Matching Items (3)
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- All Subjects: Electronic mail systems--Security measures.
- Creators: Yau, Stephen
- Creators: Zhong, Yunji
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
Due to the shortcomings of modern Mobile Device Management solutions, businesses
have begun to incorporate forensics to analyze their mobile devices and respond
to any incidents of malicious activity in order to protect their sensitive data. Current
forensic tools, however, can only look a static image of the device being examined,
making it difficult for a forensic analyst to produce conclusive results regarding the
integrity of any sensitive data on the device. This research thesis expands on the
use of forensics to secure data by implementing an agent on a mobile device that can
continually collect information regarding the state of the device. This information is
then sent to a separate server in the form of log files to be analyzed using a specialized
tool. The analysis tool is able to look at the data collected from the device over time
and perform specific calculations, according to the user's specifications, highlighting
any correlations or anomalies among the data which might be considered suspicious
to a forensic analyst. The contribution of this paper is both an in-depth explanation
on the implementation of an iOS application to be used to improve the mobile forensics
process as well as a proof-of-concept experiment showing how evidence collected
over time can be used to improve the accuracy of a forensic analysis.
have begun to incorporate forensics to analyze their mobile devices and respond
to any incidents of malicious activity in order to protect their sensitive data. Current
forensic tools, however, can only look a static image of the device being examined,
making it difficult for a forensic analyst to produce conclusive results regarding the
integrity of any sensitive data on the device. This research thesis expands on the
use of forensics to secure data by implementing an agent on a mobile device that can
continually collect information regarding the state of the device. This information is
then sent to a separate server in the form of log files to be analyzed using a specialized
tool. The analysis tool is able to look at the data collected from the device over time
and perform specific calculations, according to the user's specifications, highlighting
any correlations or anomalies among the data which might be considered suspicious
to a forensic analyst. The contribution of this paper is both an in-depth explanation
on the implementation of an iOS application to be used to improve the mobile forensics
process as well as a proof-of-concept experiment showing how evidence collected
over time can be used to improve the accuracy of a forensic analysis.
ContributorsWhitaker, Jeremy (Author) / Ahn, Gail-Joon (Thesis advisor) / Doupe, Adam (Committee member) / Yau, Stephen (Committee member) / Arizona State University (Publisher)
Created2015