Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 373 for “"Anomaly detection"”.
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Structural Anomaly Detection
… increases dramatically. Traditional intrusion detection systems that rely on log analysis struggle to keep pace with these evolving threats, as the attacking trails are often buried in high-volume and high-velocity legitimate activities in the system. Despite tremendous progress in applying …
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Active graph anomaly detection
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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Anomaly detection of time series.
This thesis deals with the problem of anomaly detection for time series data. Some of the important applications of time series anomaly detection are healthcare, eco-system disturbances, intrusion detection and aircraft system health management. Although there has been extensive work on anomaly …
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Ensemble Methods for Anomaly Detection
<p>Anomaly detection has many applications in numerous areas such as intrusion detection, fraud detection, and medical diagnosis. Most current techniques are specialized for detecting one type of anomaly, and work well on specific domains and when the data satisfies specific assumptions. </p> <p>We …
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Rank Based Anomaly Detection Algorithms
<p>Anomaly or outlier detection problems are of considerable importance, arising frequently in diverse real-world applications such as finance and cyber-security. Several algorithms have been formulated for such problems, usually based on formulating a problem-dependent heuristic or distance …
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Closed-loop network anomaly detection
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms
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Anomaly detection using network metadata
Networks are traditionally configured manually by operators who can potentially introduce misconfigurations, exposing the network to security risks. Furthermore, as network complexity grows it becomes harder to track anomalous activity in networks, especially for configuration changes which may go …
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Anomaly Detection for Control Centers
… injection attacks are known to evade bad data detection. Due to the limitations with bad data detection at the control center, a lot of approaches have been explored especially in the cyber layer to detect measurement-based attacks. Though helpful, these approaches do not look at the physical …
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An Axiomatic Perspective on Anomaly Detection
… unsupervised learning tasks, such as clustering, anomaly detection or generative modeling, is the inherent lack of quantifiable objectives. Choosing methods and evaluating outcomes is then often a matter of ad-hoc heuristics or personal taste. Anomaly detection is often employed as a preprocessing …
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Deep Neural Network for Anomaly Detection
… for cyberattacks. To safeguard these CPSs, anomaly detection (AD) that detects potential attacks/adversarial behaviors plays a pivotal role. This thesis aims to design novel deep neural models to handle four challenges of the AD problem to deal with new/unknown attacks, imbalanced data, the …
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Robust Anomaly Detection in Critical Infrastructure
… tools, including Machine Learning (ML)-based Anomaly Detection Systems (ADSs). These detection systems use ML models to learn the profile of the normal behaviour of a CI and classify deviations that go well beyond the normality profile as anomalies. However, ML methods are vulnerable to both …
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Nonparametric Anomaly Detection and Secure Communication
… security challenges in information systems are detection of anomalous data patterns that reflect malicious intrusions into data storage systems and protection of data from malicious eavesdropping during data transmissions. The first problem typically involves design of statistical tests to …
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Anomaly Detection for Mobile Device Comfort
… apply the "time slice" notion to existing anomaly detection methods, evaluate our approach on two published data sets, and confirm that it is feasible to use our approach on smartphones with modest hardware.Our work is part of Marsh et al.'s Device Comfort paradigm, which is an application …
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Sequential anomaly detection under sampling constraints
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01
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Dynamic anomaly detection in sensor networks
In the problem of quickest change detection, a sequence of random variables is observed sequentially by a decision maker. At some unknown time instant, the emergence of an anomaly leads to a change in the distribution of the observations. The goal in quickest change detection is to detect this …
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Anomaly detection for environmental noise monitoring
… human activities. Directly applying well-known anomaly detection algorithms including one-class support vector machine, replicator neural network, and principal component analysis based anomaly detection shows low performance in the collected data because these standard algorithms are unable to …
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Anomaly Detection in Database Operating System
… been fully explored with respect to real-time anomaly detection. To that end, Nectar Network (NN) was developed on top of DBOS as a public web application to generate real-world traffic and provenance data. In this thesis, I present a machine learning (ML) model to label anomalous provenance …
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Non-intrusive anomaly detection for encrypted networks
The use of encryption is steadily increasing. Packet payloads that are encrypted are becoming increasingly difficult to analyze using IDSs. This investigation uses a new non-intrusive IDS approach to detect network intrusions using a K-Means clustering methodology. It was found that this approach …
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Formulating test oracles via anomaly detection techniques
… approach to build an automated test oracle using anomaly detection techniques (based on semi-supervised and unsupervised learning approaches) on dynamic execution data (test input/output pairs and execution traces).;Firstly, anomaly detection techniques based on semi-supervised learning approach …
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