giardia cyst, and Norovirus) can be present in surface water and pose human health risks linked to CECs. Previously developed De facto Reuse Incidence in our Nations Consumable Supply (DRINCS) model predicted DFR for the national largest DWTPs that serve >10,000 people (N = 2,056 SW intakes at 1,210 DWTPs). The dissertation aims to quantify DFR at all surface water intakes for smaller DWTPs serving ≤10,000 people across the United States and develop a programmed ArcGIS tool for proximity analysis between upstream WWTPs and DWTPs. The tested hypothesis is whether DWTPs serving ≤10,000 people are more likely to be impacted by DFR than larger systems serving > 10,000 people.The original DRINCS model was expanded to include all smaller DWTPs (N = 6,045 SW intakes at 3,984 DWTPs) in the U.S. First, results for Texas predicted that two-thirds of all SW intakes were impacted by at least one WWTP upstream. The level of DFR at SW intakes in Texas ranged between 1% to 20% under average flow and exceeded 90% during mild droughts. Smaller DWTPs in Texas had a higher frequency of DFR than larger systems while < 10% of these DWTPs employed advanced technology (AT) capable of removing CECs. Second, nationally over 40% of surface water intakes at all DWTPs were impacted by DFR under average flow (2,917 of 6,826). Smaller DWTPs had a higher frequency (1,504 and 1,413, respectively) of being impacted by upstream WWTP discharges than larger DWTPs. Third, the difference in DFR levels at smaller versus larger DWTPs was statistically unclear (t-test, p = 0.274). Smaller communities could have high risks to CECs as they rely on surface water from lower-order streams impacted by DFR. Furthermore, smaller DWTPs lack more than twice as advanced unit processes as larger DWTPs with 52.1% and 23%, respectively. DFR levels for DWTPs serving > 10,000 people were statistically higher on mid-size order streams (3, 5, and 8) than those for smaller DWTPs. Finally, DWTPs serving > 10,000 people could pose risks to a population impacted by DFR > 1% as 40 times as those served by smaller DWTPs with 71 million and 1.7 million people, respectively. The total exposed population to risks of CECs served by DWTPs impacted by upstream WWTP discharges (DFR >10%) was estimated at 12.3 million people in the United States. Future studies can use DRINCS results to conduct an epidemiological risk assessment for impacted communities and identify communities that would benefit from advanced technology to remove CECs.
ContributorsNguyen, Thuy Thi Thu (Author) / Westerhoff, Paul K (Thesis advisor) / Hristovski, Kiril (Committee member) / Fox, Peter (Committee member) / Muenich, Rebecca (Committee member) / Quay, Ray (Committee member) / Arizona State University (Publisher)
Created2020
Description
Research in microbial biofuels has dramatically increased over the last decade. The bulk of this research has focused on increasing the production yields of cyanobacteria and algal cells and improving extraction processes. However, there has been little to no research on the potential impact of viruses on the yields of these phototrophic microbes for biofuel production. Viruses have the potential to significantly reduce microbial populations and limit their growth rates. It is therefore important to understand how viruses affect phototrophic microbes and the prevalence of these viruses in the environment. For this study, phototrophic microbes were grown in glass bioreactors, under continuous light and aeration. Detection and quantification of viruses of both environmental and laboratory microbial strains were measured through the use of a plaque assay. Plates were incubated at 25º C under continuous direct florescent light. Several environmental samples were taken from Tempe Town Lake (Tempe, AZ) and all the samples tested positive for viruses. Virus free phototrophic microbes were obtained from plaque assay plates by using a sterile loop to scoop up a virus free portion of the microbial lawn and transferred into a new bioreactor. Isolated cells were confirmed virus free through subsequent plaque assays. Viruses were detected from the bench scale bioreactors of Cyanobacteria Synechocystis PCC 6803 and the environmental samples. Viruses were consistently present through subsequent passage in fresh cultures; demonstrating viral contamination can be a chronic problem. In addition TEM was performed to examine presence or viral attachment to cyanobacterial cells and to characterize viral particles morphology. Electron micrographs obtained confirmed viral attachment and that the viruses detected were all of a similar size and shape. Particle sizes were measured to be approximately 50-60 nm. Cell reduction was observed as a decrease in optical density, with a transition from a dark green to a yellow green color for the cultures. Phototrophic microbial viruses were demonstrated to persist in the natural environment and to cause a reduction in algal populations in the bioreactors. Therefore it is likely that viruses could have a significant impact on microbial biofuel production by limiting the yields of production ponds.
ContributorsKraft, Kyle (Author) / Abbaszadegan, Morteza (Thesis advisor) / Alum, Absar (Committee member) / Fox, Peter (Committee member) / Arizona State University (Publisher)
Created2014
Description
Radioactive cesium (137Cs), released from nuclear power plants and nuclear accidental releases, is a problem due to difficulties regarding its removal. Efforts have been focused on removing cesium and the remediation of the contaminated environment. Traditional treatment techniques include Prussian blue and nano zero-valent ion (nZVI) and nano-Fe/Cu particles to remove Cs from water; however, they are not efficient at removing Cs when present at low concentrations of about 10 parts-per-billion (ppb), typical of concentrations found in the radioactive contaminated sites.
The objective of this study was to develop an innovative and simple method to remove Cs+ present at low concentrations by engineering a proteoliposome transporter composed of an uptake protein reconstituted into a liposome vesicle. To achieve this, the uptake protein, Kup, from E. coli, was isolated through protein extraction and purification procedures. The new and simple extraction methodology developed in this study was highly efficient and resulted in purified Kup at ~1 mg/mL. A new method was also developed to insert purified Kup protein into the bilayers of liposome vesicles. Finally, removal of CsCl (10 and 100 ppb) was demonstrated by spiking the constructed proteoliposome in lab-fortified water, followed by incubation and ultracentrifugation, and measuring Cs+ with inductively coupled plasma mass spectrometry (ICP-MS).
The ICP-MS results from testing water contaminated with 100 ppb CsCl, revealed that adding 0.1 – 8 mL of Kup proteoliposome resulted in 0.29 – 12.7% Cs removal. Addition of 0.1 – 2 mL of proteoliposome to water contaminated with 10 ppb CsCl resulted in 0.65 – 3.43% Cs removal. These removal efficiencies were greater than the control, liposome with no protein.
A linear relationship was observed between the amount of proteoliposome added to the contaminated water and removal percentage. Consequently, by adding more volumes of proteoliposome, removal can be simply improved. This suggests that with ~ 60-70 mL of proteoliposome, removal of about 90% can be achieved. The novel technique developed herein is a contribution to emerging technologies in the water and wastewater treatment industry.
The objective of this study was to develop an innovative and simple method to remove Cs+ present at low concentrations by engineering a proteoliposome transporter composed of an uptake protein reconstituted into a liposome vesicle. To achieve this, the uptake protein, Kup, from E. coli, was isolated through protein extraction and purification procedures. The new and simple extraction methodology developed in this study was highly efficient and resulted in purified Kup at ~1 mg/mL. A new method was also developed to insert purified Kup protein into the bilayers of liposome vesicles. Finally, removal of CsCl (10 and 100 ppb) was demonstrated by spiking the constructed proteoliposome in lab-fortified water, followed by incubation and ultracentrifugation, and measuring Cs+ with inductively coupled plasma mass spectrometry (ICP-MS).
The ICP-MS results from testing water contaminated with 100 ppb CsCl, revealed that adding 0.1 – 8 mL of Kup proteoliposome resulted in 0.29 – 12.7% Cs removal. Addition of 0.1 – 2 mL of proteoliposome to water contaminated with 10 ppb CsCl resulted in 0.65 – 3.43% Cs removal. These removal efficiencies were greater than the control, liposome with no protein.
A linear relationship was observed between the amount of proteoliposome added to the contaminated water and removal percentage. Consequently, by adding more volumes of proteoliposome, removal can be simply improved. This suggests that with ~ 60-70 mL of proteoliposome, removal of about 90% can be achieved. The novel technique developed herein is a contribution to emerging technologies in the water and wastewater treatment industry.
ContributorsHakim Elahi, Sepideh (Author) / Conroy-Ben, Otakuye (Thesis advisor) / Abbaszadegan, Morteza (Committee member) / Fox, Peter (Committee member) / Arizona State University (Publisher)
Created2018
Description
Carbon dioxide (CO2) is one of the most dangerous greenhouse gas. Its concentration in the atmosphere has increased to very high levels since the industrial revolution. This continues to be a threat due to increasing energy demands. 60% of the worlds global emissions come from automobiles and other such moving sources. Hence, to stay within safe limits, it is extremely important to curb current emissions and remove those which have already been emitted. Out of many available technologies, one such technology is the moisture swing based air capture technology that makes use of resin material that absorbs CO2 when it is dry and releases it when it is wet. A mathematical model was developed to better understand the mechanism of this process. In order to validate this model, numerical simulation and experimentation was done. Once the mechanism was proved, it was seen that there are many factors and parameters that govern this process. Some of these do not have definite value. To find the best fit value for these parameters, an optimized fitting routine needs to be developed that can minimize the standard deviation of the error. This thesis looks into ways in which the optimization of parameters can be done and the possible future work by using substantial data.
ContributorsChopra, Vinuta (Author) / Lackner, Klaus S (Thesis advisor) / Fox, Peter (Committee member) / Wright, Allen (Committee member) / Arizona State University (Publisher)
Created2016
Description
Despite incremental improvements over decades, academic planning solutions see relatively little use in many industrial domains despite the relevance of planning paradigms to those problems. This work observes four shortfalls of existing academic solutions which contribute to this lack of adoption.
To address these shortfalls this work defines model-independent semantics for planning and introduces an extensible planning library. This library is shown to produce feasible results on an existing benchmark domain, overcome the usual modeling limitations of traditional planners, and accommodate domain-dependent knowledge about the problem structure within the planning process.
To address these shortfalls this work defines model-independent semantics for planning and introduces an extensible planning library. This library is shown to produce feasible results on an existing benchmark domain, overcome the usual modeling limitations of traditional planners, and accommodate domain-dependent knowledge about the problem structure within the planning process.
ContributorsJonas, Michael (Author) / Gaffar, Ashraf (Thesis advisor) / Fainekos, Georgios (Committee member) / Doupe, Adam (Committee member) / Herley, Cormac (Committee member) / Arizona State University (Publisher)
Created2016