Defense Notices

Scanning hosts on the internet for vulnerable devices and services is a key step in numerous cyberattacks. Previous work has shown that scanning is a widespread phenomenon on the internet and commonly targets web application/server deployments. Given that automated scanning is a crucial step in many cyberattacks, it would be beneficial to make it more difficult for adversaries to perform such activity.

In this work, we propose Web-Armour, a mitigation approach to adversarial reconnaissance and vulnerability scanning of web deployments. The proposed approach relies on injecting scanning impeding delays to infrequently or rarely used portions of a web deployment. Web-Armour has two goals: First, increase the cost for attackers to perform automated reconnaissance and vulnerability scanning; Second, introduce minimal to negligible performance overhead to benign users of the deployment. We evaluate Web-Armour on live environments, operated by real users, and on different controlled (offline) scenarios. We show that Web-Armour can effectively lead to thwarting reconnaissance and internet-wide scanning.

Afghanistan deployed Biometric Voter Verification (BVV) technology nationally for the first time in the 2019 presidential election to address the systematic frauds in the prior elections. Through semi-structure interviews with 18 key national and international stakeholders who had an active role in this election, this study investigates the gap between intended outcomes of the BVV technology—focused on voter enfranchisement, fraud prevention, and public trust—and the reality on election day and beyond within the unique socio-political and technical landscape of Afghanistan.

Our findings reveal that while BVV technology initially promised a secure and transparent election, various technical and implementation challenges emerged, including threats for voters, staff, and officials. We found that the BVVs both supported and violated electoral goals: while they helped reduce fraud, they inadvertently disenfranchised some voters and caused delays that affected public trust. Technical limitations, usability issues, and administrative misalignments contributed to these outcomes. This study recommends critical lessons for future implementations of electoral technologies, emphasizing the importance of context-aware technological solutions and the need for robust administrative and technical frameworks to fully realize the potential benefits of election technology in fragile democracies.

Although the Center for Remote Sensing of Ice Sheets (CReSIS) performs several radar calibration steps to produce Operation IceBridge (OIB) radar depth sounder data products, these datasets are not radiometrically calibrated and the swath array processing uses ideal (rather than measured [calibrated]) steering vectors. Any errors in the steering vectors, which describe the response of the radar as a function of arrival angle, will lead to errors in positioning and backscatter that subsequently affect estimates of basal conditions, ice thickness, and radar attenuation. Scientific applications that estimate physical characteristics of surface and subsurface targets from the backscatter are limited with the current data because it is not absolutely calibrated. Moreover, changes in instrument hardware and processing methods for OIB over the last decade affect the quality of inter-seasonal comparisons. Recent methods which interpret basal conditions and calculate radar attenuation using CReSIS OIB 2D radar depth sounder echograms are forced to use relative scattering power, rather than absolute methods.

As an active target calibration is not possible for past field seasons, a method that uses natural targets will be developed. Unsaturated natural target returns from smooth sea-ice leads or lakes are imaged in many datasets and have known scattering responses. The proposed method forms a system of linear equations with the recorded scattering signatures from these known targets, scattering signatures from crossing flight paths, and the radiometric correction terms. A least squares solution to optimize the radiometric correction terms is calculated, which minimizes the error function representing the mismatch in expected and measured scattering. The new correction terms will be used to correct the remaining mission data. The radar depth sounder data from all OIB campaigns can be reprocessed to produce absolutely calibrated echograms for the Arctic and Antarctic. A software simulator will be developed to study calibration errors and verify the calibration software. The software for processing natural targets and crossovers will be made available in CReSIS’s open-source polar radar software toolbox. The OIB data will be reprocessed with new calibration terms, providing to the data user community a complete set of radiometrically calibrated radar echograms for the CReSIS OIB radar depth sounder for the first time.

The fundamental task of a radar system is to utilize the electromagnetic spectrum to sense a scattering environment and generate some estimate from this measurement. This task can be posed as a Bayesian estimation problem of random parameters (the scattering environment) through an imperfect sensor (the radar system). From this viewpoint, metrics such as error covariance and estimator precision (or information) can be leveraged to evaluate and improve the performance of radar systems. Here, physically realizable radar waveforms are designed to maximize the Fisher information (FI) (specifically, a derivative of FI known as marginal Fisher information (MFI)) extracted from a scattering environment thereby minimizing the expected error covariance about an estimation parameter space. This information theoretic framework, along with the high-degree of design flexibility afforded by fully digital transmitter and receiver architectures, creates a high-dimensionality design space for optimizing radar performance.

First, the problem of joint-domain range-Doppler estimation utilizing a pulse-agile radar is posed from an estimation theoretic framework, and the minimum mean square error (MMSE) estimator is shown to suppress the range-sidelobe modulation (RSM) induced by pulse agility which may improve the signal-to-interference-plus-noise ratio (SINR) in signal-limited scenarios. A computationally efficient implementation of the range-Doppler MMSE estimator is developed as a series of range-profile estimation problems, under specific modeling and statistical assumptions. Next, a transformation of the estimation parameterization is introduced which ameliorates the high noise-gain typically associated with traditional MMSE estimation by sacrificing the super-resolution achieved by the MMSE estimator. Then, coordinate descent and gradient descent optimization methods are developed for designing MFI optimal waveforms with respect to either the original or transformed estimation space. These MFI optimal waveforms are extended to provide pulse-agility, which produces high-dimensionality radar emissions amenable to non-traditional receive processing techniques (such as MMSE estimation). Finally, informationally optimal waveform design and optimal estimation are extended into a cognitive radar concept capable of adaptive and dynamic sensing. The efficacy of the MFI waveform design and MMSE estimation are demonstrated via open-air hardware experimentation where their performance is compared against traditional techniques

Radar systems perform 3 basic tasks: search/detection, tracking, and imaging. Traditionally, varied operational and hardware requirements have compartmentalized these functions to distinct and specialized radars, which may communicate actionable information between them. Expedited by the growth in computational capabilities modeled by Moore’s law, next-generation radars will be sophisticated, multi-function systems comprising generalized and reprogrammable subsystems. The advance of fully Digital Array Radars (DAR) has enabled the implementation of highly directive phased arrays that can scan, detect, and track scatterers through a volume-of-interest. Conversely, DAR technology has also enabled Multiple-Input Multiple-Output (MIMO) radar methodologies that seek to illuminate all space on transmit, while forming separate but simultaneous, directive beams on receive.

Waveform diversity has been repeatedly proven to enhance radar operation through added Degrees-of-Freedom (DoF) that can be leveraged to expand dynamic range, provide ambiguity resolution, and improve parameter estimation.  In particular, diversity among the DAR’s transmitting elements provides flexibility to the emission, allowing simultaneous multi-function capability. By precise design of the emission, the DAR can utilize the operationally-continuous trade-space between a fully coherent phased array and a fully incoherent MIMO system. This flexibility could enable the optimal management of the radar’s resources, where Signal-to-Noise Ratio (SNR) would be traded for robustness in detection, measurement capability, and tracking.

Waveform diversity is herein leveraged as the predominant enabling technology for multi-function radar emission design. Three methods of emission optimization are considered to design distinct beams in space and frequency, according to classical error minimization techniques. First, a gradient-based optimization of the Space-Frequency Template Error (SFTE) is applied to a high-fidelity model for a wideband array’s far-field emission. Second, a more efficient optimization is considered, based on the SFTE for narrowband arrays. Finally, a suboptimal solution, based on alternating projections, is shown to provide rapidly reconfigurable transmit patterns. To improve the dynamic range observed for MIMO radars employing pulse-agile quasi-orthogonal waveforms, a pulse-compression model is derived that manages to suppress both autocorrelation sidelobes and multi-transmitter-induced cross-correlation. The proposed waveforms and filters are implemented in hardware to demonstrate performance, validate robustness, and reflect real-world application to the degree possible with laboratory experimentation.

Lidar systems are gaining popularity due to their benefits, including high resolution, precise accuracy and scalability. An FMCW lidar based on self-homodyne coherent detection technique is used for range and velocity measurement with a phase diverse coherent receiver. The system employs a self-homodyne detection technique, where a LO signal is derived directly from the same laser source as the transmitted signal and is the same linear chirp as the transmitted signal, thereby minimizing phase noise. A coherent receiver is employed to get in-phase and quadrature components of the photocurrent and to perform de-chirping. Since the LO has the same chirp as the transmitted signal, the mixing process in the photodiodes effectively cancels out the chirp or frequency modulation from the received signal. The spectrum of the de-chirped complex waveform is used to determine the range and velocity of the target. This lidar system simplifies the signal processing by using photodetectors for de-chirping. Additionally, after de-chirping, the resulting signal has a much narrower bandwidth compared to the original chirp signal and signal processing can be performed at lower frequencies.

Runtime attestation is a way to gain confidence in the current state of a remote target. 

Layered attestation is a way of extending that confidence from one component to another. 

Introspective solutions for layered attestation require strict isolation. 

The seL4 is uniquely well-suited to offer kernel properties sufficient to achieve such isolation. 

I design, implement, and evaluate introspective measurements and the layered runtime attestation of a Linux kernel hosted by the seL4. 

VERIAL can detect diamorphine-style rootkits with performance cost comparable to previous work. 

The accelerated melting of ice sheets in Greenland and Antarctica, driven by climate warming, is significantly contributing to global sea level rise. To better understand this phenomenon, airborne radars have been deployed to create echogram images that map snow accumulation patterns in these regions. Utilizing advanced radar systems developed by the Center for Remote Sensing and Integrated Systems (CReSIS), around 1.5 petabytes of climate data have been collected. However, extracting ice-related information, such as accumulation rates, remains limited due to the largely manual and time-consuming process of tracking internal layers in radar echograms. This highlights the need for automated solutions.

Machine learning and deep learning algorithms are well-suited for this task, given their near-human performance on optical images. The overlap between classical radar signal processing and machine learning techniques suggests that combining concepts from both fields could lead to optimized solutions.

In this work, we developed custom deep learning algorithms for automatic layer tracking (both supervised and self-supervised) to address the challenge of limited annotated data and achieve accurate tracking of radiostratigraphic layers in echograms. We introduce an iterative multi-class classification algorithm, termed “Row Block,” which sequentially tracks internal layers from the top to the bottom of an echogram based on the surface location. This approach was used in an active learning framework to expand the labeled dataset. We also developed deep learning segmentation algorithms by framing the echogram layer tracking problem as a binary segmentation task, followed by post-processing to generate vector-layer annotations using a connected-component 1-D layer-contour extractor.

Additionally, we aimed to provide the deep learning and scientific communities with a large, fully annotated dataset. This was achieved by synchronizing radar data with outputs from a regional climate model, creating what are currently the two largest machine-learning-ready Snow Radar datasets available, with 10,000 and 50,000 echograms, respectively.

The artificially intelligent Dietician Web application is an innovative piece of technology that makes use of artificial intelligence to offer individualised nutritional guidance and assistance. This web application uses advanced machine learning algorithms and natural language processing to provide users with individualized nutritional advice and assistance in meal planning. Users who are interested in improving their eating habits can benefit from this bot. The system collects relevant data about users' dietary choices, as well as information about calories, and provides insights into body mass index (BMI) and basal metabolic rate (BMR) through interactive conversations, resulting in tailored recommendations. To enhance its capacity for prediction, a number of classification methods, including naive Bayes, neural networks, random forests, and support vector machines, were utilised and evaluated. Following an exhaustive analysis, the model that proved to be the most effective random forest is selected for the purpose of incorporating it into the development of the artificial intelligence Dietician Web application. The purpose of this study is to emphasise the significance of the artificial intelligence Dietician Web application as a versatile and intelligent instrument that encourages the adoption of healthy eating habits and empowers users to make intelligent decisions regarding their dietary requirements.

Data science is revolutionizing the world of sports by uncovering hidden patterns and providing profound insights that enhance performance, strategy, and decision-making. This project, "Understanding Soccer Through Data Science," exemplifies the transformative power of data analytics in sports. By leveraging Graph Neural Networks (GNNs), this project delves deep into the intricate passing dynamics within soccer teams. 

A key innovation of this project is the development of a novel metric called PassNetScore, which aims to contextualize and provide meaningful insights into passing networks—a popular application of graph network theory in soccer. Utilizing the Statsbomb Event Data, which captures every event during a soccer match, including passes, shots, fouls, and substitutions, this project constructs detailed passing network graphs. Each player is represented as a node, and each pass as an edge, creating a comprehensive representation of team interactions on the pitch. The project harnesses the power of Spektral, a Python library for graph deep learning, to build and analyze these graphs. Key node features include players' average positions, total passes and expected threat of passes, while edges encapsulate the passing interactions and pass counts. 

The project explores two distinct models to calculate PassNetScore through predicting match outcomes. The first model is a basic GNN that employs a binary adjacency matrix to represent the presence or absence of passes between players. This model captures the fundamental structure of passing networks, highlighting key players and connections within the team. There are three variations of this model, each building on the binary model by adding new features to nodes or edges. The second model integrates GNN with Long Short-Term Memory (LSTM) networks to account for temporal dependencies in passing sequences. This advanced model provides deeper insights into how passing patterns evolve over time and how these dynamics impact match outcomes. To evaluate the effectiveness of these models, a suite of graph theory metrics is employed. These metrics illuminate the dynamics of team play and the influence of individual players, offering a comprehensive assessment of the PassNet Score metric. 

Through this innovative approach, the project demonstrates the powerful application of GNNs in sports analytics and offers a novel metric for evaluating passing networks based on match outcomes. This project paves the way for new strategies and insights that could revolutionize how teams analyze and improve their gameplay, showcasing the profound impact of data science in sports.