Defense Notices

While identifying an object in an image is almost an instantaneous task for the human visual perception system, it takes more effort and time to process and identify a camouflaged object - an entity that flawlessly blends with the background in the image. This explains why it is much more challenging to enable a machine learning model to do the same, in comparison to generic object detection or salient object detection.

This project implements a framework called Search Identification Network, that simulates the search and identification pattern adopted by predators in hunting their prey and applies it to detect camouflaged objects. The efficiency of this framework in detecting polyps in medical image datasets is also measured.

Matched processing is fundamental filtering operation within radar signal processing to estimate scattering in the radar scene based on the transmit signal. Although matched processing maximizes the signal-to-noise ratio (SNR), the filtering operation is ineffective when interference is captured in the receive measurement. Adaptive interference mitigation combined with matched processing has proven to mitigate interference and estimate the radar scene. But, a known caveat of matched processing is the resulting sidelobes that may mask other scatterers. The sidelobes can be efficiently addressed by windowing but this approach also comes with limited suppression capabilities, loss in resolution, and loss in SNR. The recent emergence of mismatch processing has shown to optimally reduce sidelobes while maintaining nominal resolution and signal estimation performance. Throughout this work, re-iterative minimum-mean square error (RMMSE) adaptive and least-squares (LS) optimal mismatch processing are proposed for enhanced signal estimation in unison with adaptive interference mitigation for various radar applications including random pulse repetition interval (PRI) staggering pulse-Doppler radar, airborne ground moving target indication, and radar & communication spectrum sharing. Mismatch processing and adaptive interference cancellation each can be computationally complex for practical implementation. Sub-optimal RMMSE and LS approaches are also introduced to address computational limitations. The efficacy of these algorithms are presented using various high-fidelity Monte Carlo simulations and open-air experimental datasets. 

Quantum computing is a promising technology that can potentially demonstrate supremacy over classical computing in solving specific problems. At present, two critical challenges for quantum computing are quantum state decoherence, and low scalability of current quantum devices. Decoherence places constraints on realistic applicability of quantum algorithms as real-life applications usually require complex equivalent quantum circuits to be realized. For example, encoding classical data on quantum computers for solving I/O and data-intensive applications generally requires quantum circuits that violate decoherence constraints. In addition, current quantum devices are of small-scale having low quantum bit(qubit) counts, and often producing inaccurate or noisy measurements, which also impacts the realistic applicability of real-world quantum algorithms. Consequently, benchmarking of existing quantum algorithms and investigation of new applications are heavily dependent on classical simulations that use costly, resource-intensive computing platforms. Hardware-based emulation has been alternatively proposed as a more cost-effective and power-efficient approach. This work proposes a hardware-based emulation methodology for quantum algorithms, using cost-effective Field-Programmable Gate-Array(FPGA) technology. The proposed methodology consists of three components that are required for complete emulation of quantum algorithms; the first component models classical-to-quantum(C2Q) data encoding, the second emulates the behavior of quantum algorithms, and the third models the process of measuring the quantum state and extracting classical information, i.e., quantum-to-classical(Q2C) data decoding. The proposed emulation methodology is used to investigate and optimize methods for C2Q/Q2C data encoding/decoding, as well as several important quantum algorithms such as Quantum Fourier Transform(QFT), Quantum Haar Transform(QHT), and Quantum Grover’s Search(QGS). This work delivers contributions in terms of reducing complexities of quantum circuits, extending and optimizing quantum algorithms, and developing new quantum applications. For higher emulation performance and scalability of the framework, hardware design techniques and hardware architectural optimizations are investigated and proposed. The emulation architectures are designed and implemented on a high-performance-reconfigurable-computer(HPRC), and proposed quantum circuits are implemented on a state-of-the-art quantum processor. Experimental results show that the proposed hardware architectures enable emulation of quantum algorithms with higher scalability, higher accuracy, and higher throughput, compared to existing hardware-based emulators. As a case study, quantum image processing using multi-spectral images is considered for the experimental evaluations. 

Multi-core, integrated CPU-GPU embedded systems provide new capabilities for sophisticated real-time systems with size, weight, and power limitations; however, interference between shared resources remains a challenge in providing necessary performance guarantees. The shared main memory is a notable system bottleneck - causing throughput slowdowns and timing unpredictability.

In this paper, we propose a full system mechanism which can provide memory bandwidth regulation across both CPU and the GPU complexes. This system monitors the memory controller accesses directly through hardware statistics counters, performs memory regulation at the software level for real-time CPU tasks, and incorporates a feedback-based throttling mechanism for non-critical GPU kernels using hardware within the NVIDIA Tegra X1 memory controller subsystem. The system is built as a loadable Linux kernel module that extends the MemGuard tool. We show that this system can make CPU task execution more predictable against co-running, memory intensive interference on either CPU or GPU.

Remote attestation is a technology for establishing trust in a remote computing system.  Core to the integrity of the attestation mechanisms themselves are components that orchestrate, cryptographically bundle, and appraise measurements of the target system.  Copland is a domain-specific language for specifying attestation protocols that operate in diverse, layered measurement topologies.  In this work we formally define and verify the Copland Compiler and Copland Virtual Machine for executing Copland protocols to produce evidence.  Appraisal is a dual un-bundling procedure over the raw evidence segments produced by arbitrary Copland-based attestations.  All artifacts are implemented as monadic, functional programs in the Coq proof assistant and verified with respect to a Copland reference semantics that characterizes attestation-relevant event traces and cryptographic evidence shapes.  Appraisal soundness is positioned within a novel end-to-end workflow that leverages formal properties of the attestation components to discharge assumptions about honest Copland participants.  These assumptions inform an existing model-finder tool that analyzes a Copland scenario in the context of an active adversary attempting to subvert attestation.  An initial case study exercises this workflow through the iterative design and analysis of a Copland protocol and accompanying security architecture for an Unmanned Air Vehicle DARPA demonstration platform.  We conclude by instantiating a more diverse benchmark of attestation patterns called the “Flexible Mechanisms for Remote Attestation”, leveraging Coq's built-in code synthesis to integrate the formal artifacts within an executable attestation environment.

Multimedia networking is the area of study associated with the delivery of heterogeneous data including, but not limited to, imagery, video, audio, and interactive content. Multimedia and communication network researchers have continually struggled to devise solutions for addressing the three core challenges in multimedia delivery: security, reliability, and performance. Solutions to these challenges typically exist in a spectrum of compromises achieving gains in one aspect at the cost of one or more of the others. Networked videogames represent the pinnacle of multimedia challenges presented in a real-time, delay-sensitive, interactive format. Continual improvements to multimedia delivery have led to tools such as buffering, redundant coupling of low-resolution alternative data streams, congestion avoidance, and forced in-order delivery of best-effort service; however, videogames cannot afford to pay the latency tax of these solutions in their current state.



Practical assessments of contemporary videogame networking applications have confirmed security and performance flaws existing in well-funded, top-tier videogame titles.  This dissertation addresses these challenges through the application of a novel networking protocol, leveraging emerging blockchain technology to provide security, reliability, and performance gains to distributed network applications. This work provides a comprehensive overview of contemporary networking approaches used in delivering videogame multimedia content and their associated shortcomings. Additionally, key elements of blockchain technology are identified as focal points for solution development, notably the application of distributed ledger technology, consensus mechanisms, and smart contracts.  We conducted empirical evaluations of a network video game using both traditional TCP and UDP sockets compared with a modified video game sending state updates via hyperledger fabric channels. Reliability and security were substantially improved with no significant impact on performance.



The broader impact of this research is the improvement of real-time delivery for interactive multimedia content. This has wide-reaching effects across multiple industries including entertainment streaming, virtual conferencing, video games, manufacturing, financial transactions, and autonomous systems.

The Internet of Things platforms have been widely developed to better assist users to design, control, and monitor their smart home system. These platforms provide a programming interface and allows users to install a variety of IoT apps that published by third-party. As users could obtain the IoT apps from unvetted sources, a malicious app could be installed to perform unexpected behaviors that violating users’ security and safety, such as open the door when no motion detected. Additionally, prior research shows that due to the lack of access control mechanisms, even the benign IoT apps can cause severe security and safety risks by interact with each other in unanticipated ways. To address such threats, an improved access control system is needed to detect and monitor unexpected behaviors from IoT apps. In this paper, we provide a dynamic policy enforcement system for IoT that detects IoT behaviors and defines policies based on users’ expectation. The system relies on code analysis to identify single app behaviors and discover all potential cross-app interactions with configured devices. Discovered behaviors are displayed to users through app user interface and allow users to specify policy rules to restrict unwanted behaviors. Code instrumentation will be applied to guard apps actions and collect apps information at runtime. A policy enforcement module in the system will collect and enforce users specified policies at runtime by block actions that violate the policy. We implement the system with benign and malicious apps on SmartThings platform and shows that our system can effectively identify cross-app interactions and correctly enforce policy violations.

Auctioning of frequency bands to support growing demand for high bandwidth 5G communications is driving research into spectral cohabitation strategies for next generation radar systems. The loss of radio frequency (RF) spectrum once designated for radar operation is forcing radar systems to either learn how to coexist in these frequency spectrum bands, without causing mutual interference, or move to other bands of the spectrum, the latter being the more undesirable choice. Two methods of spectral cohabitation are proposed and presented in this work, each taking advantage of recent developments in random FM (RFM) waveforms, which have the advantage of never repeating. RFM waveforms are optimized to have favorable radar waveform properties while also readily incorporating agile spectral notches. The first method of spectral cohabitation uses these spectral notches to avoid narrow-band RF interference (RFI) in the form of other spectrum users residing in the same band as the radar system, allowing both to operate while minimizing mutual interference. The second method of spectral cohabitation uses spectral notches, along with an optimization procedure, to embed a communications signal into a dual-purpose radar/communications emission, allowing one waveform to serve both functions simultaneously. Preliminary simulation and open-air experimental results are shown which attest to the efficacy of these two methods of spectral cohabitation. Improvements are proposed to extend the capabilities of each method such that they can provide further utility to both radar and communications functions while minimizing any mutually included performance degradation.

The growth of IoT apps poses increasing concerns on sensitive data leaks. While privacy policies are required to describe how IoT apps use private user data (i.e., data practice), problems such as missing, inaccurate, and inconsistent policies have been repeatedly reported. Therefore, it is important to assess the actual data practice in IoT apps and identify the potential gaps between the actual data usage and the declared usages in the apps' privacy policies. In this work, we propose a framework called IoTPrivComp, which applies automated privacy policy and app code analysis of the IoT apps, to study the compliance gaps in IoT app practices and app privacy policies. We have collected 1,737 IoT apps from Play Store, and found that only 1,323 of them have English privacy policies available. We used IoTPrivComp to examine 411 apps that contain sensitive external data flows, and found compliance gaps in 312 (75.9%) of them. In addition, there are apps that do not have a privacy policy at all, while there is a significant number of apps that have undisclosed, inaccurately disclosed, and contradictorily disclosed data leaks. Out of the 43 data flows that involve health and wellness data, 34 (79.1%) flows were inconsistent with the disclosed practices in the app privacy policies.

Spectrum sensing and transmit waveform frequency notching is a form of cognitive radar that seeks to reduce mutual interference with other spectrum users in the same band. With the reality of increasing radio frequency (RF) spectral congestion, radar systems capable of dynamic spectrum sharing are needed. The cognitive sense-and-notch (SAN) emission strategy has recently been experimentally demonstrated as an effective way in which to reduce the interference a spectrum-sharing radar causes to other in-band users. The case of modifying transmit waveform frequency notch locations when another spectrum user moves in frequency during the radar's coherent processing interval is considered here. The physical radar emission is based on a recent random FM waveform possessing attributes that are inherently robust to sidelobes that otherwise arise for spectral notching. To contend with dynamic interference the transmit notch may be required to move during the coherent processing interval (CPI), which introduces a nonstationarity effect that results in increased residual clutter after cancellation. Here a new approach to compensate for this nonstationarity is proposed that borrows the missing portion of the clutter (due to notching) from another pulsed response for which the notch is in a different location.