ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- Creators: Ahn, Gail-Joon
Description
A firewall is a necessary component for network security and just like any regular equipment it requires maintenance. To keep up with changing cyber security trends and threats, firewall rules are modified frequently. Over time such modifications increase the complexity, size and verbosity of firewall rules. As the rule set grows in size, adding and modifying rule becomes a tedious task. This discourages network administrators to review the work done by previous administrators before and after applying any changes. As a result the quality and efficiency of the firewall goes down.
Modification and addition of rules without knowledge of previous rules creates anomalies like shadowing and rule redundancy. Anomalous rule sets not only limit the efficiency of the firewall but in some cases create a hole in the perimeter security. Detection of anomalies has been studied for a long time and some well established procedures have been implemented and tested. But they all have a common problem of visualizing the results. When it comes to visualization of firewall anomalies, the results do not fit in traditional matrix, tree or sunburst representations.
This research targets the anomaly detection and visualization problem. It analyzes and represents firewall rule anomalies in innovative ways such as hive plots and dynamic slices. Such graphical representations of rule anomalies are useful in understanding the state of a firewall. It also helps network administrators in finding and fixing the anomalous rules.
Modification and addition of rules without knowledge of previous rules creates anomalies like shadowing and rule redundancy. Anomalous rule sets not only limit the efficiency of the firewall but in some cases create a hole in the perimeter security. Detection of anomalies has been studied for a long time and some well established procedures have been implemented and tested. But they all have a common problem of visualizing the results. When it comes to visualization of firewall anomalies, the results do not fit in traditional matrix, tree or sunburst representations.
This research targets the anomaly detection and visualization problem. It analyzes and represents firewall rule anomalies in innovative ways such as hive plots and dynamic slices. Such graphical representations of rule anomalies are useful in understanding the state of a firewall. It also helps network administrators in finding and fixing the anomalous rules.
ContributorsKhatkar, Pankaj Kumar (Author) / Huang, Dijiang (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Syrotiuk, Violet R. (Committee member) / Arizona State University (Publisher)
Created2014
Description
This dissertation studies three classes of combinatorial arrays with practical applications in testing, measurement, and security. Covering arrays are widely studied in software and hardware testing to indicate the presence of faulty interactions. Locating arrays extend covering arrays to achieve identification of the interactions causing a fault by requiring additional conditions on how interactions are covered in rows. This dissertation introduces a new class, the anonymizing arrays, to guarantee a degree of anonymity by bounding the probability a particular row is identified by the interaction presented. Similarities among these arrays lead to common algorithmic techniques for their construction which this dissertation explores. Differences arising from their application domains lead to the unique features of each class, requiring tailoring the techniques to the specifics of each problem.
One contribution of this work is a conditional expectation algorithm to build covering arrays via an intermediate combinatorial object. Conditional expectation efficiently finds intermediate-sized arrays that are particularly useful as ingredients for additional recursive algorithms. A cut-and-paste method creates large arrays from small ingredients. Performing transformations on the copies makes further improvements by reducing redundancy in the composed arrays and leads to fewer rows.
This work contains the first algorithm for constructing locating arrays for general values of $d$ and $t$. A randomized computational search algorithmic framework verifies if a candidate array is $(\bar{d},t)$-locating by partitioning the search space and performs random resampling if a candidate fails. Algorithmic parameters determine which columns to resample and when to add additional rows to the candidate array. Additionally, analysis is conducted on the performance of the algorithmic parameters to provide guidance on how to tune parameters to prioritize speed, accuracy, or a combination of both.
This work proposes anonymizing arrays as a class related to covering arrays with a higher coverage requirement and constraints. The algorithms for covering and locating arrays are tailored to anonymizing array construction. An additional property, homogeneity, is introduced to meet the needs of attribute-based authorization. Two metrics, local and global homogeneity, are designed to compare anonymizing arrays with the same parameters. Finally, a post-optimization approach reduces the homogeneity of an anonymizing array.
One contribution of this work is a conditional expectation algorithm to build covering arrays via an intermediate combinatorial object. Conditional expectation efficiently finds intermediate-sized arrays that are particularly useful as ingredients for additional recursive algorithms. A cut-and-paste method creates large arrays from small ingredients. Performing transformations on the copies makes further improvements by reducing redundancy in the composed arrays and leads to fewer rows.
This work contains the first algorithm for constructing locating arrays for general values of $d$ and $t$. A randomized computational search algorithmic framework verifies if a candidate array is $(\bar{d},t)$-locating by partitioning the search space and performs random resampling if a candidate fails. Algorithmic parameters determine which columns to resample and when to add additional rows to the candidate array. Additionally, analysis is conducted on the performance of the algorithmic parameters to provide guidance on how to tune parameters to prioritize speed, accuracy, or a combination of both.
This work proposes anonymizing arrays as a class related to covering arrays with a higher coverage requirement and constraints. The algorithms for covering and locating arrays are tailored to anonymizing array construction. An additional property, homogeneity, is introduced to meet the needs of attribute-based authorization. Two metrics, local and global homogeneity, are designed to compare anonymizing arrays with the same parameters. Finally, a post-optimization approach reduces the homogeneity of an anonymizing array.
ContributorsLanus, Erin (Author) / Colbourn, Charles J (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Montgomery, Douglas C. (Committee member) / Syrotiuk, Violet R. (Committee member) / Arizona State University (Publisher)
Created2019