+publisher:"University of North Carolina" +contributor:("Monrose, Fabian")

University of North Carolina

1. Zhang, Pintian. A Frameless Imaging System: Architectural Design and Simulation.

Degree: Computer Science, 2016, University of North Carolina

URL: https://cdr.lib.unc.edu/record/uuid:8d43361b-f9a5-48d0-a3a9-b154976095f8

► This report presents the work on the architectural design and simulation of the IMAGIN sensing system, a novel frameless camera sensor system that can provide… (more)

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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APA (6^th Edition):

Zhang, P. (2016). A Frameless Imaging System: Architectural Design and Simulation. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:8d43361b-f9a5-48d0-a3a9-b154976095f8

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Zhang, Pintian. “A Frameless Imaging System: Architectural Design and Simulation.” 2016. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:8d43361b-f9a5-48d0-a3a9-b154976095f8.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Zhang, Pintian. “A Frameless Imaging System: Architectural Design and Simulation.” 2016. Web. 18 Jan 2021.

Vancouver:

Zhang P. A Frameless Imaging System: Architectural Design and Simulation. [Internet] [Thesis]. University of North Carolina; 2016. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:8d43361b-f9a5-48d0-a3a9-b154976095f8.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhang P. A Frameless Imaging System: Architectural Design and Simulation. [Thesis]. University of North Carolina; 2016. Available from: https://cdr.lib.unc.edu/record/uuid:8d43361b-f9a5-48d0-a3a9-b154976095f8

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of North Carolina

2. Bartel, Jacob. Predictions to Ease Users' Effort in Scalable Sharing.

Degree: Computer Science, 2015, University of North Carolina

URL: https://cdr.lib.unc.edu/record/uuid:279a0cd5-ee7e-4045-8be2-2bf2323107ff

► Significant user effort is required to choose recipients of shared information, which grows as the scale of the number of potential or target recipients increases.… (more)

▼ Significant user effort is required to choose recipients of shared information, which grows as the scale of the number of potential or target recipients increases. It is our thesis that it is possible to develop new approaches to predict persistent named groups, ephemeral groups, and response times that will reduce user effort. We predict persistent named groups using the insight that implicit social graphs inferred from messages can be composed with existing prediction techniques designed for explicit social graphs, thereby demonstrating similar grouping patterns in email and communities. However, this approach still requires that users know when to generate such predictions. We predict group creation times based on the intuition that bursts of change in the social graph likely signal named group creation. While these recommendations can help create new groups, they do not update existing ones. We predict how existing named groups should evolve based on the insight that the growth rates of named groups and the underlying social graph will match. When appropriate named groups do not exist, it is useful to predict ephemeral groups of information recipients. We have developed an approach to make hierarchical recipient recommendations that groups the elements in a flat list of recommended recipients, and thus is composable with existing flat recipient-recommendation techniques. It is based on the insight that groups of recipients in past messages can be organized in a tree. To help users select among alternative sets of recipients, we have made predictions about the scale of response time of shared information, based on the insights that messages addressed to similar recipients or containing similar titles will yield similar response times. Our prediction approaches have been applied to three specific systems - email, Usenet and Stack Overflow - based on the insight that email recipients correspond to Stack Overflow tags and Usenet newsgroups. We evaluated these approaches with actual user data using new metrics for measuring the differences in scale between predicted and actual response times and measuring the costs of eliminating spurious named-group predictions, editing named-group recommendations for use in future messages, scanning and selecting hierarchical ephemeral group-recommendations, and manually entering recipients. Advisors/Committee Members: Bartel, Jacob, Dewan, Prasun, Ahalt, Stanley, Berg, Alexander, Kelly, Diane, Jojic, Vladimir, Monrose, Fabian, Reiter, Michael.

Subjects/Keywords: Computer science; College of Arts and Sciences; Department of Computer Science

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APA (6^th Edition):

Bartel, J. (2015). Predictions to Ease Users' Effort in Scalable Sharing. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:279a0cd5-ee7e-4045-8be2-2bf2323107ff

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Bartel, Jacob. “Predictions to Ease Users' Effort in Scalable Sharing.” 2015. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:279a0cd5-ee7e-4045-8be2-2bf2323107ff.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Bartel, Jacob. “Predictions to Ease Users' Effort in Scalable Sharing.” 2015. Web. 18 Jan 2021.

Vancouver:

Bartel J. Predictions to Ease Users' Effort in Scalable Sharing. [Internet] [Thesis]. University of North Carolina; 2015. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:279a0cd5-ee7e-4045-8be2-2bf2323107ff.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bartel J. Predictions to Ease Users' Effort in Scalable Sharing. [Thesis]. University of North Carolina; 2015. Available from: https://cdr.lib.unc.edu/record/uuid:279a0cd5-ee7e-4045-8be2-2bf2323107ff

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of North Carolina

3. White, Andrew. Practical Analysis of Encrypted Network Traffic.

Degree: Computer Science, 2015, University of North Carolina

URL: https://cdr.lib.unc.edu/record/uuid:a009d909-d0f3-4870-a49c-294cd966ab5a

► The growing use of encryption in network communications is an undoubted boon for user privacy. However, the limitations of real-world encryption schemes are still not… (more)

▼ The growing use of encryption in network communications is an undoubted boon for user privacy. However, the limitations of real-world encryption schemes are still not well understood, and new side-channel attacks against encrypted communications are disclosed every year. Furthermore, encrypted network communications, by preventing inspection of packet contents, represent a significant challenge from a network security perspective: our existing infrastructure relies on such inspection for threat detection. Both problems are exacerbated by the increasing prevalence of encrypted traffic: recent estimates suggest that 65% or more of downstream Internet traffic will be encrypted by the end of 2016. This work addresses these problems by expanding our understanding of the properties and characteristics of encrypted network traffic and exploring new, specialized techniques for the handling of encrypted traffic by network monitoring systems. We first demonstrate that opaque traffic, of which encrypted traffic is a subset, can be identified in real-time and how this ability can be leveraged to improve the capabilities of existing IDS systems. To do so, we evaluate and compare multiple methods for rapid identification of opaque packets, ultimately pinpointing a simple hypothesis test (which can be implemented on an FPGA) as an efficient and effective detector of such traffic. In our experiments, using this technique to “winnow”, or filter, opaque packets from the traffic load presented to an IDS system significantly increased the throughput of the system, allowing the identification of many more potential threats than the same system without winnowing. Second, we show that side channels in encrypted VoIP traffic enable the reconstruction of approximate transcripts of conversations. Our approach leverages techniques from linguistics, machine learning, natural language processing, and machine translation to accomplish this task despite the limited information leaked by such side channels. Our ability to do so underscores both the potential threat to user privacy which such side channels represent and the degree to which this threat has been underestimated. Finally, we propose and demonstrate the effectiveness of a new paradigm for identifying HTTP resources retrieved over encrypted connections. Our experiments demonstrate how the predominant paradigm from prior work fails to accurately represent real-world situations and how our proposed approach offers significant advantages, including the ability to infer partial information, in comparison. We believe these results represent both an enhanced threat to user privacy and an opportunity for network monitors and analysts to improve their own capabilities with respect to encrypted traffic. Advisors/Committee Members: White, Andrew, Monrose, Fabian, Reiter, Michael, Bailey, Michael, Jeffay, Kevin, Porras, Phillip.

Subjects/Keywords: Computer science; College of Arts and Sciences; Department of Computer Science

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

White, A. (2015). Practical Analysis of Encrypted Network Traffic. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:a009d909-d0f3-4870-a49c-294cd966ab5a

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

White, Andrew. “Practical Analysis of Encrypted Network Traffic.” 2015. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:a009d909-d0f3-4870-a49c-294cd966ab5a.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

White, Andrew. “Practical Analysis of Encrypted Network Traffic.” 2015. Web. 18 Jan 2021.

Vancouver:

White A. Practical Analysis of Encrypted Network Traffic. [Internet] [Thesis]. University of North Carolina; 2015. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:a009d909-d0f3-4870-a49c-294cd966ab5a.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

White A. Practical Analysis of Encrypted Network Traffic. [Thesis]. University of North Carolina; 2015. Available from: https://cdr.lib.unc.edu/record/uuid:a009d909-d0f3-4870-a49c-294cd966ab5a

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of North Carolina

4. Taylor, Teryl. Using Context to Improve Network-based Exploit Kit Detection.

Degree: Computer Science, 2016, University of North Carolina

URL: https://cdr.lib.unc.edu/record/uuid:71d5279c-99b1-4f99-b8d2-d7a2b82dae8a

► Today, our computers are routinely compromised while performing seemingly innocuous activities like reading articles on trusted websites (e.g., the NY Times). These compromises are perpetrated… (more)

▼ Today, our computers are routinely compromised while performing seemingly innocuous activities like reading articles on trusted websites (e.g., the NY Times). These compromises are perpetrated via complex interactions involving the advertising networks that monetize these sites. Web-based compromises such as exploit kits are similar to any other scam – the attacker wants to lure an unsuspecting client into a trap to steal private information, or resources – generating 10s of millions of dollars annually. Exploit kits are web-based services specifically designed to capitalize on vulnerabilities in unsuspecting client computers in order to install malware without a user's knowledge. Sadly, it only takes a single successful infection to ruin a user's financial life, or lead to corporate breaches that result in millions of dollars of expense and loss of customer trust. Exploit kits use a myriad of techniques to obfuscate each attack instance, making current network-based defenses such as signature-based network intrusion detection systems far less effective than in years past. Dynamic analysis or honeyclient analysis on these exploits plays a key role in identifying new attacks for signature generation, but provides no means of inspecting end-user traffic on the network to identify attacks in real time. As a result, defenses designed to stop such malfeasance often arrive too late or not at all resulting in high false positive and false negative (error) rates. In order to deal with these drawbacks, three new detection approaches are presented. To deal with the issue of a high number of errors, a new technique for detecting exploit kit interactions on a network is proposed. The technique capitalizes on the fact that an exploit kit leads its potential victim through a process of exploitation by forcing the browser to download multiple web resources from malicious servers. This process has an inherent structure that can be captured in HTTP traffic and used to significantly reduce error rates. The approach organizes HTTP traffic into tree-like data structures, and, using a scalable index of exploit kit traces as samples, models the detection process as a subtree similarity search problem. The technique is evaluated on 3,800 hours of web traffic on a large enterprise network, and results show that it reduces false positive rates by four orders of magnitude over current state-of-the-art approaches. While utilizing structure can vastly improve detection rates over current approaches, it does not go far enough in helping defenders detect new, previously unseen attacks. As a result, a new framework that applies dynamic honeyclient analysis directly on network traffic at scale is proposed. The framework captures and stores a configurable window of reassembled HTTP objects network wide, uses lightweight content rendering to establish the chain of requests leading up to a suspicious event, then serves the initial response content back to the honeyclient in an isolated network. The framework is evaluated on a diverse collection of… Advisors/Committee Members: Taylor, Teryl, Monrose, Fabian, McHugh, John, Aikat, Jay, Berg, Alexander, Wang, Ting.

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Taylor, T. (2016). Using Context to Improve Network-based Exploit Kit Detection. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:71d5279c-99b1-4f99-b8d2-d7a2b82dae8a

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Taylor, Teryl. “Using Context to Improve Network-based Exploit Kit Detection.” 2016. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:71d5279c-99b1-4f99-b8d2-d7a2b82dae8a.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Taylor, Teryl. “Using Context to Improve Network-based Exploit Kit Detection.” 2016. Web. 18 Jan 2021.

Vancouver:

Taylor T. Using Context to Improve Network-based Exploit Kit Detection. [Internet] [Thesis]. University of North Carolina; 2016. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:71d5279c-99b1-4f99-b8d2-d7a2b82dae8a.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Taylor T. Using Context to Improve Network-based Exploit Kit Detection. [Thesis]. University of North Carolina; 2016. Available from: https://cdr.lib.unc.edu/record/uuid:71d5279c-99b1-4f99-b8d2-d7a2b82dae8a

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of North Carolina

5. Snow, Kevin. Identifying Code Injection and Reuse Payloads In Memory Error Exploits.

Degree: Computer Science, 2014, University of North Carolina

URL: https://cdr.lib.unc.edu/record/uuid:7ffd79c5-4067-4eb2-a3f7-74e60fb7d8e0

► Today's most widely exploited applications are the web browsers and document readers we use every day. The immediate goal of these attacks is to compromise… (more)

▼ Today's most widely exploited applications are the web browsers and document readers we use every day. The immediate goal of these attacks is to compromise target systems by executing a snippet of malicious code in the context of the exploited application. Technical tactics used to achieve this can be classified as either code injection - wherein malicious instructions are directly injected into the vulnerable program - or code reuse, where bits of existing program code are pieced together to form malicious logic. In this thesis, I present a new code reuse strategy that bypasses existing and up-and-coming mitigations, and two methods for detecting attacks by identifying the presence of code injection or reuse payloads. Fine-grained address space layout randomization efficiently scrambles program code, limiting one's ability to predict the location of useful instructions to construct a code reuse payload. To expose the inadequacy of this exploit mitigation, a technique for "just-in-time" exploitation is developed. This new technique maps memory on-the-fly and compiles a code reuse payload at runtime to ensure it works in a randomized application. The attack also works in face of all other widely deployed mitigations, as demonstrated with a proof-of-concept attack against Internet Explorer 10 in Windows 8. This motivates the need for detection of such exploits rather than solely relying on prevention. Two new techniques are presented for detecting attacks by identifying the presence of a payload. Code reuse payloads are identified by first taking a memory snapshot of the target application, then statically profiling the memory for chains of code pointers that reuse code to implement malicious logic. Code injection payloads are identified with runtime heuristics by leveraging hardware virtualization for efficient sandboxed execution of all buffers in memory. Employing both detection methods together to scan program memory takes about a second and produces negligible false positives and false negatives provided that the given exploit is functional and triggered in the target application version. Compared to other strategies, such as the use of signatures, this approach requires relatively little effort spent on maintenance over time and is capable of detecting never before seen attacks. Moving forward, one could use these contributions to form the basis of a unique and effective network intrusion detection system (NIDS) to augment existing systems. Advisors/Committee Members: Snow, Kevin, Monrose, Fabian, Smith, Don, Singh, Montek, Bailey, Michael, Provos, Niels.

Subjects/Keywords: Computer science; College of Arts and Sciences; Department of Computer Science

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Snow, K. (2014). Identifying Code Injection and Reuse Payloads In Memory Error Exploits. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:7ffd79c5-4067-4eb2-a3f7-74e60fb7d8e0

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Snow, Kevin. “Identifying Code Injection and Reuse Payloads In Memory Error Exploits.” 2014. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:7ffd79c5-4067-4eb2-a3f7-74e60fb7d8e0.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Snow, Kevin. “Identifying Code Injection and Reuse Payloads In Memory Error Exploits.” 2014. Web. 18 Jan 2021.

Vancouver:

Snow K. Identifying Code Injection and Reuse Payloads In Memory Error Exploits. [Internet] [Thesis]. University of North Carolina; 2014. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:7ffd79c5-4067-4eb2-a3f7-74e60fb7d8e0.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Snow K. Identifying Code Injection and Reuse Payloads In Memory Error Exploits. [Thesis]. University of North Carolina; 2014. Available from: https://cdr.lib.unc.edu/record/uuid:7ffd79c5-4067-4eb2-a3f7-74e60fb7d8e0

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of North Carolina

6. Cochran, Robert. Symbolic Verification of Remote Client Behavior in Distributed Systems.

Degree: Computer Science, 2016, University of North Carolina

URL: https://cdr.lib.unc.edu/record/uuid:cece40cb-c644-4c69-8afd-8e86c31017bd

► A malicious client in a distributed system can undermine the integrity of the larger distributed application in a number of different ways. For example, a… (more)

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Cochran, R. (2016). Symbolic Verification of Remote Client Behavior in Distributed Systems. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:cece40cb-c644-4c69-8afd-8e86c31017bd

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Cochran, Robert. “Symbolic Verification of Remote Client Behavior in Distributed Systems.” 2016. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:cece40cb-c644-4c69-8afd-8e86c31017bd.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Cochran, Robert. “Symbolic Verification of Remote Client Behavior in Distributed Systems.” 2016. Web. 18 Jan 2021.

Vancouver:

Cochran R. Symbolic Verification of Remote Client Behavior in Distributed Systems. [Internet] [Thesis]. University of North Carolina; 2016. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:cece40cb-c644-4c69-8afd-8e86c31017bd.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Cochran R. Symbolic Verification of Remote Client Behavior in Distributed Systems. [Thesis]. University of North Carolina; 2016. Available from: https://cdr.lib.unc.edu/record/uuid:cece40cb-c644-4c69-8afd-8e86c31017bd

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of North Carolina

7. Xu, Yi. Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints.

Degree: Computer Science, 2016, University of North Carolina

URL: https://cdr.lib.unc.edu/record/uuid:926a5d94-944d-4c61-963f-b863b0dc1f41

► The continuous stream of videos that are uploaded and shared on the Internet has been leveraged by computer vision researchers for a myriad of detection… (more)

▼ The continuous stream of videos that are uploaded and shared on the Internet has been leveraged by computer vision researchers for a myriad of detection and retrieval tasks, including gesture detection, copy detection, face authentication, etc. However, the existing state-of-the-art event detection and retrieval techniques fail to deal with several real-world challenges (e.g., low resolution, low brightness and noise) under adversary constraints. This dissertation focuses on these challenges in realistic scenarios and demonstrates practical methods to address the problem of robustness and efficiency within video event detection and retrieval systems in five application settings (namely, CAPTCHA decoding, face liveness detection, reconstructing typed input on mobile devices, video confirmation attack, and content-based copy detection). Specifically, for CAPTCHA decoding, I propose an automated approach which can decode moving-image object recognition (MIOR) CAPTCHAs faster than humans. I showed that not only are there inherent weaknesses in current MIOR CAPTCHA designs, but that several obvious countermeasures (e.g., extending the length of the codeword) are not viable. More importantly, my work highlights the fact that the choice of underlying hard problem selected by the designers of a leading commercial solution falls into a solvable subclass of computer vision problems. For face liveness detection, I introduce a novel approach to bypass modern face authentication systems. More specifically, by leveraging a handful of pictures of the target user taken from social media, I show how to create realistic, textured, 3D facial models that undermine the security of widely used face authentication solutions. My framework makes use of virtual reality (VR) systems, incorporating along the way the ability to perform animations (e.g., raising an eyebrow or smiling) of the facial model, in order to trick liveness detectors into believing that the 3D model is a real human face. I demonstrate that such VR-based spoofing attacks constitute a fundamentally new class of attacks that point to a serious weaknesses in camera-based authentication systems. For reconstructing typed input on mobile devices, I proposed a method that successfully transcribes the text typed on a keyboard by exploiting video of the user typing, even from significant distances and from repeated reflections. This feat allows us to reconstruct typed input from the image of a mobile phone’s screen on a user’s eyeball as reflected through a nearby mirror, extending the privacy threat to include situations where the adversary is located around a corner from the user. To assess the viability of a video confirmation attack, I explored a technique that exploits the emanations of changes in light to reveal the programs being watched. I leverage the key insight that the observable emanations of a display (e.g., a TV or monitor) during presentation of the viewing content induces a distinctive flicker pattern that can be exploited by an adversary. My proposed approach… Advisors/Committee Members: Xu, Yi, Frahm, Jan-Michael, Monrose, Fabian, Dunn, Enrique, Berg, Tamara, Crandall, David.

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Xu, Y. (2016). Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:926a5d94-944d-4c61-963f-b863b0dc1f41

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Xu, Yi. “Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints.” 2016. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:926a5d94-944d-4c61-963f-b863b0dc1f41.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Xu, Yi. “Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints.” 2016. Web. 18 Jan 2021.

Vancouver:

Xu Y. Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints. [Internet] [Thesis]. University of North Carolina; 2016. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:926a5d94-944d-4c61-963f-b863b0dc1f41.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Xu Y. Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints. [Thesis]. University of North Carolina; 2016. Available from: https://cdr.lib.unc.edu/record/uuid:926a5d94-944d-4c61-963f-b863b0dc1f41

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of North Carolina

8. Cochran, Robert. Symbolic Verification of Remote Client Behavior in Distributed Systems.

Degree: Computer Science, 2016, University of North Carolina

URL: https://cdr.lib.unc.edu/record/uuid:cf4c9320-985d-4abc-8816-c4758d4dbd1c

► A malicious client in a distributed system can undermine the integrity of the larger distributed application in a number of different ways. For example, a… (more)

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

Record Details Similar Records Cite « Share

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Cochran, R. (2016). Symbolic Verification of Remote Client Behavior in Distributed Systems. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:cf4c9320-985d-4abc-8816-c4758d4dbd1c

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Cochran, Robert. “Symbolic Verification of Remote Client Behavior in Distributed Systems.” 2016. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:cf4c9320-985d-4abc-8816-c4758d4dbd1c.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Cochran, Robert. “Symbolic Verification of Remote Client Behavior in Distributed Systems.” 2016. Web. 18 Jan 2021.

Vancouver:

Cochran R. Symbolic Verification of Remote Client Behavior in Distributed Systems. [Internet] [Thesis]. University of North Carolina; 2016. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:cf4c9320-985d-4abc-8816-c4758d4dbd1c.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Cochran R. Symbolic Verification of Remote Client Behavior in Distributed Systems. [Thesis]. University of North Carolina; 2016. Available from: https://cdr.lib.unc.edu/record/uuid:cf4c9320-985d-4abc-8816-c4758d4dbd1c

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of North Carolina

9. Sanders, Sean. Techniques for the Analysis of Modern Web Page Traffic using Anonymized TCP/IP Headers.

Degree: Computer Science, 2017, University of North Carolina

URL: https://cdr.lib.unc.edu/record/uuid:62494e12-b6b0-44ff-b169-66f2376b8583

► Analysis of traces of network traffic is a methodology that has been widely adopted for studying the Web for several decades. However, due to recent… (more)

▼ Analysis of traces of network traffic is a methodology that has been widely adopted for studying the Web for several decades. However, due to recent privacy legislation and increasing adoption of traffic encryption, often only anonymized TCP/IP headers are accessible in traffic traces. For traffic traces to remain useful for analysis, techniques must be developed to glean insight using this limited header information. This dissertation evaluates approaches for classifying individual web page downloads — referred to as web page classification — when only anonymized TCP/IP headers are available. The context in which web page classification is defined and evaluated in this dissertation is different from prior traffic classification methods in three ways. First, the impact of diversity in client platforms (browsers, operating systems, device type, and vantage point) on network traffic is explicitly considered. Second, the challenge of overlapping traffic from multiple web pages is explicitly considered and demultiplexing approaches are evaluated (web page segmentation). And lastly, unlike prior work on traffic classification, four orthogonal labeling schemes are considered (genre-based, device-based, navigation-based, and video streaming-based) — these are of value in several web-related applications, including privacy analysis, user behavior modeling, traffic forecasting, and potentially behavioral ad-targeting. We conduct evaluations using large collections of both synthetically generated data, as well as browsing data from real users. Our analysis shows that the client platform choice has a statistically significant impact on web traffic. It also shows that change point detection methods, a new class of segmentation approach, outperform existing idle time-based methods. Overall, this work establishes that web page classification performance can be improved by: (i) incorporating client platform differences in the feature selection and training methodology, and (ii) utilizing better performing web page segmentation approaches. This research increases the overall awareness on the challenges associated with the analysis of modern web traffic. It shows and advocates for considering real-world factors, such as client platform diversity and overlapping traffic from multiple streams, when developing and evaluating traffic analysis techniques. Advisors/Committee Members: Sanders, Sean, Kaur, Jasleen, Monrose, Fabian, Beyah, Raheem, Jeffay, Kevin, Aikat, Jay, University of North Carolina at Chapel Hill.

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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APA (6^th Edition):

Sanders, S. (2017). Techniques for the Analysis of Modern Web Page Traffic using Anonymized TCP/IP Headers. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:62494e12-b6b0-44ff-b169-66f2376b8583

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Sanders, Sean. “Techniques for the Analysis of Modern Web Page Traffic using Anonymized TCP/IP Headers.” 2017. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:62494e12-b6b0-44ff-b169-66f2376b8583.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Sanders, Sean. “Techniques for the Analysis of Modern Web Page Traffic using Anonymized TCP/IP Headers.” 2017. Web. 18 Jan 2021.

Vancouver:

Sanders S. Techniques for the Analysis of Modern Web Page Traffic using Anonymized TCP/IP Headers. [Internet] [Thesis]. University of North Carolina; 2017. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:62494e12-b6b0-44ff-b169-66f2376b8583.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sanders S. Techniques for the Analysis of Modern Web Page Traffic using Anonymized TCP/IP Headers. [Thesis]. University of North Carolina; 2017. Available from: https://cdr.lib.unc.edu/record/uuid:62494e12-b6b0-44ff-b169-66f2376b8583

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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