Matching Items (46)
Description
Internet browsers are today capable of warning internet users of a potential phishing attack. Browsers identify these websites by referring to blacklists of reported phishing websites maintained by trusted organizations like Google, Phishtank etc. On identifying a Unified Resource Locator (URL) requested by a user as a reported phishing URL, browsers like Mozilla Firefox and Google Chrome display an 'active' warning message in an attempt to stop the user from making a potentially dangerous decision of visiting the website and sharing confidential information like username-password, credit card information, social security number etc.
However, these warnings are not always successful at safeguarding the user from a phishing attack. On several occasions, users ignore these warnings and 'click through' them, eventually landing at the potentially dangerous website and giving away confidential information. Failure to understand the warning, failure to differentiate different types of browser warnings, diminishing trust on browser warnings due to repeated encounter are some of the reasons that make users ignore these warnings. It is important to address these factors in order to eventually improve a user’s reaction to these warnings.
In this thesis, I propose a novel design to improve the effectiveness and reliability of phishing warning messages. This design utilizes the name of the target website that a fake website is mimicking, to display a simple, easy to understand and interactive warning message with the primary objective of keeping the user away from a potentially spoof website.
However, these warnings are not always successful at safeguarding the user from a phishing attack. On several occasions, users ignore these warnings and 'click through' them, eventually landing at the potentially dangerous website and giving away confidential information. Failure to understand the warning, failure to differentiate different types of browser warnings, diminishing trust on browser warnings due to repeated encounter are some of the reasons that make users ignore these warnings. It is important to address these factors in order to eventually improve a user’s reaction to these warnings.
In this thesis, I propose a novel design to improve the effectiveness and reliability of phishing warning messages. This design utilizes the name of the target website that a fake website is mimicking, to display a simple, easy to understand and interactive warning message with the primary objective of keeping the user away from a potentially spoof website.
ContributorsSharma, Satyabrata (Author) / Bazzi, Rida (Thesis advisor) / Walker, Erin (Committee member) / Gaffar, Ashraf (Committee member) / Arizona State University (Publisher)
Created2015
Description
Many web search improvements have been developed since the advent of the modern search engine, but one underrepresented area is the application of specific customizations to search results for educational web sites. In order to address this issue and improve the relevance of search results in automated learning environments, this work has integrated context-aware search principles with applications of preference based re-ranking and query modifications. This research investigates several aspects of context-aware search principles, specifically context-sensitive and preference based re-ranking of results which take user inputs as to their preferred content, and combines this with search query modifications which automatically search for a variety of modified terms based on the given search query, integrating these results into the overall re-ranking for the context. The result of this work is a novel web search algorithm which could be applied to any online learning environment attempting to collect relevant resources for learning about a given topic. The algorithm has been evaluated through user studies comparing traditional search results to the context-aware results returned through the algorithm for a given topic. These studies explore how this integration of methods could provide improved relevance in the search results returned when compared against other modern search engines.
ContributorsVan Egmond, Eric (Author) / Burleson, Winslow (Thesis advisor) / Syrotiuk, Violet (Thesis advisor) / Nelson, Brian (Committee member) / Arizona State University (Publisher)
Created2014
Description
Corporations invest considerable resources to create, preserve and analyze
their data; yet while organizations are interested in protecting against
unauthorized data transfer, there lacks a comprehensive metric to discriminate
what data are at risk of leaking.
This thesis motivates the need for a quantitative leakage risk metric, and
provides a risk assessment system, called Whispers, for computing it. Using
unsupervised machine learning techniques, Whispers uncovers themes in an
organization's document corpus, including previously unknown or unclassified
data. Then, by correlating the document with its authors, Whispers can
identify which data are easier to contain, and conversely which are at risk.
Using the Enron email database, Whispers constructs a social network segmented
by topic themes. This graph uncovers communication channels within the
organization. Using this social network, Whispers determines the risk of each
topic by measuring the rate at which simulated leaks are not detected. For the
Enron set, Whispers identified 18 separate topic themes between January 1999
and December 2000. The highest risk data emanated from the legal department
with a leakage risk as high as 60%.
their data; yet while organizations are interested in protecting against
unauthorized data transfer, there lacks a comprehensive metric to discriminate
what data are at risk of leaking.
This thesis motivates the need for a quantitative leakage risk metric, and
provides a risk assessment system, called Whispers, for computing it. Using
unsupervised machine learning techniques, Whispers uncovers themes in an
organization's document corpus, including previously unknown or unclassified
data. Then, by correlating the document with its authors, Whispers can
identify which data are easier to contain, and conversely which are at risk.
Using the Enron email database, Whispers constructs a social network segmented
by topic themes. This graph uncovers communication channels within the
organization. Using this social network, Whispers determines the risk of each
topic by measuring the rate at which simulated leaks are not detected. For the
Enron set, Whispers identified 18 separate topic themes between January 1999
and December 2000. The highest risk data emanated from the legal department
with a leakage risk as high as 60%.
ContributorsWright, Jeremy (Author) / Syrotiuk, Violet (Thesis advisor) / Davulcu, Hasan (Committee member) / Yau, Stephen (Committee member) / Arizona State University (Publisher)
Created2014
Description
This thesis proposed a novel approach to establish the trust model in a social network scenario based on users' emails. Email is one of the most important social connections nowadays. By analyzing email exchange activities among users, a social network trust model can be established to judge the trust rate between each two users. The whole trust checking process is divided into two steps: local checking and remote checking. Local checking directly contacts the email server to calculate the trust rate based on user's own email communication history. Remote checking is a distributed computing process to get help from user's social network friends and built the trust rate together. The email-based trust model is built upon a cloud computing framework called MobiCloud. Inside MobiCloud, each user occupies a virtual machine which can directly communicate with others. Based on this feature, the distributed trust model is implemented as a combination of local analysis and remote analysis in the cloud. Experiment results show that the trust evaluation model can give accurate trust rate even in a small scale social network which does not have lots of social connections. With this trust model, the security in both social network services and email communication could be improved.
ContributorsZhong, Yunji (Author) / Huang, Dijiang (Thesis advisor) / Dasgupta, Partha (Committee member) / Syrotiuk, Violet (Committee member) / Arizona State University (Publisher)
Created2011
Description
Despite the various driver assistance systems and electronics, the threat to life of driver, passengers and other people on the road still persists. With the growth in technology, the use of in-vehicle devices with a plethora of buttons and features is increasing resulting in increased distraction. Recently, speech recognition has emerged as an alternative to distraction and has the potential to be beneficial. However, considering the fact that automotive environment is dynamic and noisy in nature, distraction may not arise from the manual interaction, but due to the cognitive load. Hence, speech recognition certainly cannot be a reliable mode of communication.
The thesis is focused on proposing a simultaneous multimodal approach for designing interface between driver and vehicle with a goal to enable the driver to be more attentive to the driving tasks and spend less time fiddling with distractive tasks. By analyzing the human-human multimodal interaction techniques, new modes have been identified and experimented, especially suitable for the automotive context. The identified modes are touch, speech, graphics, voice-tip and text-tip. The multiple modes are intended to work collectively to make the interaction more intuitive and natural. In order to obtain a minimalist user-centered design for the center stack, various design principles such as 80/20 rule, contour bias, affordance, flexibility-usability trade-off etc. have been implemented on the prototypes. The prototype was developed using the Dragon software development kit on android platform for speech recognition.
In the present study, the driver behavior was investigated in an experiment conducted on the DriveSafety driving simulator DS-600s. Twelve volunteers drove the simulator under two conditions: (1) accessing the center stack applications using touch only and (2) accessing the applications using speech with offered text-tip. The duration for which user looked away from the road (eyes-off-road) was measured manually for each scenario. Comparison of results proved that eyes-off-road time is less for the second scenario. The minimalist design with 8-10 icons per screen proved to be effective as all the readings were within the driver distraction recommendations (eyes-off-road time < 2sec per screen) defined by NHTSA.
The thesis is focused on proposing a simultaneous multimodal approach for designing interface between driver and vehicle with a goal to enable the driver to be more attentive to the driving tasks and spend less time fiddling with distractive tasks. By analyzing the human-human multimodal interaction techniques, new modes have been identified and experimented, especially suitable for the automotive context. The identified modes are touch, speech, graphics, voice-tip and text-tip. The multiple modes are intended to work collectively to make the interaction more intuitive and natural. In order to obtain a minimalist user-centered design for the center stack, various design principles such as 80/20 rule, contour bias, affordance, flexibility-usability trade-off etc. have been implemented on the prototypes. The prototype was developed using the Dragon software development kit on android platform for speech recognition.
In the present study, the driver behavior was investigated in an experiment conducted on the DriveSafety driving simulator DS-600s. Twelve volunteers drove the simulator under two conditions: (1) accessing the center stack applications using touch only and (2) accessing the applications using speech with offered text-tip. The duration for which user looked away from the road (eyes-off-road) was measured manually for each scenario. Comparison of results proved that eyes-off-road time is less for the second scenario. The minimalist design with 8-10 icons per screen proved to be effective as all the readings were within the driver distraction recommendations (eyes-off-road time < 2sec per screen) defined by NHTSA.
ContributorsMittal, Richa (Author) / Gaffar, Ashraf (Thesis advisor) / Femiani, John (Committee member) / Gray, Robert (Committee member) / Arizona State University (Publisher)
Created2015
Description
Exhaustive testing is generally infeasible except in the smallest of systems. Research
has shown that testing the interactions among fewer (up to 6) components is generally
sufficient while retaining the capability to detect up to 99% of defects. This leads to a
substantial decrease in the number of tests. Covering arrays are combinatorial objects
that guarantee that every interaction is tested at least once.
In the absence of direct constructions, forming small covering arrays is generally
an expensive computational task. Algorithms to generate covering arrays have been
extensively studied yet no single algorithm provides the smallest solution. More
recently research has been directed towards a new technique called post-optimization.
These algorithms take an existing covering array and attempt to reduce its size.
This thesis presents a new idea for post-optimization by representing covering
arrays as graphs. Some properties of these graphs are established and the results are
contrasted with existing post-optimization algorithms. The idea is then generalized to
close variants of covering arrays with surprising results which in some cases reduce
the size by 30%. Applications of the method to generation and test prioritization are
studied and some interesting results are reported.
has shown that testing the interactions among fewer (up to 6) components is generally
sufficient while retaining the capability to detect up to 99% of defects. This leads to a
substantial decrease in the number of tests. Covering arrays are combinatorial objects
that guarantee that every interaction is tested at least once.
In the absence of direct constructions, forming small covering arrays is generally
an expensive computational task. Algorithms to generate covering arrays have been
extensively studied yet no single algorithm provides the smallest solution. More
recently research has been directed towards a new technique called post-optimization.
These algorithms take an existing covering array and attempt to reduce its size.
This thesis presents a new idea for post-optimization by representing covering
arrays as graphs. Some properties of these graphs are established and the results are
contrasted with existing post-optimization algorithms. The idea is then generalized to
close variants of covering arrays with surprising results which in some cases reduce
the size by 30%. Applications of the method to generation and test prioritization are
studied and some interesting results are reported.
ContributorsKaria, Rushang Vinod (Author) / Colbourn, Charles J (Thesis advisor) / Syrotiuk, Violet (Committee member) / Richa, Andréa W. (Committee member) / Arizona State University (Publisher)
Created2015
Description
Assemblers and compilers provide feedback to a programmer in the form of error messages. These error messages become input to the debugging model of the programmer. For the programmer to fix an error, they should first locate the error in the program, understand what is causing that error, and finally resolve that error. Error messages play an important role in all three stages of fixing of errors. This thesis studies the effects of error messages in the context of teaching programming. Given an error message, this work investigates how it effects student’s way of 1) understanding the error, and 2) fixing the error. As part of the study, three error message types were developed – Default, Link and Example, to better understand the effects of error messages. The Default type provides an assembler-centric single line error message, the Link type provides a program-centric detailed error description with a hyperlink for more information, and the Example type provides a program centric detailed error description with a relevant example. All these error message types were developed for assembly language programming. A think aloud programming exercise was conducted as part of the study to capture the student programmer’s knowledge model. Different codes were developed to analyze the data collected as part of think aloud exercise. After transcribing, coding, and analyzing the data, it was found that the Link type of error message helped to fix the error in less time and with fewer steps. Among the three types, the Link type of error message also resulted in a significantly higher ratio of correct to incorrect steps taken by the programmer to fix the error.
ContributorsBeejady Murthy Kadekar, Harsha Kadekar (Author) / Sohoni, Sohum (Thesis advisor) / Craig, Scotty D. (Committee member) / Jordan, Shawn S (Committee member) / Gary, Kevin A (Committee member) / Arizona State University (Publisher)
Created2017
Description
Each programming language has a compiler associated with it which helps to identify logical or syntactical errors in the program. These compiler error messages play important part in the form of formative feedback for the programmer. Thus, the error messages should be constructed carefully, considering the affective and cognitive needs of programmers. This is especially true for systems that are used in educational settings, as the messages are typically seen by students who are novice programmers. If the error messages are hard to understand then they might discourage students from understanding or learning the programming language. The primary goal of this research is to identify methods to make the error messages more effective so that students can understand them better and simultaneously learn from their mistakes. This study is focused on understanding how the error message affects the understanding of the error and the approach students take to solve the error. In this study, three types of error messages were provided to the students. The first type is Default type error message which is an assembler centric error message. The second type is Link type error message which is a descriptive error message along with a link to the appropriate section of the PLP manual. The third type is Example type error message which is again a descriptive error message with an example of the similar type of error along with correction step. All these error types were developed for the PLP assembly language. A think-aloud experiment was designed and conducted on the students. The experiment was later transcribed and coded to understand different approach students take to solve different type of error message. After analyzing the result of the think-aloud experiment it was found that student read the Link type error message completely and they understood and learned from the error message to solve the error. The results also indicated that Link type was more helpful compare to other types of error message. The Link type made error solving process more effective compared to other error types.
ContributorsTanpure, Siddhant Bapusaheb (Author) / Sohoni, Sohum (Thesis advisor) / Gary, Kevin A (Committee member) / Craig, Scotty D. (Committee member) / Arizona State University (Publisher)
Created2018
Description
Medium access control (MAC) is a fundamental problem in wireless networks.
In ad-hoc wireless networks especially, many of the performance and scaling issues
these networks face can be attributed to their use of the core IEEE 802.11 MAC
protocol: distributed coordination function (DCF). Smoothed Airtime Linear Tuning
(SALT) is a new contention window tuning algorithm proposed to address some of the
deficiencies of DCF in 802.11 ad-hoc networks. SALT works alongside a new user level
and optimized implementation of REACT, a distributed resource allocation protocol,
to ensure that each node secures the amount of airtime allocated to it by REACT.
The algorithm accomplishes that by tuning the contention window size parameter
that is part of the 802.11 backoff process. SALT converges more tightly on airtime
allocations than a contention window tuning algorithm from previous work and this
increases fairness in transmission opportunities and reduces jitter more than either
802.11 DCF or the other tuning algorithm. REACT and SALT were also extended
to the multi-hop flow scenario with the introduction of a new airtime reservation
algorithm. With a reservation in place multi-hop TCP throughput actually increased
when running SALT and REACT as compared to 802.11 DCF, and the combination of
protocols still managed to maintain its fairness and jitter advantages. All experiments
were performed on a wireless testbed, not in simulation.
In ad-hoc wireless networks especially, many of the performance and scaling issues
these networks face can be attributed to their use of the core IEEE 802.11 MAC
protocol: distributed coordination function (DCF). Smoothed Airtime Linear Tuning
(SALT) is a new contention window tuning algorithm proposed to address some of the
deficiencies of DCF in 802.11 ad-hoc networks. SALT works alongside a new user level
and optimized implementation of REACT, a distributed resource allocation protocol,
to ensure that each node secures the amount of airtime allocated to it by REACT.
The algorithm accomplishes that by tuning the contention window size parameter
that is part of the 802.11 backoff process. SALT converges more tightly on airtime
allocations than a contention window tuning algorithm from previous work and this
increases fairness in transmission opportunities and reduces jitter more than either
802.11 DCF or the other tuning algorithm. REACT and SALT were also extended
to the multi-hop flow scenario with the introduction of a new airtime reservation
algorithm. With a reservation in place multi-hop TCP throughput actually increased
when running SALT and REACT as compared to 802.11 DCF, and the combination of
protocols still managed to maintain its fairness and jitter advantages. All experiments
were performed on a wireless testbed, not in simulation.
ContributorsMellott, Matthew (Author) / Syrotiuk, Violet (Thesis advisor) / Colbourn, Charles (Committee member) / Tinnirello, Ilenia (Committee member) / Arizona State University (Publisher)
Created2018
Description
As an example of "big data," we consider a repository of Arctic sea ice concentration data collected from satellites over the years 1979-2005. The data is represented by a graph, where vertices correspond to measurement points, and an edge is inserted between two vertices if the Pearson correlation coefficient between them exceeds a threshold. We investigate new questions about the structure of the graph related to betweenness, closeness centrality, vertex degrees, and characteristic path length. We also investigate whether an offset of weeks and years in graph generation results in a cosine similarity value that differs significantly from expected values. Finally, we relate the computational results to trends in Arctic ice.
ContributorsDougherty, Ryan Edward (Author) / Syrotiuk, Violet (Thesis director) / Colbourn, Charles (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2015-05