Matching Items (22)
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- All Subjects: Security
- Creators: Ahn, Gail-Joon
Description
Network Management is a critical process for an enterprise to configure and monitor the network devices using cost effective methods. It is imperative for it to be robust and free from adversarial or accidental security flaws. With the advent of cloud computing and increasing demands for centralized network control, conventional management protocols like Simple Network Management Protocol (SNMP) appear inadequate and newer techniques like Network Management Datastore Architecture (NMDA) design and Network Configuration (NETCONF) have been invented. However, unlike SNMP which underwent improvements concentrating on security, the new data management and storage techniques have not been scrutinized for the inherent security flaws.
In this thesis, I identify several vulnerabilities in the widely used critical infrastructures which leverage the NMDA design. Software Defined Networking (SDN), a proponent of NMDA, heavily relies on its datastores to program and manage the network. I base my research on the security challenges put forth by the existing datastore’s design as implemented by the SDN controllers. The vulnerabilities identified in this work have a direct impact on the controllers like OpenDayLight, Open Network Operating System and their proprietary implementations (by CISCO, Ericsson, RedHat, Brocade, Juniper, etc). Using the threat detection methodology, I demonstrate how the NMDA-based implementations are vulnerable to attacks which compromise availability, integrity, and confidentiality of the network. I finally propose defense measures to address the security threats in the existing design and discuss the challenges faced while employing these countermeasures.
In this thesis, I identify several vulnerabilities in the widely used critical infrastructures which leverage the NMDA design. Software Defined Networking (SDN), a proponent of NMDA, heavily relies on its datastores to program and manage the network. I base my research on the security challenges put forth by the existing datastore’s design as implemented by the SDN controllers. The vulnerabilities identified in this work have a direct impact on the controllers like OpenDayLight, Open Network Operating System and their proprietary implementations (by CISCO, Ericsson, RedHat, Brocade, Juniper, etc). Using the threat detection methodology, I demonstrate how the NMDA-based implementations are vulnerable to attacks which compromise availability, integrity, and confidentiality of the network. I finally propose defense measures to address the security threats in the existing design and discuss the challenges faced while employing these countermeasures.
ContributorsDixit, Vaibhav Hemant (Author) / Ahn, Gail-Joon (Thesis advisor) / Doupe, Adam (Thesis advisor) / Shoshitaishvili, Yan (Committee member) / Zhao, Ziming (Committee member) / Arizona State University (Publisher)
Created2018
Description
Modern computer processors contain an embedded firmware known as microcode that controls decode and execution of x86 instructions. Although proprietary and relatively obscure, this microcode can be modified using updates released by hardware manufacturers to correct processor logic flaws (errata). At the same time, a malicious microcode update could compromise a processor by implementing new malicious instructions or altering the functionality of existing instructions, including processor-accelerated virtualization or cryptographic primitives. Not only is this attack vector capable of subverting all software-enforced security policies and access controls, but it also leaves behind no postmortem forensic evidence since the write-only patch memory is cleared upon system reset. Although supervisor privileges (ring zero) are required to update processor microcode, this attack cannot be easily mitigated due to the implementation of microcode update functionality within processor silicon. In this paper, we reveal the microarchitecture and mechanism of microcode updates, present a security analysis of this attack vector, and provide some mitigation suggestions.
ContributorsChen, Daming Dominic (Author) / Ahn, Gail-Joon (Thesis director) / Lee, Joohyung (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of International Letters and Cultures (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2014-05
Description
We discuss processes involved in user-centric security design, including the synthesis of goals based on security and usability tasks. We suggest the usage of implicit security and the facilitation of secureuser actions. We propose a process for evaluating usability flaws by treating them as security threats and adapting traditional HCI methods. We discuss how to correct these flaws once they are discovered. Finally, we discuss the Usable Security Development Model for developing usable secure systems.
ContributorsJorgensen, Jan Drake (Author) / Ahn, Gail-Joon (Thesis director) / VanLehn, Kurt (Committee member) / Wilkerson, Kelly (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2013-05
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
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
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
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
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
Microblogging services such as Twitter, Sina Weibo, and Tumblr have been emerging and deeply embedded into people's daily lives. Used by hundreds of millions of users to connect the people worldwide and share and access information in real-time, the microblogging service has also became the target of malicious attackers due to its massive user engagement and structural openness. Although existed, little is still known in the community about new types of vulnerabilities in current microblogging services which could be leveraged by the intelligence-evolving attackers, and more importantly, the corresponding defenses that could prevent both the users and the microblogging service providers from being attacked. This dissertation aims to uncover a number of challenging security and privacy issues in microblogging services and also propose corresponding defenses.
This dissertation makes fivefold contributions. The first part presents the social botnet, a group of collaborative social bots under the control of a single botmaster, demonstrate the effectiveness and advantages of exploiting a social botnet for spam distribution and digital-influence manipulation, and propose the corresponding countermeasures and evaluate their effectiveness. Inspired by Pagerank, the second part describes TrueTop, the first sybil-resilient system to find the top-K influential users in microblogging services with very accurate results and strong resilience to sybil attacks. TrueTop has been implemented to handle millions of nodes and 100 times more edges on commodity computers. The third and fourth part demonstrate that microblogging systems' structural openness and users' carelessness could disclose the later's sensitive information such as home city and age. LocInfer, a novel and lightweight system, is presented to uncover the majority of the users in any metropolitan area; the dissertation also proposes MAIF, a novel machine learning framework that leverages public content and interaction information in microblogging services to infer users' hidden ages. Finally, the dissertation proposes the first privacy-preserving social media publishing framework to let the microblogging service providers publish their data to any third-party without disclosing users' privacy and meanwhile meeting the data's commercial utilities. This dissertation sheds the light on the state-of-the-art security and privacy issues in the microblogging services.
This dissertation makes fivefold contributions. The first part presents the social botnet, a group of collaborative social bots under the control of a single botmaster, demonstrate the effectiveness and advantages of exploiting a social botnet for spam distribution and digital-influence manipulation, and propose the corresponding countermeasures and evaluate their effectiveness. Inspired by Pagerank, the second part describes TrueTop, the first sybil-resilient system to find the top-K influential users in microblogging services with very accurate results and strong resilience to sybil attacks. TrueTop has been implemented to handle millions of nodes and 100 times more edges on commodity computers. The third and fourth part demonstrate that microblogging systems' structural openness and users' carelessness could disclose the later's sensitive information such as home city and age. LocInfer, a novel and lightweight system, is presented to uncover the majority of the users in any metropolitan area; the dissertation also proposes MAIF, a novel machine learning framework that leverages public content and interaction information in microblogging services to infer users' hidden ages. Finally, the dissertation proposes the first privacy-preserving social media publishing framework to let the microblogging service providers publish their data to any third-party without disclosing users' privacy and meanwhile meeting the data's commercial utilities. This dissertation sheds the light on the state-of-the-art security and privacy issues in the microblogging services.
ContributorsZhang, Jinxue (Author) / Zhang, Yanchao (Thesis advisor) / Zhang, Junshan (Committee member) / Ying, Lei (Committee member) / Ahn, Gail-Joon (Committee member) / Arizona State University (Publisher)
Created2016