Matching Items (10)
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- All Subjects: Cybersecurity
- Genre: Academic theses
- Genre: Masters Thesis
- Creators: Doupe, Adam
- Member of: Theses and Dissertations
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
Data breaches have been on a rise and financial sector is among the top targeted. It can take a few months and upto a few years to identify the occurrence of a data breach. A major motivation behind data breaches is financial gain, hence most of the data ends up being on sale on the darkweb websites. It is important to identify sale of such stolen information on a timely and relevant manner. In this research, we present a system for timely identification of sale of stolen data on darkweb websites. We frame identifying sale of stolen data as a multi-label classification problem and leverage several machine learning approaches based on the thread content (textual) and social network analysis of the user communication seen on darkweb websites. The system generates alerts about trends based on popularity amongst the users of such websites. We evaluate our system using the K-fold cross validation as well as manual evaluation of blind (unseen) data. The method of combining social network and textual features outperforms baseline method i.e only using textual features, by 15 to 20 % improved precision. The alerts provide a good insight and we illustrate our findings by cases studies of the results.
ContributorsDharaiya, Krishna Tushar (Author) / Shakarian, Paulo (Thesis advisor) / Doupe, Adam (Committee member) / Shoshitaishvili, Yan (Committee member) / Arizona State University (Publisher)
Created2018
Description
With the rise of the Internet of Things, embedded systems have become an integral part of life and can be found almost anywhere. Their prevalence and increased interconnectivity has made them a prime target for malicious attacks. Today, the vast majority of embedded devices are powered by ARM processors. To protect their processors from attacks, ARM introduced a hardware security extension known as TrustZone. It provides an isolated execution environment within the embedded device in which to deploy various memory integrity and malware detection tools.
Even though Secure World can monitor the Normal World, attackers can attempt to bypass the security measures to retain control of a compromised system. CacheKit is a new type of rootkit that exploits such a vulnerability in the ARM architecture to hide in Normal World cache from memory introspection tools running in Secure World by exploiting cache locking mechanisms. If left unchecked, ARM processors that provide hardware assisted cache locking for performance and time-critical applications in real-time and embedded systems would be completely vulnerable to this undetectable and untraceable attack. Therefore, a new approach is needed to ensure the correct use of such mechanisms and prevent malicious code from being hidden in the cache.
CacheLight is a lightweight approach that leverages the TrustZone and Virtualization extensions of the ARM architecture to allow the system to continue to securely provide these hardware facilities to users while preventing attackers from exploiting them. CacheLight restricts the ability to lock the cache to the Secure World of the processor such that the Normal World can still request certain memory to be locked into the cache by the secure operating system (OS) through a Secure Monitor Call (SMC). This grants the secure OS the power to verify and validate the information that will be locked in the requested cache way thereby ensuring that any data that remains in the cache will not be inconsistent with what exists in main memory for inspection. Malicious attempts to hide data can be prevented and recovered for analysis while legitimate requests can still generate valid entries in the cache.
Even though Secure World can monitor the Normal World, attackers can attempt to bypass the security measures to retain control of a compromised system. CacheKit is a new type of rootkit that exploits such a vulnerability in the ARM architecture to hide in Normal World cache from memory introspection tools running in Secure World by exploiting cache locking mechanisms. If left unchecked, ARM processors that provide hardware assisted cache locking for performance and time-critical applications in real-time and embedded systems would be completely vulnerable to this undetectable and untraceable attack. Therefore, a new approach is needed to ensure the correct use of such mechanisms and prevent malicious code from being hidden in the cache.
CacheLight is a lightweight approach that leverages the TrustZone and Virtualization extensions of the ARM architecture to allow the system to continue to securely provide these hardware facilities to users while preventing attackers from exploiting them. CacheLight restricts the ability to lock the cache to the Secure World of the processor such that the Normal World can still request certain memory to be locked into the cache by the secure operating system (OS) through a Secure Monitor Call (SMC). This grants the secure OS the power to verify and validate the information that will be locked in the requested cache way thereby ensuring that any data that remains in the cache will not be inconsistent with what exists in main memory for inspection. Malicious attempts to hide data can be prevented and recovered for analysis while legitimate requests can still generate valid entries in the cache.
ContributorsGutierrez, Mauricio (Author) / Zhao, Ziming (Thesis advisor) / Doupe, Adam (Committee member) / Shoshitaishvili, Yan (Committee member) / Arizona State University (Publisher)
Created2018
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 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.
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
Description
The omnipresent data, growing number of network devices, and evolving attack techniques have been challenging organizations’ security defenses over the past decade. With humongous volumes of logs generated by those network devices, looking for patterns of malicious activities and identifying them in time is growing beyond the capabilities of their defense systems. Deep Learning, a subset of Machine Learning (ML) and Artificial Intelligence (AI), fills in this gapwith its ability to learn from huge amounts of data, and improve its performance as the data it learns from increases. In this dissertation, I bring forward security issues pertaining to two top threats that most organizations fear, Advanced Persistent Threat (APT), and Distributed Denial of Service (DDoS), along with deep learning models built towards addressing those
security issues. First, I present a deep learning model, APT Detection, capable of detecting anomalous activities in a system. Evaluation of this model demonstrates how it can contribute to early detection of an APT attack with an Area Under the Curve (AUC) of up to 91% on a Receiver Operating Characteristic (ROC) curve. Second, I present DAPT2020, a first of its kind dataset capturing an APT attack exploiting web and system vulnerabilities in an emulated organization’s production network. Evaluation of the dataset using well known machine learning models demonstrates the need for better deep learning models to detect APT attacks. I then present DAPT2021, a semi-synthetic dataset capturing an APT attackexploiting human vulnerabilities, alongside 2 less skilled attacks. By emulating the normal behavior of the employees in a set target organization, DAPT2021 has been created to enable researchers study the causations and correlations among the captured data, a much-needed information to detect an underlying threat early. Finally, I present a distributed defense
framework, SmartDefense, that can detect and mitigate over 90% of DDoS traffic at the source and over 97.5% of the remaining DDoS traffic at the Internet Service Provider’s (ISP’s) edge network. Evaluation of this work shows how by using attributes sent by customer edge network, SmartDefense can further help ISPs prevent up to 51.95% of the DDoS traffic
from going to the destination.
ContributorsMyneni, Sowmya (Author) / Xue, Guoliang (Thesis advisor) / Doupe, Adam (Committee member) / Li, Baoxin (Committee member) / Baral, Chitta (Committee member) / Arizona State University (Publisher)
Created2022
Description
As computers and the Internet have become integral to daily life, the potential gains from exploiting these resources have increased significantly. The global landscape is now rife with highly skilled wrongdoers seeking to steal from and disrupt society. In order to safeguard society and its infrastructure, a comprehensive approach to research is essential. This work aims to enhance security from three unique viewpoints by expanding the resources available to educators, users, and analysts. For educators, a capture the flag as-a-service was developed to support cybersecurity education. This service minimizes the skill and time needed to establish the infrastructure for hands-on hacking experiences for cybersecurity students. For users, a tool called CloakX was created to improve online anonymity. CloakX prevents the identification of browser extensions by employing both static and dynamic rewriting techniques, thwarting contemporary methods of detecting installed extensions and thus protecting user identity. Lastly, for cybersecurity analysts, a tool named Witcher was developed to automate the process of crawling and exercising web applications while identifying web injection vulnerabilities. Overall, these contributions serve to strengthen security education, bolster privacy protection for users, and facilitate vulnerability discovery for cybersecurity analysts.
ContributorsTrickel, Erik (Author) / Doupe, Adam (Thesis advisor) / Shoshitaishvili, Yan (Thesis advisor) / Bao, Tiffany (Committee member) / Wang, Ruoyu (Committee member) / Arizona State University (Publisher)
Created2023
Description
Malicious hackers utilize the World Wide Web to share knowledge. Previous work has demonstrated that information mined from online hacking communities can be used as precursors to cyber-attacks. In a threatening scenario, where security alert systems are facing high false positive rates, understanding the people behind cyber incidents can help reduce the risk of attacks. However, the rapidly evolving nature of those communities leads to limitations still largely unexplored, such as: who are the skilled and influential individuals forming those groups, how they self-organize along the lines of technical expertise, how ideas propagate within them, and which internal patterns can signal imminent cyber offensives? In this dissertation, I have studied four key parts of this complex problem set. Initially, I leverage content, social network, and seniority analysis to mine key-hackers on darkweb forums, identifying skilled and influential individuals who are likely to succeed in their cybercriminal goals. Next, as hackers often use Web platforms to advertise and recruit collaborators, I analyze how social influence contributes to user engagement online. On social media, two time constraints are proposed to extend standard influence measures, which increases their correlation with adoption probability and consequently improves hashtag adoption prediction. On darkweb forums, the prediction of where and when hackers will post a message in the near future is accomplished by analyzing their recurrent interactions with other hackers. After that, I demonstrate how vendors of malware and malicious exploits organically form hidden organizations on darkweb marketplaces, obtaining significant consistency across the vendors’ communities extracted using the similarity of their products in different networks. Finally, I predict imminent cyber-attacks correlating malicious hacking activity on darkweb forums with real-world cyber incidents, evidencing how social indicators are crucial for the performance of the proposed model. This research is a hybrid of social network analysis (SNA), machine learning (ML), evolutionary computation (EC), and temporal logic (TL), presenting expressive contributions to empower cyber defense.
ContributorsSantana Marin, Ericsson (Author) / Shakarian, Paulo (Thesis advisor) / Doupe, Adam (Committee member) / Liu, Huan (Committee member) / Ferrara, Emilio (Committee member) / Arizona State University (Publisher)
Created2020
Description
Recent advances in techniques allow the extraction of Cyber Threat Information (CTI) from online content, such as social media, blog articles, and posts in discussion forums. Most research work focuses on social media and blog posts since their content is often contributed by cybersecurity experts and is usually of cleaner formats. While posts in online forums are noisier and less structured, online forums attract more users than other sources and contain much valuable information that may help predict cyber threats. Therefore, effectively extracting CTI from online forum posts is an important task in today's data-driven cybersecurity defenses. Many Natural Language Processing (NLP) techniques are applied to the cybersecurity domains to extract the useful information, however, there is still space to improve. In this dissertation, a new Named Entity Recognition framework for cybersecurity domains and thread structure construction methods for unstructured forums are proposed to support the extraction of CTI. Then, extend them to filter the posts in the forums to eliminate non cybersecurity related topics with Cyber Attack Relevance Scale (CARS), extract the cybersecurity knowledgeable users to enhance more information for enhancing cybersecurity, and extract trending topic phrases related to cyber attacks in the hackers forums to find the clues for potential future attacks to predict them.
ContributorsKashihara, Kazuaki (Author) / Baral, Chitta (Thesis advisor) / Doupe, Adam (Committee member) / Blanco, Eduardo (Committee member) / Wang, Ruoyu (Committee member) / Arizona State University (Publisher)
Created2022
Description
The lack of fungibility in Bitcoin has forced its userbase to seek out tools that can heighten their anonymity. Third-party Bitcoin mixers utilize obfuscation techniques to protect participants from blockchain analysis. In recent years, various centralized and decentralized Bitcoin mixing implementations have been proposed in academic literature. Although these methods depict a threat-free environment for users to preserve their anonymity, public Bitcoin mixers continue to be associated with theft and poor implementation.
This research explores the public Bitcoin mixer ecosystem to identify if today's mixing services have adopted academically proposed solutions. This is done through real-world interactions with publicly available mixers to analyze both implementation and resistance to common threats in the mixing landscape. First, proposed decentralized and centralized mixing protocols found in literature are outlined. Then, data is presented from 19 publicly announced mixing services available on the deep web and clearnet. The services are categorized based on popularity with the Bitcoin community and experiments are conducted on five public mixing services: ChipMixer, MixTum, Bitcoin Mixer, CryptoMixer, and Sudoku Wallet.
The results of the experiments highlight a clear gap between public and proposed Bitcoin mixers in both implementation and security. Today's mixing services focus on presenting users with a false sense of control to gain their trust rather then employing secure mixing techniques. As a result, the five selected services lack implementation of academically proposed techniques and display poor resistance to common mixer-related threats.
This research explores the public Bitcoin mixer ecosystem to identify if today's mixing services have adopted academically proposed solutions. This is done through real-world interactions with publicly available mixers to analyze both implementation and resistance to common threats in the mixing landscape. First, proposed decentralized and centralized mixing protocols found in literature are outlined. Then, data is presented from 19 publicly announced mixing services available on the deep web and clearnet. The services are categorized based on popularity with the Bitcoin community and experiments are conducted on five public mixing services: ChipMixer, MixTum, Bitcoin Mixer, CryptoMixer, and Sudoku Wallet.
The results of the experiments highlight a clear gap between public and proposed Bitcoin mixers in both implementation and security. Today's mixing services focus on presenting users with a false sense of control to gain their trust rather then employing secure mixing techniques. As a result, the five selected services lack implementation of academically proposed techniques and display poor resistance to common mixer-related threats.
ContributorsPakki, Jaswant (Author) / Doupe, Adam (Thesis advisor) / Shoshitaishvili, Yan (Committee member) / Wang, Ruoyu (Committee member) / Arizona State University (Publisher)
Created2020
Description
Human civilization within the last two decades has largely transformed into an online one, with many of its associated activities taking place on computers and complex networked systems -- their analog and real-world equivalents having been rendered obsolete.These activities run the gamut from the ordinary and mundane, like ordering food, to complex and large-scale, such as those involving critical infrastructure or global trade and communications.
Unfortunately, the activities of human civilization also involve criminal, adversarial, and malicious ones with the result that they also now have their digital equivalents.
Ransomware, malware, and targeted cyberattacks are a fact of life today and are instigated not only by organized criminal gangs, but adversarial nation-states and organizations as well.
Needless to say, such actions result in disastrous and harmful real-world consequences. As the complexity and variety of software has evolved, so too has the ingenuity of attacks that exploit them; for example modern cyberattacks typically involve sequential exploitation of multiple software vulnerabilities.Compared to a decade ago, modern software stacks on personal computers, laptops, servers, mobile phones, and even Internet of Things (IoT) devices involve a dizzying array of interdependent programs and software libraries, with each of these components presenting attractive attack-surfaces for adversarial actors.
However, the responses to this still rely on paradigms that can neither react quickly enough nor scale to increasingly dynamic, ever-changing, and complex software environments.
Better approaches are therefore needed, that can assess system readiness and vulnerabilities, identify potential attack vectors and strategies (including ways to counter them), and proactively detect vulnerabilities in complex software before they can be exploited. In this dissertation, I first present a mathematical model and associated algorithms to identify attacker strategies for sequential cyberattacks based on attacker state, attributes and publicly-available vulnerability information.Second, I extend the model and design algorithms to help identify defensive courses of action against attacker strategies.
Finally, I present my work to enhance the ability of coverage-based fuzzers to identify software vulnerabilities by providing visibility into complex, internal program-states.
ContributorsPaliath, Vivin Suresh (Author) / Doupe, Adam (Thesis advisor) / Shoshitaishvili, Yan (Thesis advisor) / Wang, Ruoyu (Committee member) / Shakarian, Paulo (Committee member) / Arizona State University (Publisher)
Created2023