Matching Items (86)
Description
Radio Frequency Identification (RFID) technology allows objects to be identified electronically by way of a small electronic tag. RFID is quickly becoming quite popular, and there are many security hurdles for this technology to overcome. The iCLASS line of RFID, produced by HID Global, is one such technology that is widely used for secure access control and applications where a contactless authentication element is desirable. Unfortunately, iCLASS has been shown to have security issues. Nevertheless customers continue to use it because of the great cost that would be required to completely replace it. This Honors Thesis will address attacks against iCLASS and means for countering them that do not require such an overhaul.
ContributorsMellott, Matthew John (Author) / Ahn, Gail-Joon (Thesis director) / Thorstenson, Tina (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2014-05
Description
Malware that perform identity theft or steal bank credentials are becoming increasingly common and can cause millions of dollars of damage annually. A large area of research focus is the automated detection and removal of such malware, due to their large impact on millions of people each year. Such a detector will be beneficial to any industry that is regularly the target of malware, such as the financial sector. Typical detection approaches such as those found in commercial anti-malware software include signature-based scanning, in which malware executables are identified based on a unique signature or fingerprint developed for that malware. However, as malware authors continue to modify and obfuscate their malware, heuristic detection is increasingly popular, in which the behaviors of the malware are identified and patterns recognized. We explore a malware analysis and classification framework using machine learning to train classifiers to distinguish between malware and benign programs based upon their features and behaviors. Using both decision tree learning and support vector machines as classifier models, we obtained overall classification accuracies of around 80%. Due to limitations primarily including the usage of a small data set, our approach may not be suitable for practical classification of malware and benign programs, as evident by a high error rate.
ContributorsAnwar, Sajid (Co-author) / Chan, Tsz (Co-author) / Ahn, Gail-Joon (Thesis director) / Zhao, Ziming (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
IoT Media broadcast devices, such as the Roku stick, Amazon Fire, and Chromecast have been emerging onto the market recently as a portable and inexpensive alternative to cable and disk players, allowing easy integration between home and business Wi-Fi networks and television systems capable of supporting HDMI inputs without the additional overhead of setting up a heavy or complicated player or computer. The rapid expansion of these products as a mechanism to provide for TV Everywhere services for entertainment as well as cheap office appliances brings yet another node in the rapidly expanding network of IoT that surrounds us today. However, the security implications of these devices are nearly unexplored, despite their prevalence. In this thesis, I will go over the structure and mechanisms of Chromecast, and explore some of the potential exploits and consequences of the device. The thesis contains an overview of the inner workings of Chromecast, goes over the segregation and limited control and fundamental design choices of the Android based OS. It then identifies the objectives of security, four different potential methods of exploit to compromise those objectives on a Chromecast and/or its attached network, including rogue applications, traffic sniffing, evil access points and the most effective one: deauthentication attack. Tests or relevant analysis were carried out for each of these methods, and conclusions were drawn on their effectiveness. There is then a conclusion revolving around the consequences, mitigation and the future implications of security issues on Chromecast and the larger IoT landscape.
ContributorsHuang, Kaiyi (Author) / Zhao, Ziming (Thesis director) / Ahn, Gail-Joon (Committee member) / W. P. Carey School of Business (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
In the area of hardware, reverse engineering was traditionally focused on developing clones—duplicated components that performed the same functionality of the original component. While reverse engineering techniques have been applied to software, these techniques have instead focused on understanding high-level software designs to ease the software maintenance burden. This approach works well for traditional applications that contain source code, however, there are circumstances, particularly regarding web applications, where it would be very beneficial to clone a web application and no source code is present, e.g., for security testing of the application or for offline mock testing of a third-party web service. We call this the web application cloning problem.
This thesis presents a possible solution to the problem of web application cloning. Our approach is a novel application of inductive programming, which we call inductive reverse engineering. The goal of inductive reverse engineering is to automatically reverse engineer an abstraction of the web application’s code in a completely black-box manner. We build this approach using recent advances in inductive programming, and we solve several technical challenges to scale the inductive programming techniques to realistic-sized web applications. We target the initial version of our inductive reverse engineering tool to a subset of web applications, i.e., those that do not store state and those that do not have loops. We introduce an evaluation methodology for web application cloning techniques and evaluate our approach on several real-world web applications. The results indicate that inductive reverse engineering can effectively reverse engineer specific types of web applications. In the future, we hope to extend the power of inductive reverse engineering to web applications with state and to learn loops, while still maintaining tractability.
This thesis presents a possible solution to the problem of web application cloning. Our approach is a novel application of inductive programming, which we call inductive reverse engineering. The goal of inductive reverse engineering is to automatically reverse engineer an abstraction of the web application’s code in a completely black-box manner. We build this approach using recent advances in inductive programming, and we solve several technical challenges to scale the inductive programming techniques to realistic-sized web applications. We target the initial version of our inductive reverse engineering tool to a subset of web applications, i.e., those that do not store state and those that do not have loops. We introduce an evaluation methodology for web application cloning techniques and evaluate our approach on several real-world web applications. The results indicate that inductive reverse engineering can effectively reverse engineer specific types of web applications. In the future, we hope to extend the power of inductive reverse engineering to web applications with state and to learn loops, while still maintaining tractability.
ContributorsLiao, Kevin (Author) / Doupe, Adam (Thesis director) / Ahn, Gail-Joon (Committee member) / Zhao, Ziming (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Smart cars are defined by the European Union Agency for Network and Information Security (ENISA) as systems providing connected, added-value features in order to enhance car users' experience or improve car safety. Because of their extra features, smart cars utilize sophisticated computer systems. These systems, particularly the Controller Area Network (CAN) bus and protocol, have been shown to provide information that can be used to accurately identify individual Electronic Control Units (ECUs) within a car and the driver that is operating a car. I expand upon this work to consider how information from in-vehicle computer systems can be used to identify individual vehicles. I consider fingerprinting vehicles as a means of aiding in stolen car recovery, thwarting VIN forgery, and supporting an intrusion detection system for networks of smart and autonomous vehicles in the near future. I provide an overview of in-vehicle computer systems and detail my work toward building an ECU testbed and fingerprinting vehicles.
ContributorsDavison, Paulina (Author) / Zhao, Ziming (Thesis director) / Ahn, Gail-Joon (Committee member) / Shoshitaishvili, Yan (Committee member) / Doupe, Adam (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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 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
Description
Node.js is an extremely popular development framework for web applications. The appeal of its event-driven, asynchronous flow and the convenience of JavaScript as its programming language have driven its rapid growth, and it is currently deployed by leading companies in retail, finance, and other important sectors. However, the tools currently available for Node.js developers to secure their applications against malicious attackers are notably scarce. While there has been a substantial amount of security tools created for web applications in many other languages such as PHP and Java, very little exists for Node.js applications. This could compromise private information belonging to companies such as PayPal and WalMart. We propose a tool to statically analyze Node.js web applications for five popular vulnerabilites: cross-site scripting, SQL injection, server-side request forgery, command injection, and code injection. We base our tool off of JSAI, a platform created to parse client-side JavaScript for security risks. JSAI is novel because of its configuration capabilities, which allow a user to choose between various analysis options at runtime in order to select the most thorough analysis with the least amount of processing time. We contribute to the development of our tool by rigorously analyzing and documenting vulnerable functions and objects in Node.js that are relevant to the vulnerabilities we have selected. We intend to use this documentation to build a robust Node.js static analysis tool and we hope that other developers will also incorporate this analysis into their Node.js security projects.
ContributorsWasserman, Jonathan Kanter (Author) / Doupe, Adam (Thesis director) / Ahn, Gail-Joon (Committee member) / Zhao, Ziming (Committee member) / School of Historical, Philosophical and Religious Studies (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
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 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
Description
On Android, existing security procedures require apps to request permissions for access to sensitive resources.
Only when the user approves the requested permissions will the app be installed.
However, permissions are an incomplete security mechanism.
In addition to a user's limited understanding of permissions, the mechanism does not account for the possibility that different permissions used together have the ability to be more dangerous than any single permission alone.
Even if users did understand the nature of an app's requested permissions, this mechanism is still not enough to guarantee that a user's information is protected.
Applications can potentially send or receive sensitive information from other applications without the required permissions by using intents.
In other words, applications can potentially collaborate in ways unforeseen by the user, even if the user understands the permissions of each app independently.
In this thesis, we present several graph-based approaches to address these issues.
We determine the permissions of an app and generate scores based on our assigned value of certain resources.
We analyze these scores overall, as well as in the context of the app's category as determined by Google Play.
We show that these scores can be used to identify overzealous apps, as well as apps that do not properly fit within their category.
We analyze potential interactions between different applications using intents, and identify several promiscuous apps with low permission scores, showing that permissions alone are not sufficient to evaluate the security risks of an app.
Our analyses can form the basis of a system to assist users in identifying apps that can potentially compromise user privacy.
Only when the user approves the requested permissions will the app be installed.
However, permissions are an incomplete security mechanism.
In addition to a user's limited understanding of permissions, the mechanism does not account for the possibility that different permissions used together have the ability to be more dangerous than any single permission alone.
Even if users did understand the nature of an app's requested permissions, this mechanism is still not enough to guarantee that a user's information is protected.
Applications can potentially send or receive sensitive information from other applications without the required permissions by using intents.
In other words, applications can potentially collaborate in ways unforeseen by the user, even if the user understands the permissions of each app independently.
In this thesis, we present several graph-based approaches to address these issues.
We determine the permissions of an app and generate scores based on our assigned value of certain resources.
We analyze these scores overall, as well as in the context of the app's category as determined by Google Play.
We show that these scores can be used to identify overzealous apps, as well as apps that do not properly fit within their category.
We analyze potential interactions between different applications using intents, and identify several promiscuous apps with low permission scores, showing that permissions alone are not sufficient to evaluate the security risks of an app.
Our analyses can form the basis of a system to assist users in identifying apps that can potentially compromise user privacy.
ContributorsGibson, Aaron (Author) / Bazzi, Rida (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Walker, Erin (Committee member) / Arizona State University (Publisher)
Created2015
Description
Smartphones are pervasive nowadays. They are supported by mobile platforms that allow users to download and run feature-rich mobile applications (apps). While mobile apps help users conveniently process personal data on mobile devices, they also pose security and privacy threats and put user's data at risk. Even though modern mobile platforms such as Android have integrated security mechanisms to protect users, most mechanisms do not easily adapt to user's security requirements and rapidly evolving threats. They either fail to provide sufficient intelligence for a user to make informed security decisions, or require great sophistication to configure the mechanisms for enforcing security decisions. These limitations lead to a situation where users are disadvantageous against emerging malware on modern mobile platforms. To remedy this situation, I propose automated and systematic approaches to address three security management tasks: monitoring, assessment, and confinement of mobile apps. In particular, monitoring apps helps a user observe and record apps' runtime behaviors as controlled under security mechanisms. Automated assessment distills intelligence from the observed behaviors and the security configurations of security mechanisms. The distilled intelligence further fuels enhanced confinement mechanisms that flexibly and accurately shape apps' behaviors. To demonstrate the feasibility of my approaches, I design and implement a suite of proof-of-concept prototypes that support the three tasks respectively.
ContributorsJing, Yiming (Author) / Ahn, Gail-Joon (Thesis advisor) / Doupe, Adam (Committee member) / Huang, Dijiang (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2015