Defense Notices

The Black Literature Network Project is a comprehensive initiative to disseminate literature knowledge to students, academics, and the general public. It encompasses four distinct portals, each featuring content created and curated by scholars in the field. These portals include the Novel Generator Machine, Literary Data Gallery, Multithreaded Literary Briefs, and Remarkable Receptions Podcast Series. My significant contribution to this project was creating a standalone website for the Current Archives and Collections Index that offers an easily searchable index of black-themed collections. Additionally, I was exclusively responsible for the complete development of the novel generator tool. This application provides customized book recommendations based on user preferences. As a part of the History of Black Writing (HBW) Program, I had the opportunity to customize an open-source annotation tool called Hypothesis. This customization allowed for its use on all websites related to the Black Literature Network Project by the end users. The Black Book Interactive Project (BBIP) collaborates with institutions and groups nationwide to promote access to Black-authored texts and digital publishing. Through BBIP, we plan to increase black literature’s visibility in digital humanities research.

With the increased adoption of complex machine learning algorithms across many different fields, powerful computing platforms have become necessary to meet their computational needs. Multicore platforms are a popular choice as they provide greater computing capabilities and can still meet different size, weight, and power (SWaP) constraints. However, contention for shared hardware resources between multiple cores remains a significant challenge that can lead to interference and unpredictable timing behaviors. Furthermore, this contention can be intentionally induced by malicious actors with the specific goals of delaying safety-critical tasks and jeopardizing system safety. This is done by performing Denial-of-Service (DoS) attacks that target shared resources such that the other cores in a system are unable to access them. When done properly, these shared resource DoS attacks can significantly impact performance and threaten system stability. For example, DoS attacks can cause >300X slowdown on the popular Raspberry Pi 3 embedded platform.

Motivated by the inherent risks posed by these DoS attacks, this dissertation presents investigations and evaluations of shared resource contention on multicore platforms, and the impacts it can have on the performance of real-time tasks. We propose various DoS attacks that each target different shared resources in the memory hierarchy with the goal of causing as much slowdown as possible. We show that each attack can inflict significant temporal slowdowns to victim tasks on target platforms by exploiting different hardware and software mechanisms. We then develop and analyze techniques for providing shared resource isolation and temporal performance guarantees for safety-critical tasks running on multicore platforms. In particular, we find that bandwidth throttling mechanisms are effective solutions against most DoS attacks and can protect the performance of real-time victim tasks.

Development and deployment of trusted systems often require definitive identification of devices. A remote entity should have confidence that a device is as it claims to be. An ideal method for fulfulling this need is through the use of secure device identitifiers. A secure device identifier (DevID) is defined as an identifier that is cryptographically bound to a device. A DevID must not be transferable from one device to another as that would allow distinct devices to be identified as the same. Since the Trusted Platform Module (TPM) is a secure Root of Trust for Storage, it provides the necessary protections for storing these identifiers. Consequently, the Trusted Computing Group (TCG) recommends the use of TPM keys for DevIDs. The TCG's specification TPM 2.0 Keys for Device Identity and Attestation describes several methods for remotely proving a key to be resident in a specific device's TPM. These methods are carefully constructed protocols which are intended to be performed by a trusted Certificate Authority (CA) in communication with a certificate-requesting device. DevID certificates produced by an OEM's CA at device manufacturing time may be used to provide definitive evidence to a remote entity that a key belongs to a specific device. Whereas DevID certificates produced by an Owner/Administrator's CA require a chain of certificates in order to verify a chain of trust to an OEM-provided root certificate. This distinction is due to the differences in the respective protocols prescribed by the TCG's specification. We aim to abstractly model these protocols and formally verify that their resulting assurances on TPM-residency do in fact hold. We choose this goal since the TCG themselves do not provide any proofs or clear justifications for how the protocols might provide these assurances. The resulting TPM-command library and execution relation modeled in Coq may easily be expanded upon to become useful in verifying a wide range of properties regarding DevIDs and TPMs.

Remote attestation is a process of establishing trust between various systems on a network. Until now, attestations had to be done on the fly as caching attestations had not yet been solved. With the blockchain providing a monotonic record, this work attempts to enable attestations to be cached. This paves the way for more complex attestation protocols to fit the wide variety of needs of users. We also developed specifications for these records to be cached on the blockchain.

Scattering by nanoparticles is an exciting branch of physics to control and manipulate light. More specifically, there have been fascinating developments regarding light scattering by sub-wavelength particles, including high-index dielectric and metal particles, for their applications in optical resonance phenomena, detecting the fluorescence of molecules, enhancing Raman scattering, transferring the energy to the higher order modes, sensing and photodetector technologies. It recently gained more attention due to its near-field effect at the nanoscale and achieving new insights and applications through space and time-varying parametric modulation and including nonlinear effects. When the particle size is comparable to or slightly bigger than the incident wavelength, Mie solutions to Maxwell's equations describe these electromagnetic scattering problems. The addition and excitation of nonlinear effects in these high-indexed sub-wavelength dielectric and plasmonic particles might improve the existing performance of the system or provide additional features directed toward unique applications. In this thesis, we study the Mie scattering from dielectric and plasmonic particles in the presence of nonlinear effects. For dielectrics, we present a numerical study of the linear and nonlinear diffraction and focusing properties of dielectric metasurfaces consisting of silicon microcylinder arrays resting on a silicon substrate. Upon diffraction, such structures lead to the formation of near-field intensity profiles reminiscent of photonic nanojets and propagate similarly. Our results indicate that the Kerr nonlinear effect enhances light concentration throughout the generated photonic jet with an increase in the intensity of about 20% compared to the linear regime for the power levels considered in this work. The transverse beamwidth remains subwavelength in all cases, and the nonlinear effect reduces the full width. In the future, we want to optimize the performance through parametric modification of the system and continue our study with plasmonic structures in time–varying scenarios. We hope that with appropriate parametric modulation, intermodal energy transfer is possible in such structures. We want to explore the nonlinear excitation to transfer energy in higher-order modes by exploiting different wave-mixing interactions in time-modulated scatterers.

During remote attestation, a relying party prompts a target to perform some stateful measurement which can be appraised to determine trust in the target's system. In this current framework, requested measurement operations must be provisioned by a knowledgeable system user who may fail to consider situational demands which potentially impact the desired measurement. To solve this problem, we introduce negotiation: a framework that allows the target and relying party to mutually determine an attestation protocol that satisfies both the target's need to protect sensitive information and the relying party's desire for a comprehensive measurement. We designed and verified this negotiation procedure such that for all negotiations, we can provably produce an executable protocol that satisfies the targets privacy standards. With the remainder of this work, we aim to realize and instantiate protocol orderings ensuring negotiation produces a protocol sufficient for the relying party. All progress is towards our ultimate goal of producing a working, fully verified negotiation scheme which will be integrated into our current attestation framework for flexible, end-to-end attestations.

Information retrieval system plays an important role in the modern era in retrieving relevant information from a large collection of data, such as documents, webpages, and other multimedia content. Having an information retrieval system in any domain allows users to collect relevant information. Unfortunately, navigating a modern-day recipe website presents the audience with numerous recipes in a colorful user interface but with very little capability to search and narrow down your content based on your specific interests. The goal of the project is to develop a search engine for recipes using standard TF-IDF weighting and to improve the performance of the standard IR by implementing term proximity. The approach used to calculate term proximity in this project is a hybrid approach, a combination of span-based and pair-based approaches. The project architecture includes a crawler, a database, an API, a service responsible for TF-IDF weighting and term proximity calculation, and a web application to present the search results. 

The control-flow graph (CFG) is a graphical representation of the program and holds information that is critical to the correct application of many other program analysis, performance optimization, and software security algorithms and techniques. While CFG generation is an ordinary task for source-level tools, like the compiler, the loss of high-level program information makes accurate CFG recovery a challenging issue for binary-level software reverse engineering (SRE) tools. Earlier research has shown that while advanced SRE tools can precisely reconstruct most of the CFG for the programs, important gaps and inaccuracies remain that may hamper critical tasks, from vulnerability and malicious code detection to adequately securing software binaries.

In this paper, we study three reverse engineering tools - angr, radare2 and Ghidra and perform an in-depth analysis of control-flow graphs generated by these tools. We develop a unique methodology using manual analysis and automated scripting to understand and categorize the CFG errors over a large benchmark set. Of the several interesting observations revealed by this work, one that is particularly unexpected is that most errors in the reconstructed CFGs appear to not be intrinsic limitations of the binary-level algorithms, as currently believed, and may be simply eliminated by more robust implementations. We expect our work to lead to more accurate CFG reconstruction in SRE tools and improved precision for other algorithms that employ CFGs.

Security Operations Centers (SOCs) are central components of modern enterprise networks. Organizations in industry, government, and academia deploy SOCs to manage their networks, defend against cyber threats, and maintain regulatory compliance. For reporting, SOC leadership typically use metrics such as “number of security incidents”, “mean time to remediation/ticket closure”, and “risk analysis” to name a few. However, these commonly leveraged metrics may not necessarily reflect the effectiveness of a SOC and its supporting tools.

To better understand these environments, we employ ethnographic approaches (e.g., participant observation) and embed a graduate student (a.k.a., field worker) in a real-world SOC. As the field worker worked in-person, alongside SOC employees and recorded observations on technological tools, employees and culture, COVID-19's work-from-home (WFH) phenomena occurred. In response, this dissertation traces and analyzes the SOC's effort to adapt and reprioritize. By intersecting historical analysis (starting in the 1970s) and ethnographic field notes (analyzed 352 field notes across 1,000+ hours in a SOC over 34 months) whilst complementing with quantitative interviews (covering 7 other SOCs), we find additional causal forces that, for decades, have pushed SOC network management toward endpoints.

Although endpoint management is not a novel concept to SOCs, COVID-19's WFH phenomena highlighted the need for flexible, supportive, and customizable metrics. As such, we develop a sociotechnical metrics framework with these qualities in mind and limit the scope to a core SOC function: alert handling. With a similar ethnographic approach (participant observation paired with semi-structured interviews covering 15 SOC employees across 10 SOCs), we develop the framework's foundation by analyzing and capturing the alert handling process (a.k.a., alert triage). This process demonstrates the significance of not only technical expertise (e.g., data exfiltration, command and control, etc.) but also the social characteristics (e.g., collaboration, communication, etc.). In fact, we point out the underlying presence and importance of expert judgment during alert triaging particularly during conclusion development.

In addition to the aforementioned qualities, our alert handling sociotechnical metrics framework aims to capture current gaps during the alert triage process that, if improved, could help SOC employees' effectiveness. With the focus upon this process and the uncovered limitations SOCs usually face today during alert handling, we validate not only this flexibility of our framework but also the accuracy in a real-world SOC

Vertical velocity is the rate at which ice moves vertically within an ice sheet, usually measured in meters per year. This movement can occur due to various factors, including accumulation, ice deformation, basal sliding, and subglacial melting. The measurement of vertical velocities within the ice sheet can assist in determining the age of the ice and assessing the rheology of the ice, thereby mitigating uncertainties due to analytical approximations of ice flow models.

We apply differential interferometric synthetic aperture radar (DInSAR) techniques to data from the Multichannel Coherent Radar Depth Sounder (MCoRDS) to measure the vertical displacement of englacial layers within an ice sheet. DInSAR’s accuracy is usually on the order of a small fraction of the wavelength (e.g., millimeter to centimeter precision is typical) in monitoring displacement along the radar line of sight (LOS). Ground-based Autonomous phase-sensitive Radio-Echo Sounder (ApRES) units have demonstrated the ability to precisely measure the relative vertical velocity by taking multiple measurements from the same location on the ice. Airborne systems can make a similar measurement but can suffer from spatial baseline errors since it is generally impossible to fly over the same stretch of ice on each pass with enough precision to ignore the spatial baseline. In this work, we compensate for spatial baseline errors using precise trajectory information and estimates of the cross-track layer slope using direction of arrival estimation. The current DInSAR algorithm is applied to airborne radar depth sounder data to produce results for flights near Summit camp and the EGIG (Expéditions Glaciologiques Internationales au Groenland) line in Greenland using the CReSIS toolbox. The current approach estimates the baseline error in multiple steps. Each step has dependencies on all the values to be estimated. To overcome this drawback, we have implemented a maximum likelihood estimator that jointly estimates the vertical velocity, the cross-track internal layer slope, and the unknown baseline error due to GPS and INS (Inertial Navigation System) errors. We incorporate the Lliboutry parametric model for vertical velocity into the maximum likelihood estimator framework.

To improve the direction of arrival estimation, we explore the use of focusing matrices against other wideband direction of arrival methods, such as wideband MLE, wideband MUSIC, and wideband MVDR, by comparing the mean squared error of the DOA estimates.