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Description
Cyber threats are growing in number and sophistication making it important to continually study and improve all dimensions of digital forensics. Teamwork in forensic analysis has been overlooked in systems even though forensics relies on collaboration. Forensic analysis lacks a system that is flexible and available on different electronic devices which are being used and incorporated into everyday life. For instance, cellphones or tablets that are easy to bring on-the-go to sites where the first steps of forensic analysis is done. Due to the present day conversion to online accessibility, most electronic devices connect to the internet. Squeegee is a proof of concept that forensic analysis can be done on the web. The forensic analysis expansion to the web opens many doors to collaboration and accessibility.
ContributorsJuntiff, Samantha Maria (Author) / Ahn, Gail-Joon (Thesis director) / Kashiwagi, Jacob (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2015-05
Description
Abstract
My thesis aims to uncover the ultimate strategy behind short form visual stories, otherwise known as the digital advertisment. In this thesis, I analyze traditional storytelling, visual storytelling, and short-form visual storytelling in order to uncover the best practices advertisers should use when crafting a digital advertisement.
Storytelling “reveals elements and images of a story while also catalyzing the imagination of the listener” (National Storytelling Network, 2017). This tradition has two purposes for society: a neurological structure, and a social mechanism (for historic preservation, human interaction, and a vehicle for connecting with others) (Gottshcall, 2012; Scott, 2012; Paul, 2012; Woodside, 2008).
Visual Storytelling is “using photography, illustration, video, (usually with a musical enhancement) to guide” the human brain along a plotline, and has an unlimited timeframe (Ron, 2017). There are seven key elements to effective visual storytelling: A listener/audience, an element of realism coupled with escapism, a focus on the dread of life, an element of the unknown, emotion, simplicity, and a three-part plot structure (Andrews, 2010; ProQuest, 2012; Zak, 2014; Stanton, 2014; Reagan, 2016; Jarvis, 2014; Petrick, 2014)
In the words of Sholmi Ron, from a marketing perspective, “Visual [short hand] Storytelling is a marketing strategy that communicates powerful ideas through a compelling story arc, with your customer at the heart of the story, and delivered through interactive and immersive visual media – in order to create profitable customer engagements" (Ron, 2017). This advertising strategy has four best practices: non-obvious logo placement, a comedic emotion, multiple emotional arcs, and a relevant message (Golan, 2017; Teixeira, 2015; Graves, 2017, Teixeira, 2017). These are important to understand because, in 2017, online consumers can be described as skeptical, conscious of content, individualistic, and drawn to authenticity (Teixeira, 2014).
To supplement my findings, I conducted primary research by analyzing the 2017 Super Bowl videos against a criteria created using the best practices previously identified (in Part 1 and Part 2). Through the data collection of the 66 videos, I uncovered the most popular plotline is "fall than rise," the most popular emotions are humor, inspiration, and empathy and people tend to have a preference towards videos that are more realistic and simplistic in nature.
In the end, I recommend that advertisers identify an authentic yet relevant message, while employing a comedic, inspirational, or empathic tone, and that they place their ads exclusively for their target market. Additionally, producers should use a fall then rise plotline (with multiple mini plot peaks and valleys), a "logo-pulsing" strategy, and a minimal amount of characters and settings to keep the audience's focus on the ad’s message.
My thesis aims to uncover the ultimate strategy behind short form visual stories, otherwise known as the digital advertisment. In this thesis, I analyze traditional storytelling, visual storytelling, and short-form visual storytelling in order to uncover the best practices advertisers should use when crafting a digital advertisement.
Storytelling “reveals elements and images of a story while also catalyzing the imagination of the listener” (National Storytelling Network, 2017). This tradition has two purposes for society: a neurological structure, and a social mechanism (for historic preservation, human interaction, and a vehicle for connecting with others) (Gottshcall, 2012; Scott, 2012; Paul, 2012; Woodside, 2008).
Visual Storytelling is “using photography, illustration, video, (usually with a musical enhancement) to guide” the human brain along a plotline, and has an unlimited timeframe (Ron, 2017). There are seven key elements to effective visual storytelling: A listener/audience, an element of realism coupled with escapism, a focus on the dread of life, an element of the unknown, emotion, simplicity, and a three-part plot structure (Andrews, 2010; ProQuest, 2012; Zak, 2014; Stanton, 2014; Reagan, 2016; Jarvis, 2014; Petrick, 2014)
In the words of Sholmi Ron, from a marketing perspective, “Visual [short hand] Storytelling is a marketing strategy that communicates powerful ideas through a compelling story arc, with your customer at the heart of the story, and delivered through interactive and immersive visual media – in order to create profitable customer engagements" (Ron, 2017). This advertising strategy has four best practices: non-obvious logo placement, a comedic emotion, multiple emotional arcs, and a relevant message (Golan, 2017; Teixeira, 2015; Graves, 2017, Teixeira, 2017). These are important to understand because, in 2017, online consumers can be described as skeptical, conscious of content, individualistic, and drawn to authenticity (Teixeira, 2014).
To supplement my findings, I conducted primary research by analyzing the 2017 Super Bowl videos against a criteria created using the best practices previously identified (in Part 1 and Part 2). Through the data collection of the 66 videos, I uncovered the most popular plotline is "fall than rise," the most popular emotions are humor, inspiration, and empathy and people tend to have a preference towards videos that are more realistic and simplistic in nature.
In the end, I recommend that advertisers identify an authentic yet relevant message, while employing a comedic, inspirational, or empathic tone, and that they place their ads exclusively for their target market. Additionally, producers should use a fall then rise plotline (with multiple mini plot peaks and valleys), a "logo-pulsing" strategy, and a minimal amount of characters and settings to keep the audience's focus on the ad’s message.
ContributorsBosmeny, Mackenzie Lauren (Author) / Ostrom, Amy (Thesis director) / Montoya, Detra (Committee member) / Department of Marketing (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Microblogging services such as Twitter, Sina Weibo, and Tumblr have been emerging and deeply embedded into people's daily lives. Used by hundreds of millions of users to connect the people worldwide and share and access information in real-time, the microblogging service has also became the target of malicious attackers due to its massive user engagement and structural openness. Although existed, little is still known in the community about new types of vulnerabilities in current microblogging services which could be leveraged by the intelligence-evolving attackers, and more importantly, the corresponding defenses that could prevent both the users and the microblogging service providers from being attacked. This dissertation aims to uncover a number of challenging security and privacy issues in microblogging services and also propose corresponding defenses.
This dissertation makes fivefold contributions. The first part presents the social botnet, a group of collaborative social bots under the control of a single botmaster, demonstrate the effectiveness and advantages of exploiting a social botnet for spam distribution and digital-influence manipulation, and propose the corresponding countermeasures and evaluate their effectiveness. Inspired by Pagerank, the second part describes TrueTop, the first sybil-resilient system to find the top-K influential users in microblogging services with very accurate results and strong resilience to sybil attacks. TrueTop has been implemented to handle millions of nodes and 100 times more edges on commodity computers. The third and fourth part demonstrate that microblogging systems' structural openness and users' carelessness could disclose the later's sensitive information such as home city and age. LocInfer, a novel and lightweight system, is presented to uncover the majority of the users in any metropolitan area; the dissertation also proposes MAIF, a novel machine learning framework that leverages public content and interaction information in microblogging services to infer users' hidden ages. Finally, the dissertation proposes the first privacy-preserving social media publishing framework to let the microblogging service providers publish their data to any third-party without disclosing users' privacy and meanwhile meeting the data's commercial utilities. This dissertation sheds the light on the state-of-the-art security and privacy issues in the microblogging services.
This dissertation makes fivefold contributions. The first part presents the social botnet, a group of collaborative social bots under the control of a single botmaster, demonstrate the effectiveness and advantages of exploiting a social botnet for spam distribution and digital-influence manipulation, and propose the corresponding countermeasures and evaluate their effectiveness. Inspired by Pagerank, the second part describes TrueTop, the first sybil-resilient system to find the top-K influential users in microblogging services with very accurate results and strong resilience to sybil attacks. TrueTop has been implemented to handle millions of nodes and 100 times more edges on commodity computers. The third and fourth part demonstrate that microblogging systems' structural openness and users' carelessness could disclose the later's sensitive information such as home city and age. LocInfer, a novel and lightweight system, is presented to uncover the majority of the users in any metropolitan area; the dissertation also proposes MAIF, a novel machine learning framework that leverages public content and interaction information in microblogging services to infer users' hidden ages. Finally, the dissertation proposes the first privacy-preserving social media publishing framework to let the microblogging service providers publish their data to any third-party without disclosing users' privacy and meanwhile meeting the data's commercial utilities. This dissertation sheds the light on the state-of-the-art security and privacy issues in the microblogging services.
ContributorsZhang, Jinxue (Author) / Zhang, Yanchao (Thesis advisor) / Zhang, Junshan (Committee member) / Ying, Lei (Committee member) / Ahn, Gail-Joon (Committee member) / Arizona State University (Publisher)
Created2016
Description
Mobile devices are penetrating everyday life. According to a recent Cisco report [10], the number of mobile connected devices such as smartphones, tablets, laptops, eReaders, and Machine-to-Machine (M2M) modules will hit 11.6 billion by 2021, exceeding the world's projected population at that time (7.8 billion). The rapid development of mobile devices has brought a number of emerging security and privacy issues in mobile computing. This dissertation aims to address a number of challenging security and privacy issues in mobile computing.
This dissertation makes fivefold contributions. The first and second parts study the security and privacy issues in Device-to-Device communications. Specifically, the first part develops a novel scheme to enable a new way of trust relationship called spatiotemporal matching in a privacy-preserving and efficient fashion. To enhance the secure communication among mobile users, the second part proposes a game-theoretical framework to stimulate the cooperative shared secret key generation among mobile users. The third and fourth parts investigate the security and privacy issues in mobile crowdsourcing. In particular, the third part presents a secure and privacy-preserving mobile crowdsourcing system which strikes a good balance among object security, user privacy, and system efficiency. The fourth part demonstrates a differentially private distributed stream monitoring system via mobile crowdsourcing. Finally, the fifth part proposes VISIBLE, a novel video-assisted keystroke inference framework that allows an attacker to infer a tablet user's typed inputs on the touchscreen by recording and analyzing the video of the tablet backside during the user's input process. Besides, some potential countermeasures to this attack are also discussed. This dissertation sheds the light on the state-of-the-art security and privacy issues in mobile computing.
This dissertation makes fivefold contributions. The first and second parts study the security and privacy issues in Device-to-Device communications. Specifically, the first part develops a novel scheme to enable a new way of trust relationship called spatiotemporal matching in a privacy-preserving and efficient fashion. To enhance the secure communication among mobile users, the second part proposes a game-theoretical framework to stimulate the cooperative shared secret key generation among mobile users. The third and fourth parts investigate the security and privacy issues in mobile crowdsourcing. In particular, the third part presents a secure and privacy-preserving mobile crowdsourcing system which strikes a good balance among object security, user privacy, and system efficiency. The fourth part demonstrates a differentially private distributed stream monitoring system via mobile crowdsourcing. Finally, the fifth part proposes VISIBLE, a novel video-assisted keystroke inference framework that allows an attacker to infer a tablet user's typed inputs on the touchscreen by recording and analyzing the video of the tablet backside during the user's input process. Besides, some potential countermeasures to this attack are also discussed. This dissertation sheds the light on the state-of-the-art security and privacy issues in mobile computing.
ContributorsSun, Jingchao (Author) / Zhang, Yanchao (Thesis advisor) / Zhang, Junshan (Committee member) / Ying, Lei (Committee member) / Ahn, Gail-Joon (Committee member) / Arizona State University (Publisher)
Created2017
Description
Wireless sensor networks (WSN) and the communication and the security therein have been gaining further prominence in the tech-industry recently, with the emergence of the so called Internet of Things (IoT). The steps from acquiring data and making a reactive decision base on the acquired sensor measurements are complex and requires careful execution of several steps. In many of these steps there are still technological gaps to fill that are due to the fact that several primitives that are desirable in a sensor network environment are bolt on the networks as application layer functionalities, rather than built in them. For several important functionalities that are at the core of IoT architectures we have developed a solution that is analyzed and discussed in the following chapters.
The chain of steps from the acquisition of sensor samples until these samples reach a control center or the cloud where the data analytics are performed, starts with the acquisition of the sensor measurements at the correct time and, importantly, synchronously among all sensors deployed. This synchronization has to be network wide, including both the wired core network as well as the wireless edge devices. This thesis studies a decentralized and lightweight solution to synchronize and schedule IoT devices over wireless and wired networks adaptively, with very simple local signaling. Furthermore, measurement results have to be transported and aggregated over the same interface, requiring clever coordination among all nodes, as network resources are shared, keeping scalability and fail-safe operation in mind. Furthermore ensuring the integrity of measurements is a complicated task. On the one hand Cryptography can shield the network from outside attackers and therefore is the first step to take, but due to the volume of sensors must rely on an automated key distribution mechanism. On the other hand cryptography does not protect against exposed keys or inside attackers. One however can exploit statistical properties to detect and identify nodes that send false information and exclude these attacker nodes from the network to avoid data manipulation. Furthermore, if data is supplied by a third party, one can apply automated trust metric for each individual data source to define which data to accept and consider for mentioned statistical tests in the first place. Monitoring the cyber and physical activities of an IoT infrastructure in concert is another topic that is investigated in this thesis.
The chain of steps from the acquisition of sensor samples until these samples reach a control center or the cloud where the data analytics are performed, starts with the acquisition of the sensor measurements at the correct time and, importantly, synchronously among all sensors deployed. This synchronization has to be network wide, including both the wired core network as well as the wireless edge devices. This thesis studies a decentralized and lightweight solution to synchronize and schedule IoT devices over wireless and wired networks adaptively, with very simple local signaling. Furthermore, measurement results have to be transported and aggregated over the same interface, requiring clever coordination among all nodes, as network resources are shared, keeping scalability and fail-safe operation in mind. Furthermore ensuring the integrity of measurements is a complicated task. On the one hand Cryptography can shield the network from outside attackers and therefore is the first step to take, but due to the volume of sensors must rely on an automated key distribution mechanism. On the other hand cryptography does not protect against exposed keys or inside attackers. One however can exploit statistical properties to detect and identify nodes that send false information and exclude these attacker nodes from the network to avoid data manipulation. Furthermore, if data is supplied by a third party, one can apply automated trust metric for each individual data source to define which data to accept and consider for mentioned statistical tests in the first place. Monitoring the cyber and physical activities of an IoT infrastructure in concert is another topic that is investigated in this thesis.
ContributorsGentz, Reinhard Werner (Author) / Scaglione, Anna (Thesis advisor) / Zhang, Yanchao (Committee member) / Peisert, Sean (Committee member) / Ahn, Gail-Joon (Committee member) / Arizona State University (Publisher)
Created2017