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Description
Social networking services have emerged as an important platform for large-scale information sharing and communication. With the growing popularity of social media, spamming has become rampant in the platforms. Complex network interactions and evolving content present great challenges for social spammer detection. Different from some existing well-studied platforms, distinct characteristics

Social networking services have emerged as an important platform for large-scale information sharing and communication. With the growing popularity of social media, spamming has become rampant in the platforms. Complex network interactions and evolving content present great challenges for social spammer detection. Different from some existing well-studied platforms, distinct characteristics of newly emerged social media data present new challenges for social spammer detection. First, texts in social media are short and potentially linked with each other via user connections. Second, it is observed that abundant contextual information may play an important role in distinguishing social spammers and normal users. Third, not only the content information but also the social connections in social media evolve very fast. Fourth, it is easy to amass vast quantities of unlabeled data in social media, but would be costly to obtain labels, which are essential for many supervised algorithms. To tackle those challenges raise in social media data, I focused on developing effective and efficient machine learning algorithms for social spammer detection.

I provide a novel and systematic study of social spammer detection in the dissertation. By analyzing the properties of social network and content information, I propose a unified framework for social spammer detection by collectively using the two types of information in social media. Motivated by psychological findings in physical world, I investigate whether sentiment analysis can help spammer detection in online social media. In particular, I conduct an exploratory study to analyze the sentiment differences between spammers and normal users; and present a novel method to incorporate sentiment information into social spammer detection framework. Given the rapidly evolving nature, I propose a novel framework to efficiently reflect the effect of newly emerging social spammers. To tackle the problem of lack of labeling data in social media, I study how to incorporate network information into text content modeling, and design strategies to select the most representative and informative instances from social media for labeling. Motivated by publicly available label information from other media platforms, I propose to make use of knowledge learned from cross-media to help spammer detection on social media.
ContributorsHu, Xia, Ph.D (Author) / Liu, Huan (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Ye, Jieping (Committee member) / Faloutsos, Christos (Committee member) / Arizona State University (Publisher)
Created2015
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Description
The increasing usage of smart-phones and mobile devices in work environment and IT

industry has brought about unique set of challenges and opportunities. ARM architecture

in particular has evolved to a point where it supports implementations across wide spectrum

of performance points and ARM based tablets and smart-phones are in demand. The

enhancements to

The increasing usage of smart-phones and mobile devices in work environment and IT

industry has brought about unique set of challenges and opportunities. ARM architecture

in particular has evolved to a point where it supports implementations across wide spectrum

of performance points and ARM based tablets and smart-phones are in demand. The

enhancements to basic ARM RISC architecture allow ARM to have high performance,

small code size, low power consumption and small silicon area. Users want their devices to

perform many tasks such as read email, play games, and run other online applications and

organizations no longer desire to provision and maintain individual’s IT equipment. The

term BYOD (Bring Your Own Device) has come into being from demand of such a work

setup and is one of the motivation of this research work. It brings many opportunities such

as increased productivity and reduced costs and challenges such as secured data access,

data leakage and amount of control by the organization.

To provision such a framework we need to bridge the gap from both organizations side

and individuals point of view. Mobile device users face issue of application delivery on

multiple platforms. For instance having purchased many applications from one proprietary

application store, individuals may want to move them to a different platform/device but

currently this is not possible. Organizations face security issues in providing such a solution

as there are many potential threats from allowing BYOD work-style such as unauthorized

access to data, attacks from the devices within and outside the network.

ARM based Secure Mobile SDN framework will resolve these issues and enable employees

to consolidate both personal and business calls and mobile data access on a single device.

To address application delivery issue we are introducing KVM based virtualization that

will allow host OS to run multiple guest OS. To address the security problem we introduce

SDN environment where host would be running bridged network of guest OS using Open

vSwitch . This would allow a remote controller to monitor the state of guest OS for making

important control and traffic flow decisions based on the situation.
ContributorsChowdhary, Ankur (Author) / Huang, Dijiang (Thesis advisor) / Tong, Hanghang (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2015