Matching Items (2)
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- All Subjects: Electronic mail systems--Security measures.
- All Subjects: Intrusion Prevention Systems
- Genre: Academic theses
- Genre: Masters Thesis
- Creators: Yau, Stephen
- Creators: Wright, Jeremy
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
The advancement of cloud technology has impacted society positively in a number of ways, but it has also led to an increase in threats that target private information available on cloud systems. Intrusion prevention systems play a crucial role in protecting cloud systems from such threats. In this thesis, an intrusion prevention approach todetect and prevent such threats in real-time is proposed. This approach is designed for network-based intrusion prevention systems and leverages the power of supervised machine learning with Extreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM) algorithms, to analyze the flow of each packet that is sent to a cloud system through the network. The innovations of this thesis include developing a custom LSTM architecture, using this architecture to train a LSTM model to identify attacks and using TCP reset functionality to prevent attacks for cloud systems. The aim of this thesis is to provide a framework for an Intrusion Prevention System. Based on simulations and experimental results with the NF-UQ-NIDS-v2 dataset, the proposed system is accurate, fast, scalable and has a low rate of false positives, making it suitable for real world applications.
ContributorsGianchandani, Siddharth (Author) / Yau, Stephen (Thesis advisor) / Zhao, Ming (Committee member) / Lee, Kookjin (Committee member) / Arizona State University (Publisher)
Created2023