DeTorrent: An Adversarial Padding-only Traffic Analysis Defense

While anonymity networks like Tor aim to protect the privacy of their users, they are vulnerable to traffic analysis attacks such as Website Fingerprinting (WF) and Flow Correlation (FC). Recent implementations of WF and FC attacks, such as Tik-Tok and DeepCoFFEA, have shown that the attacks can be effectively carried out, threatening user privacy. Consequently, there is a need for effective traffic analysis defense. There are a variety of existing defenses, but most are either ineffective, incur high latency and bandwidth overhead, or require additional infrastructure. As a result, we aim to design a traffic analysis defense that is efficient and highly resistant to both WF and FC attacks. We propose DeTorrent, which uses competing neural networks to generate and evaluate traffic analysis defenses that insert 'dummy' traffic into real traffic flows. DeTorrent operates with moderate overhead and without delaying traffic. In a closed-world WF setting, it reduces an attacker's accuracy by 61.5%, a reduction 10.5% better than the next-best padding-only defense. Against the state-of-the-art FC attacker, DeTorrent reduces the true positive rate for a $10^{-5}$ false positive rate to about .12, which is less than half that of the next-best defense. We also demonstrate DeTorrent's practicality by deploying it alongside the Tor network and find that it maintains its performance when applied to live traffic.Comment: Accepted to the 24th Privacy Enhancing Technologies Symposium (PETS 2024

An Investigation of Turbulence and Diffusion within Vehicle Wakes and On-Road Measurements using an Instrumented Mobile Car and a Stationary Roadside Monitoring System

Moving motor vehicles emit pollutants that negatively impact human health. Stationary roadside measurements alone are not sufficient to quantify the pollutant–flow interactions that occur behind moving vehicles. The instrumented mobile car however is well–suited for on–road measurements, but has been underutilized for this purpose since limited studies have investigated its accuracy at high vehicle speeds. Thus, this work details two on–road measurement campaigns using an instrumented car, with three main objectives: (1) study the vehicle momentum wake and vehicle–induced turbulence (VIT), (2) investigate the accuracy of the mobile system for measuring atmospheric means, variances and covariances, and (3) quantify the emission of aerosols and CO2 by on–road vehicles and their subsequent diffusion. Measurements behind on–road vehicles demonstrate that VIT decays with increasing distance following a power law relationship. Comparison of measurements with prior on–road studies suggests a height dependence of VIT in vehicle wakes, and an extended parameterization is outlined that describes the total on–road turbulent kinetic energy (TKE) enhancement due to a composition of vehicles, including a vertical dependence on the magnitude of TKE. Next, a wavelet–based approach to remove the effects of sporadic passing traffic is developed and applied to a measurement period during which a heavy–duty truck passes in the opposite highway lane; removing the times with traffic in this measurement period gives a 10% reduction in the TKE. When sampling uncertainties are considered, the vertical momentum flux measured on the car is found to be not different from roadside measurements in the 95% confidence interval. The first on–road and in–traffic measurements of the vertical turbulent particle number flux and the vertical turbulent CO2 flux are presented and the results suggest this technique could be further developed to measure individual vehicle emission rates while driving. The lateral width of the wake generated by each passing vehicle is estimated using the stationary roadside measurements, and is determined to be a factor of 5 times greater for heavy–duty trucks relative to sport utility vehicles and passenger cars at a distance of 150 m behind the vehicle

An Investigation of Turbulence and Diffusion within Vehicle Wakes and On-Road Measurements using an Instrumented Mobile Car and a Stationary Roadside Monitoring System

Moving motor vehicles emit pollutants that negatively impact human health. Stationary roadside measurements alone are not sufficient to quantify the pollutantflow interactions that occur behind moving vehicles. The instrumented mobile car however is wellsuited for onroad measurements, but has been underutilized for this purpose since limited studies have investigated its accuracy at high vehicle speeds. Thus, this work details two onroad measurement campaigns using an instrumented car, with three main objectives: (1) study the vehicle momentum wake and vehicleinduced turbulence (VIT), (2) investigate the accuracy of the mobile system for measuring atmospheric means, variances and covariances, and (3) quantify the emission of aerosols and CO2 by onroad vehicles and their subsequent diffusion. Measurements behind onroad vehicles demonstrate that VIT decays with increasing distance following a power law relationship. Comparison of measurements with prior onroad studies suggests a height dependence of VIT in vehicle wakes, and an extended parameterization is outlined that describes the total onroad turbulent kinetic energy (TKE) enhancement due to a composition of vehicles, including a vertical dependence on the magnitude of TKE. Next, a waveletbased approach to remove the effects of sporadic passing traffic is developed and applied to a measurement period during which a heavyduty truck passes in the opposite highway lane; removing the times with traffic in this measurement period gives a 10% reduction in the TKE. When sampling uncertainties are considered, the vertical momentum flux measured on the car is found to be not different from roadside measurements in the 95% confidence interval. The first onroad and intraffic measurements of the vertical turbulent particle number flux and the vertical turbulent CO2 flux are presented and the results suggest this technique could be further developed to measure individual vehicle emission rates while driving. The lateral width of the wake generated by each passing vehicle is estimated using the stationary roadside measurements, and is determined to be a factor of 5 times greater for heavyduty trucks relative to sport utility vehicles and passenger cars at a distance of 150 m behind the vehicle

Concept of Operations for Integrating Commercial Supersonic Transport Aircraft into the National Airspace System

Several businesses and government agencies, including the National Aeronautics and Space Administration are currently working on solving key technological barriers that must be overcome in order to realize the vision of low-boom supersonic flights conducted over land. However, once these challenges are met, the manner in which this class of aircraft is integrated in the National Airspace System may become a potential constraint due to the significant environmental, efficiency, and economic repercussions that their integration may cause. This document was developed to create a path for research and development that exposes the benefits and barriers of seamlessly integrating a class of CSTs into the NAS, while also serving as a Concept of Operations (ConOps) which posits a mid- to far-term solution (2025-2035) concept for best integrating CST into the NAS. Background research regarding historic supersonic operations in the National Airspace System, assumptions about design aspects and equipage of commercial supersonic transport (CST) aircraft, assumptions concerning the operational environment are described in this document. Results of a simulation experiment to investigate the interactions between CST aircraft and modern-day air traffic are disseminated and are used to generate scenarios for CST operations. Finally, technology needs to realize these operational scenarios are discussed

Analytics over Encrypted Traffic and Defenses

Encrypted traffic flows have been known to leak information about their underlying content through statistical properties such as packet lengths and timing. While traffic fingerprinting attacks exploit such information leaks and threaten user privacy by disclosing website visits, videos streamed, and user activity on messaging platforms, they can also be helpful in network management and intelligence services. Most recent and best-performing such attacks are based on deep learning models. In this thesis, we identify multiple limitations in the currently available attacks and defenses against them. First, these deep learning models do not provide any insights into their decision-making process. Second, most attacks that have achieved very high accuracies are still limited by unrealistic assumptions that affect their practicality. For example, most attacks assume a closed world setting and focus on traffic classification after event completion. Finally, current state-of-the-art defenses still incur high overheads to provide reasonable privacy, which limits their applicability in real-world applications. In order to address these limitations, we first propose an inline traffic fingerprinting attack based on variable-length sequence modeling to facilitate real-time analytics. Next, we attempt to understand the inner workings of deep learning-based attacks with the dual goals of further improving attacks and designing efficient defenses against such attacks. Then, based on the observations from this analysis, we propose two novel defenses against traffic fingerprinting attacks that provide privacy under more realistic constraints and at lower bandwidth overheads. Finally, we propose a robust framework for open set classification that targets network traffic with this added advantage of being more suitable for deployment in resource-constrained in-network devices

Recruitment Strategies In Human Sympathetic Nerve Activity

The overall objectives of the current dissertation were to 1) establish the neural coding principles employed by the sympathetic nervous system (SNS) in response to acute physiological stress; and 2) to determine the various mechanisms of control underlying these sympathetic neural recruitment strategies. This research tested the working hypothesis that efferent post-ganglionic muscle sympathetic nerve activity exhibits neural coding patterns reflecting increased firing of lower-threshold axons, recruitment of latent sub-populations of higher-threshold axons, as well as malleable synaptic delays, and further, that these strategies are governed by factors such as reflex-specificity, stress severity, perception of effort or stress, age, and cardiovascular disease. Specifically, we utilized a novel signal processing approach to study sympathetic action potential discharge patterning during periods of acute reflex-mediated sympathoexcitation. Overall, these studies support the working hypothesis and confirm that neural coding principles operate within the SNS. Specifically, in response to acute homeostatic perturbation, the SNS has options to increase the firing rate of already-active, lower-threshold axons, recruit sub-populations of previously silent (i.e., not present at baseline), larger-sized and faster conducting sympathetic axons, as well as modify acutely synaptic delays. Study 1 demonstrated that the ability to recruit latent neural sub-populations represents a fixed, reflex-independent recruitment strategy, as this pattern was observed during chemoreflex- and baroreflex-mediated sympathoexcitation. In turn, this option appears reserved for severe stress scenarios. Furthermore, study 2 suggests that central, perceptual features may play a specific role in modifying the synaptic delay aspect of efferent discharge timing, whereas peripheral-reflex mechanisms mediate the recruitment of latent axons. Study 3 demonstrates that, while the ability to acutely modify synaptic delays appears preserved, the ability to increase firing frequency of already-active axons, and importantly, the capacity of the SNS to recruit latent sub-populations of higher-threshold axons are reduced with healthy aging and perhaps lost altogether with cardiovascular disease. Finally, study 4 suggests that the lack of ventilation itself, rather than the ever-increasing chemical drive, mediates the robust sympathetic neural recruitment observed during apnea. In conclusion, the series of studies contained herein confirm the presence of neural coding patterns in human efferent post-ganglionic sympathetic nerve activity

Prediction of Airport Arrival Rates Using Data Mining Methods

This research sought to establish and utilize relationships between environmental variable inputs and airport efficiency estimates by data mining archived weather and airport performance data at ten geographically and climatologically different airports. Several meaningful relationships were discovered using various statistical modeling methods within an overarching data mining protocol and the developed models were tested using historical data. Additionally, a selected model was deployed using real-time predictive weather information to estimate airport efficiency as a demonstration of potential operational usefulness. This work employed SAS® Enterprise Miner TM data mining and modeling software to train and validate decision tree, neural network, and linear regression models to estimate the importance of weather input variables in predicting Airport Arrival Rates (AAR) using the FAA’s Aviation System Performance Metric (ASPM) database. The ASPM database contains airport performance statistics and limited weather variables archived at 15-minute and hourly intervals, and these data formed the foundation of this study. In order to add more weather parameters into the data mining environment, National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) meteorological hourly station data were merged with the ASPM data to increase the number of environmental variables (e.g., precipitation type and amount) into the analyses. Using the SAS® Enterprise Miner TM, three different types of models were created, compared, and scored at the following ten airports: a) Hartsfield-Jackson Atlanta International Airport (ATL), b) Los Angeles International Airport (LAX), c) O’Hare International Airport (ORD), d) Dallas/Fort Worth International Airport (DFW), e) John F. Kennedy International Airport (JFK), f) Denver International Airport (DEN), g) San Francisco International Airport (SFO), h) Charlotte-Douglas International Airport (CLT), i) LaGuardia Airport (LGA), and j) Newark Liberty International Airport (EWR). At each location, weather inputs were used to estimate AARs as a metric of efficiency easily interpreted by FAA airspace managers. To estimate Airport Arrival Rates, three data sets were used: a) 15-minute and b) hourly ASPM data, along with c) a merged ASPM and meteorological hourly station data set. For all three data sets, the models were trained and validated using data from 2014 and 2015, and then tested using 2016 data. Additionally, a selected airport model was deployed using National Weather Service (NWS) Localized Aviation MOS (Model Output Statistics) Program (LAMP) weather guidance as the input variables over a 24-hour period as a test. The resulting AAR output predictions were then compared with the real-world AARs observed. Based on model scoring using 2016 data, LAX, ATL, and EWR demonstrated useful predictive performance that potentially could be applied to estimate real-world AARs. Marginal, but perhaps useful AAR prediction might be gleaned operationally at LGA, SFO, and DFW, as the number of successfully scored cases fall loosely within one standard deviation of acceptable model performance arbitrarily set at ten percent of the airport’s maximum AAR. The remaining models studied, DEN, CLT, ORD, and JFK appeared to have little useful operational application based on the 2016 model scoring results

Exploratory search through large video corpora

Activity retrieval is a growing field in electrical engineering that specializes in the search and retrieval of relevant activities and events in video corpora. With the affordability and popularity of cameras for government, personal and retail use, the quantity of available video data is rapidly outscaling our ability to reason over it. Towards the end of empowering users to navigate and interact with the contents of these video corpora, we propose a framework for exploratory search that emphasizes activity structure and search space reduction over complex feature representations. Exploratory search is a user driven process wherein a person provides a system with a query describing the activity, event, or object he is interested in finding. Typically, this description takes the implicit form of one or more exemplar videos, but it can also involve an explicit description. The system returns candidate matches, followed by query refinement and iteration. System performance is judged by the run-time of the system and the precision/recall curve of of the query matches returned. Scaling is one of the primary challenges in video search. From vast web-video archives like youtube (1 billion videos and counting) to the 30 million active surveillance cameras shooting an estimated 4 billion hours of footage every week in the United States, trying to find a set of matches can be like looking for a needle in a haystack. Our goal is to create an efficient archival representation of video corpora that can be calculated in real-time as video streams in, and then enables a user to quickly get a set of results that match. First, we design a system for rapidly identifying simple queries in large-scale video corpora. Instead of focusing on feature design, our system focuses on the spatiotemporal relationships between those features as a means of disambiguating an activity of interest from background. We define a semantic feature vocabulary of concepts that are both readily extracted from video and easily understood by an operator. As data streams in, features are hashed to an inverted index and retrieved in constant time after the system is presented with a user's query. We take a zero-shot approach to exploratory search: the user manually assembles vocabulary elements like color, speed, size and type into a graph. Given that information, we perform an initial downsampling of the archived data, and design a novel dynamic programming approach based on genome-sequencing to search for similar patterns. Experimental results indicate that this approach outperforms other methods for detecting activities in surveillance video datasets. Second, we address the problem of representing complex activities that take place over long spans of space and time. Subgraph and graph matching methods have seen limited use in exploratory search because both problems are provably NP-hard. In this work, we render these problems computationally tractable by identifying the maximally discriminative spanning tree (MDST), and using dynamic programming to optimally reduce the archive data based on a custom algorithm for tree-matching in attributed relational graphs. We demonstrate the efficacy of this approach on popular surveillance video datasets in several modalities. Finally, we design an approach for successive search space reduction in subgraph matching problems. Given a query graph and archival data, our algorithm iteratively selects spanning trees from the query graph that optimize the expected search space reduction at each step until the archive converges. We use this approach to efficiently reason over video surveillance datasets, simulated data, as well as large graphs of protein data

Statistical and deep learning methods for geoscience problems

Machine learning is the new frontier for technology development in geosciences and has developed extremely fast in the past decade. With the increased compute power provided by distributed computing and Graphics Processing Units (GPUs) and their exploitation provided by machine learning (ML) frameworks such as Keras, Pytorch, and Tensorflow, ML algorithms can now solve complex scientific problems. Although powerful, ML algorithms need to be applied to suitable problems conditioned for optimal results. For this reason ML algorithms require not only a deep understanding of the problem but also of the algorithm’s ability. In this dissertation, I show that Simple statistical techniques can often outperform ML-based models if applied correctly. In this dissertation, I show the success of deep learning in addressing two difficult problems. In the first application I use deep learning to auto-detect the leaks in a carbon capture project using pressure field data acquired from the DOE Cranfield site in Mississippi. I use the history of pressure, rates, and cumulative injection volumes to detect leaks as pressure anomaly. I use a different deep learning workflow to forecast high-energy electrons in Earth’s outer radiation belt using in situ measurements of different space weather parameters such as solar wind density and pressure. I focus on predicting electron fluxes of 2 MeV and higher energy and introduce the ensemble of deep learning models to further improve the results as compared to using a single deep learning architecture. I also show an example where a carefully constructed statistical approach, guided by the human interpreter, outperforms deep learning algorithms implemented by others. Here, the goal is to correlate multiple well logs across a survey area in order to map not only the thickness, but also to characterize the behavior of stacked gamma ray parasequence sets. Using tools including maximum likelihood estimation (MLE) and dynamic time warping (DTW) provides a means of generating quantitative maps of upward fining and upward coarsening across the oil field. The ultimate goal is to link such extensive well control with the spectral attribute signature of 3D seismic data volumes to provide a detailed maps of not only the depositional history, but also insight into lateral and vertical variation of mineralogy important to the effective completion of shale resource plays