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: Doupe, Adam
In this dissertation, a formalism for flow rule conflicts in SDN environments is introduced. This formalism is realized in Brew, a security policy analysis framework implemented on an OpenDaylight SDN controller. Brew has comprehensive conflict detection and resolution modules to ensure that no two flow rules in a distributed SDN-based cloud environment have conflicts at any layer; thereby assuring consistent conflict-free security policy implementation and preventing information leakage. Techniques for global prioritization of flow rules in a decentralized environment are presented, using which all SDN flow rule conflicts are recognized and classified. Strategies for unassisted resolution of these conflicts are also detailed. Alternately, if administrator input is desired to resolve conflicts, a novel visualization scheme is implemented to help the administrators view the conflicts in an aesthetic manner. The correctness, feasibility and scalability of the Brew proof-of-concept prototype is demonstrated. Flow rule conflict avoidance using a buddy address space management technique is studied as an alternate to conflict detection and resolution in highly dynamic cloud systems attempting to implement an SDN-based Moving Target Defense (MTD) countermeasures.
To overcome above issue, I explain the necessity of determining how likely a reported indicator is malicious given the evidence and prioritizing it based on such determination. Confidence Score Measurement system (CSM) introduces the concept of confidence score, where it assigns a score of being malicious to a threat indicator based on the evaluation of different threat intelligence systems. An indicator propagates maliciousness to adjacent indicators based on relationship determined from behavior of an indicator. The propagation algorithm derives final confidence to determine overall maliciousness of the threat indicator. CSM can prioritize the indicators based on confidence score; however, an analyst may not be interested in the entire result set, so CSM narrows down the results based on the analyst-driven input. To this end, CSM introduces the concept of relevance score, where it combines the confidence score with analyst-driven search by applying full-text search techniques. It prioritizes the results based on relevance score to provide meaningful results to the analyst. The analysis shows the propagation algorithm of CSM linearly scales with larger datasets and achieves 92% accuracy in determining threat indicators. The evaluation of the result demonstrates the effectiveness and practicality of the approach.
In this thesis, I incorporate live migration of Docker container using CRIU (checkpoint restore) for moving target defense. There are 460K Dockerized applications, a 3100% growth over 2 years[1]. Over 4 billion containers have been pulled so far from Docker hub. Docker is supported by a large and fast growing community of contributors and users. As an example, there are 125K Docker Meetup members worldwide. As we see industry adapting to Docker rapidly, a moving target defense solution involving containers is beneficial for being robust and fast. A proof of concept implementation is included for studying performance attributes of Docker migration.
The detection of attack is using a scenario involving definitions of normal events on servers. By defining system activities, and extracting syslog in centralized server, attack can be detected via extracting abnormal activates and this detection can be a trigger for the Docker migration.
it is possible to reach almost anyone around the globe. However, every mobile telephone
call placed implicitly leaks the user's location to the telephony service provider (TSP).
This privacy leakage is due to the fundamental nature of mobile telephony calls that
must connect to a local base station to receive service and place calls. Thus, the TSP
can track the physical location of the user for every call that they place. While the
The Internet is similar in this regard, privacy-preserving technologies such as Tor allow
users to connect to websites anonymously (without revealing to their ISP the site
that they are visiting). In this thesis, the scheme presented, called shadow calling,
to allow geolocation anonymous calling from legacy mobile devices. In this way,
the call is placed from the same number, however, the TSP will not know the user's
physical location. The scheme does not require any change on the network side and
can be used on current mobile networks. The scheme implemented is for the GSM
(commonly referred to as 2G) network, as it is the most widely used mode of mobile
telephony communication. The feasibility of our scheme is demonstrated through the
prototype. Shadow calling, which renders the users geolocation anonymous, will be
beneficial for users such as journalists, human rights activists in hostile nations, or
other privacy-demanding users.
and developers use automated vulnerability analysis tools, also known as
scanners, to automatically find vulnerabilities in their web applications during development.
Scanners have traditionally fallen into two types of approaches: black-box
and white-box. In the black-box approaches, the scanner does not have access to the
source code of the web application whereas a white-box approach has access to the
source code. Today’s state-of-the-art black-box vulnerability scanners employ various
methods to fuzz and detect vulnerabilities in a web application. However, these
scanners attempt to fuzz the web application with a number of known payloads and
to try to trigger a vulnerability. This technique is simple but does not understand
the web application that it is testing. This thesis, presents a new approach to vulnerability
analysis. The vulnerability analysis module presented uses a novel approach
of Inductive Reverse Engineering (IRE) to understand and model the web application.
IRE first attempts to understand the behavior of the web application by giving
certain number of input/output pairs to the web application. Then, the IRE module
hypothesizes a set of programs (in a limited language specific to web applications,
called AWL) that satisfy the input/output pairs. These hypotheses takes the form of
a directed acyclic graph (DAG). AWL vulnerability analysis module can then attempt
to detect vulnerabilities in this DAG. Further, it generates the payload based on the
DAG, and therefore this payload will be a precise payload to trigger the potential vulnerability
(based on our understanding of the program). It then tests this potential
vulnerability using the generated payload on the actual web application, and creates
a verification procedure to see if the potential vulnerability is actually vulnerable,
based on the web application’s response.
To overcome these limitations, we propose iGen, a novel approach to fully automate the process of IOC generation and analysis. Proposed approach is based on the idea that our model can understand English texts like human beings, and extract the IOCs from the different data sources intelligently. Identification of the IOCs is done on the basis of the syntax and semantics of the sentence as well as context words (e.g., ``attacked'', ``suspicious'') present in the sentence which helps the approach work on any kind of data source. Our proposed technique, first removes the words with no contextual meaning like stop words and punctuations etc. Then using the rest of the words in the sentence and output label (IOC or non-IOC sentence), our model intelligently learn to classify sentences into IOC and non-IOC sentences. Once IOC sentences are identified using this learned Convolutional Neural Network (CNN) based approach, next step is to identify the IOC tokens (like domains, IP, URL) in the sentences. This CNN based classification model helps in removing false positives (like IPs which are not malicious). Afterwards, IOCs extracted from different data sources are correlated to find the links between thousands of apparently unrelated attack instances, particularly infrastructures shared between them. Our approach fully automates the process of IOC generation from gathering data from different sources to creating rules (e.g. OpenIOC, snort rules, STIX rules) for deployment on
the security infrastructure.
iGen has collected around 400K IOCs till now with a precision of 95\%, better than any state-of-art method.
The significant loss to organizations due to the failure to detect and mitigate the insider threat has resulted in an increased interest in insider threat detection. The well-studied effective techniques proposed for defending against attacks by outsiders have not been proven successful against insider attacks. Although a number of security policies and models to deal with the insider threat have been developed, the approach taken by most organizations is the use of audit logs after the attack has taken place. Such approaches are inspired by academic research proposals to address the problem by tracking activities of the insider in the system. Although tracking and logging are important, it is argued that they are not sufficient. Thus, the necessity to predict the potential damage of an insider is considered to help build a stronger evaluation and mitigation strategy for the insider attack. In this thesis, the question that seeks to be answered is the following: `Considering the relationships that exist between the insiders and their role, their access to the resources and the resource set, what is the potential damage that an insider can cause?'
A general system model is introduced that can capture general insider attacks including those documented by Computer Emergency Response Team (CERT) for the Software Engineering Institute (SEI). Further, initial formulations of the damage potential for leakage and availability in the model is introduced. The model usefulness is shown by expressing 14 of actual attacks in the model and show how for each case the attack could have been mitigated.