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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,

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%.
ContributorsWright, Jeremy (Author) / Syrotiuk, Violet (Thesis advisor) / Davulcu, Hasan (Committee member) / Yau, Stephen (Committee member) / Arizona State University (Publisher)
Created2014
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Description
Reasoning about the activities of cyber threat actors is critical to defend against cyber

attacks. However, this task is difficult for a variety of reasons. In simple terms, it is difficult

to determine who the attacker is, what the desired goals are of the attacker, and how they will

carry out their attacks.

Reasoning about the activities of cyber threat actors is critical to defend against cyber

attacks. However, this task is difficult for a variety of reasons. In simple terms, it is difficult

to determine who the attacker is, what the desired goals are of the attacker, and how they will

carry out their attacks. These three questions essentially entail understanding the attacker’s

use of deception, the capabilities available, and the intent of launching the attack. These

three issues are highly inter-related. If an adversary can hide their intent, they can better

deceive a defender. If an adversary’s capabilities are not well understood, then determining

what their goals are becomes difficult as the defender is uncertain if they have the necessary

tools to accomplish them. However, the understanding of these aspects are also mutually

supportive. If we have a clear picture of capabilities, intent can better be deciphered. If we

understand intent and capabilities, a defender may be able to see through deception schemes.

In this dissertation, I present three pieces of work to tackle these questions to obtain

a better understanding of cyber threats. First, we introduce a new reasoning framework

to address deception. We evaluate the framework by building a dataset from DEFCON

capture-the-flag exercise to identify the person or group responsible for a cyber attack.

We demonstrate that the framework not only handles cases of deception but also provides

transparent decision making in identifying the threat actor. The second task uses a cognitive

learning model to determine the intent – goals of the threat actor on the target system.

The third task looks at understanding the capabilities of threat actors to target systems by

identifying at-risk systems from hacker discussions on darkweb websites. To achieve this

task we gather discussions from more than 300 darkweb websites relating to malicious

hacking.
ContributorsNunes, Eric (Author) / Shakarian, Paulo (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Baral, Chitta (Committee member) / Cooke, Nancy J. (Committee member) / Arizona State University (Publisher)
Created2018
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Description
Malware forensics is a time-consuming process that involves a significant amount of data collection. To ease the load on security analysts, many attempts have been made to automate the intelligence gathering process and provide a centralized search interface. Certain of these solutions map existing relations between threats and can discover

Malware forensics is a time-consuming process that involves a significant amount of data collection. To ease the load on security analysts, many attempts have been made to automate the intelligence gathering process and provide a centralized search interface. Certain of these solutions map existing relations between threats and can discover new intelligence by identifying correlations in the data. However, such systems generally treat each unique malware sample as its own distinct threat. This fails to model the real malware landscape, in which so many ``new" samples are actually variants of samples that have already been discovered. Were there some way to reliably determine whether two malware samples belong to the same family, intelligence for one sample could be applied to any sample in the family, greatly reducing the complexity of intelligence synthesis. Clustering is a common big data approach for grouping data samples which have common features, and has been applied in several recent papers for identifying related malware. It therefore has the potential to be used as described to simplify the intelligence synthesis process. However, existing threat intelligence systems do not use malware clustering. In this paper, we attempt to design a highly accurate malware clustering system, with the ultimate goal of integrating it into a threat intelligence platform. Toward this end, we explore the many considerations of designing such a system: how to extract features to compare malware, and how to use these features for accurate clustering. We then create an experimental clustering system, and evaluate its effectiveness using two different clustering algorithms.
ContributorsSmith, Joshua Michael (Author) / Ahn, Gail-Joon (Thesis director) / Zhao, Ziming (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
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Description
This thesis project focused on malicious hacking community activities accessible through the I2P protocol. We visited 315 distinct I2P sites to identify those with malicious hacking content. We also wrote software to scrape and parse data from relevant I2P sites. The data was integrated into the CySIS databases for further

This thesis project focused on malicious hacking community activities accessible through the I2P protocol. We visited 315 distinct I2P sites to identify those with malicious hacking content. We also wrote software to scrape and parse data from relevant I2P sites. The data was integrated into the CySIS databases for further analysis to contribute to the larger CySIS Lab Darkweb Cyber Threat Intelligence Mining research. We found that the I2P cryptonet was slow and had only a small amount of malicious hacking community activity. However, we also found evidence of a growing perception that Tor anonymity could be compromised. This work will contribute to understanding the malicious hacker community as some Tor users, seeking assured anonymity, transition to I2P.
ContributorsHutchins, James Keith (Author) / Shakarian, Paulo (Thesis director) / Ahn, Gail-Joon (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
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Description
E-Mail header injection vulnerability is a class of vulnerability that can occur in web applications that use user input to construct e-mail messages. E-Mail injection is possible when the mailing script fails to check for the presence of e-mail headers in user input (either form fields or URL parameters). The

E-Mail header injection vulnerability is a class of vulnerability that can occur in web applications that use user input to construct e-mail messages. E-Mail injection is possible when the mailing script fails to check for the presence of e-mail headers in user input (either form fields or URL parameters). The vulnerability exists in the reference implementation of the built-in “mail” functionality in popular languages like PHP, Java, Python, and Ruby. With the proper injection string, this vulnerability can be exploited to inject additional headers and/or modify existing headers in an e-mail message, allowing an attacker to completely alter the content of the e-mail.

This thesis develops a scalable mechanism to automatically detect E-Mail Header Injection vulnerability and uses this mechanism to quantify the prevalence of E- Mail Header Injection vulnerabilities on the Internet. Using a black-box testing approach, the system crawled 21,675,680 URLs to find URLs which contained form fields. 6,794,917 such forms were found by the system, of which 1,132,157 forms contained e-mail fields. The system used this data feed to discern the forms that could be fuzzed with malicious payloads. Amongst the 934,016 forms tested, 52,724 forms were found to be injectable with more malicious payloads. The system tested 46,156 of these and was able to find 496 vulnerable URLs across 222 domains, which proves that the threat is widespread and deserves future research attention.
ContributorsChandramouli, Sai Prashanth (Author) / Doupe, Adam (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Zhao, Ziming (Committee member) / Arizona State University (Publisher)
Created2016
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Description
The volume and frequency of cyber attacks have exploded in recent years. Organizations subscribe to multiple threat intelligence feeds to increase their knowledge base and better equip their security teams with the latest information in threat intelligence domain. Though such subscriptions add intelligence and can help in taking more informed

The volume and frequency of cyber attacks have exploded in recent years. Organizations subscribe to multiple threat intelligence feeds to increase their knowledge base and better equip their security teams with the latest information in threat intelligence domain. Though such subscriptions add intelligence and can help in taking more informed decisions, organizations have to put considerable efforts in facilitating and analyzing a large number of threat indicators. This problem worsens further, due to a large number of false positives and irrelevant events detected as threat indicators by existing threat feed sources. It is often neither practical nor cost-effective to analyze every single alert considering the staggering volume of indicators. The very reason motivates to solve the overcrowded threat indicators problem by prioritizing and filtering them.

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.
ContributorsModi, Ajay (Author) / Ahn, Gail-Joon (Thesis advisor) / Zhao, Ziming (Committee member) / Doupe, Adam (Committee member) / Arizona State University (Publisher)
Created2017