Partial group delay is a spectral parameter, which measures the time lag between two time series in a system after the spurious effects of the other series in the system have been eliminated. For weakly-stationary processes, estimators for partial group delay are proposed based on indirect and direct approaches. Conditions for weak consistency and asymptotic normality of the proposed estimators are obtained. Applications to a multiple test of partial group delay are investigated. The time lag interpretation of partial group delay is justified, which provides insight into the nature of linear relationships among weakly-stationary processes. Extensions are made to group delay estimation and partial group delay estimation for non-stationary "oscillatory" processes. / Ph. D.

In the case of repetitive experiments of a similar type, where the parameters vary randomly from experiment to experiment, the Empirical Bayes method often leads to estimators which have smaller mean squared errors than the classical estimators. Suppose there is an unobservable random variable θ, where θ ~ G(θ), usually called a prior distribution. The Bayes estimator of θ cannot be obtained in general unless G(θ) is known. In the empirical Bayes method we do not assume that G(θ) is known, but the sequence of past estimates is used to estimate θ. This dissertation involves the empirical Bayes estimates of various time series parameters: The autoregressive model, moving average model, mixed autoregressive-moving average, regression with time series errors, regression with unobservable variables, serial correlation, multiple time series and spectral density function. In each case, empirical Bayes estimators are obtained using the asymptotic distributions of the usual estimators. By Monte Carlo simulation the empirical Bayes estimator of first order autoregressive parameter, ρ, was shown to have smaller mean squared errors than the conditional maximum likelihood estimator for 11 past experiences. / Doctor of Philosophy

Earth observation satellites (EOS) such as Landsat provide image datasets that can be immensely useful in numerous application domains. One way of analyzing satellite images for land use and land cover change (LULCC) is time series analysis (TSA). Several algorithms for time series analysis have been proposed by various groups in remote sensing; more algorithms (that can be adapted) are available in the general time series literature. However, in spite of an abundance of algorithms, the choice of algorithm to be used for analyzing an image stack is presently an open question. A concurrent issue is the prohibitive size of Landsat datasets, currently of the order of petabytes and growing. This makes them computationally unwieldy --- both in storage and processing. An EOS image stack typically consists of multiple images of a fixed area on the Earth's surface (same latitudes and longitudes) taken at different time points. Experiments on multicore servers indicate that carrying out meaningful time series analysis on one such interannual, multitemporal stack with existing state of the art codes can take several days. This work proposes using multiple algorithms to analyze a given image stack in a polyalgorithmic framework. A polyalgorithm combines several basic algorithms, each meant to solve the same problem, producing a strategy that unites the strengths and circumvents the weaknesses of constituent algorithms. The foundation of the proposed TSA based polyalgorithm is laid using three algorithms (LandTrendR, EWMACD, and BFAST). These algorithms are precisely described mathematically, and chosen to be fundamentally distinct from each other in design and in the phenomena they capture. Analysis of results representing success, failure, and parameter sensitivity for each algorithm is presented. Scalability issues, important for real simulations, are also discussed, along with scalable implementations, and speedup results. For a given pixel, Hausdorff distance is used to compare the distance between the change times (breakpoints) obtained from two different algorithms. Timesync validation data, a dataset that is based on human interpretation of Landsat time series in concert with historical aerial photography, is used for validation. The polyalgorithm yields more accurate results than EWMACD and LandTrendR alone, but counterintuitively not better than BFAST alone. This nascent work will be directly useful in land use and land cover change studies, of interest to terrestrial science research, especially regarding anthropogenic impacts on the environment, and in much broader applications such as health monitoring and urban transportation. / M. S. / Numerous manmade satellites circling around the Earth regularly take pictures (images) of the Earth’s surface from up above. These images naturally provide information regarding the land cover of any given piece of land at the moment of capture (for e.g., whether the land area in the picture is covered with forests or with agriculture or housing). Therefore, for a fixed land area, if a person looks at a chronologically arranged series of images, any significant changes in land use can be identified. Identifying such changes is of critical importance, especially in this era where deforestation, urbanization, and global warming are major concerns. The goal of this thesis is to investigate the design of methodologies (algorithms) that can efficiently and accurately use satellite images for answering questions regarding land cover trend and change. Experience shows that the state-of-the-art methodologies produce great results for the region they were originally designed on but their performance on other regions is unpredictable. In this work, therefore, a ‘polyalgorithm’ is proposed. A ‘polyalgorithm’ utilizes multiple simple methodologies and strategically combines them so that the outcome is better than the individual components. In this introductory work, three component methodologies are utilized; each component methodology is capable of capturing phenomenon different from the other two. Mathematical formulation of each component methodology is presented. Initial strategy for combining the three component algorithms is proposed. The outcomes of each component methodology as well the polyalgorithm are tested on human interpreted data. The strengths and limitations of each methodology are also discussed. Efficiency of the codes used for implementing the polyalgorithm is also discussed; this is important because the satellite data that needs to be processed is known to be huge (petabytes sized already and growing). This nascent work will be directly useful especially in understanding the impact of human activities on the environment. It will also be useful in other applications such as health monitoring and urban transportation.

Abstract—This dissertation investigates the privacy implications of WiFi communication in Internet-of-Things (IoT) environments, focusing on the threat posed by out-of-network observers. Recent research has shown that in-network observers can glean information about IoT devices, user identities, and activities. However, the potential for information inference by out-of-network observers, who do not have WiFi network access, has not been thoroughly examined. The first study provides a detailed summary dataset, utilizing Random Forest for data summary classification. This study highlights the significant privacy threat to WiFi networks and IoT applications from out-of-network observers. Building on this investigation, the second study extends the research by utilizing a new set of time series monitored WiFi data frames and advanced machine learning algorithms, specifically xGboost, for Time Series classification. This extension achieved high accuracy of up to 94\% in identifying IoT devices and their working status, demonstrating faster IoT device profiling while maintaining classification accuracy. Furthermore, the study underscores the ease with which outside intruders can harm IoT devices without joining a WiFi network, launching attacks quickly and leaving no detectable footprints. Additionally, the dissertation presents a comprehensive survey of recent advancements in machine-learning-driven encrypted traffic analysis and classification. Given the challenges posed by encryption for traditional packet and traffic inspection, understanding and classifying encrypted traffic are crucial. The survey provides insights into utilizing machine learning for encrypted network traffic analysis and classification, reviewing state-of-the-art techniques and methodologies. This survey serves as a valuable resource for network administrators, cybersecurity professionals, and policy enforcement entities, offering insights into current practices and future directions in encrypted traffic analysis and classification.

The U.S. Coast Guard maintains a network structure to connect its nation-wide assets. This paper analyzes and models four highly aggregate traces of the traffic to/from the Coast Guard Data Network ship-shore nodes, so that the models may be used to predict future system demand. These internet traces (polled at 5â 40â intervals) are shown to adhere to a Gaussian distribution upon detrending, which imposes limits to the exponential distribution of higher time-resolution traces. Wavelet estimation of the Hurst-parameter is shown to outperform estimation by another common method (Sample-Variances). The First Differences method of detrending proved problematic to this analysis and is shown to decorrelate AR(1) processes where 0.65< phi1 <1.35 and correlate AR(1) processes with phi1 <-0.25. The Hannan-Rissanen method for estimating (phi,theta) is employed to analyze this series and a one-step ahead forecast is generated. / Master of Science

Assessing Anonymized System Logs Usefulness for Behavioral Analysis in RNN Models Tom Richard Vargis1,∗, Siavash Ghiasvand1,2 1Technische Universität Dresden, Germany 2Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, Germany Abstract System logs are a common source of monitoring data for analyzing computing systems behavior. Due to the complexity of modern computing systems and the large size of collected monitoring data, automated analysis mechanisms are required. Numerous machine learning and deep learning methods are proposed to address this challenge. However, due to the existence of sensitive data in system logs their analysis and storage raise serious privacy concerns. Anonymization methods could be used to cleanse the monitoring data before analysis. However, anonymized system logs in general do not provide an adequate usefulness for majority of behavioral analysis. Content-aware anonymization mechanisms such as 𝑃𝛼𝑅𝑆 preserve the correlation of system logs even after anonymization. This work evaluates the usefulness of anonymized system logs of Taurus HPC cluster anonymized using 𝑃𝛼𝑅𝑆, for behavioural analysis via recurrent neural network models. To facilitate the reproducibility and further development of this work, the implemented prototype and monitoring data are publicly available [12].

This thesis presents and evaluates a new cascading machine learning model framework for anomaly detection, which are essential for modern industrial applications where computing efficiency is crucial. Traditional deep learning algorithms frequently struggle to effectively deploy in edge computing due to the limitations of processing power and memory. This study addresses the challenge by creating a cascading model framework that strategically combines lightweight and more complex models to improve the efficiency of inference while maintaining the accuracy of detection.  We proposed a cascading model framework consisting of a One-Class Support Vector Machine (OCSVM) for rapid initial anomaly detection and a Variational Autoencoder (VAE) for more precise prediction in uncertain cases. The cascading technique between the OCSVM and VAE enables the system to efficiently handle regular data instances, while assigning more complex analyses only when required. This framework was tested in real-world scenarios, including anomaly detection in air pressure system of automotive industry as well as with the MNIST datasets. These tests demonstrate the framework's practical applicability and effectiveness across diverse settings, underscoring its potential for broad implementation in industrial applications.

Anomalies are rare in nature. This rarity makes it difficult for models to provide accurate and reliable predictions. Deep learning models typically excel at identifying underlying patterns from abundant data through supervised learning mechanisms but struggle with anomalies due to their limited representation. This results in a significant portion of errors arising from these rare and poorly represented events. Here, we present various methods and frameworks to develop the specialized ability of models to better detect and predict anomalies. Additionally, we improve the interpretability of these models by enhancing their anomaly awareness, leading to stronger performance on real-world datasets that often contain such anomalies. Because our models dynamically adapt to the significance of anomalies, they benefit from increased accuracy and prioritization of rare events in predictions. We demonstrate such capabilities on real-world datasets across multiple domains. Our results show that this framework enhances accuracy and interpretability, improving upon existing methods in anomaly prediction tasks.

This thesis investigates out-of-distribution recognition for time-series data of pulsedradar signals. The classifier is a naive Bayesian classifier based on Gaussian mixturemodels and Dirichlet process mixture models. In the mixture models, we model thedistribution of three pulse features in the time series, namely radio-frequency in thepulse, duration of the pulse, and pulse repetition interval which is the time betweenpulses. We found that simple thresholds on the likelihood can effectively determine ifsamples are out-of-distribution or belong to one of the classes trained on. In addition,we present a simple method that can be used for deinterleaving/pulse classification andshow that it can robustly classify 100 interleaved signals and simultaneously determineif pulses are out-of-distribution. / Det här examensarbetet undersöker hur en maskininlärnings-modell kan anpassas för attkänna igen när pulserade radar-signaler inte tillhör samma fördelning som modellen är tränadmed men också känna igen om signalen tillhör en tidigare känd klass. Klassifieringsmodellensom används här är en naiv Bayesiansk klassifierare som använder sig av Gaussian mixturemodels och Dirichlet Process mixture models. Modellen skapar en fördelning av tidsseriedatan för pulserade radar-signaler och specifikt för frekvensen av varje puls, pulsens längd och tiden till nästa puls. Genom att sätta gränser i sannolikheten av varje puls eller sannolikhetenav en sekvens kan vi känna igen om datan är okänd eller tillhör en tidigare känd klass.Vi presenterar även en enkel metod för att klassifiera specifika pulser i sammanhang närflera signaler överlappar och att metoden kan användas för att robust avgöra om pulser ärokända.

published_or_final_version / Community Medicine / Master / Master of Public Health