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.
Results
Showing 1 to 20 of 1146 for “"anomaly"”.
-
Structural Anomaly Detection
… in applying machine learning techniques to anomaly-based intrusion detection, such methods continue to suffer from a high false positive rate due to the diversity and variability of individual behavior.To address this problem, this thesis proposes a new framework for detecting structural …
-
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
-
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 …
-
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 …
-
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 …
-
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
-
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 …
-
Anomaly Detection for Control Centers
… at the physical layer. This study proposes an anomaly detection system for the control center that operates on the laws of physics. The system also identifies the specific falsified measurement and proposes its estimated measurement value.
-
The total asset growth anomaly: is it incremental to the net operating asset growth anomaly?
I find that the total asset (TA) growth anomaly (Cooper et al. 2008) is a noisy manifestation of the net operating asset (NOA) growth anomaly documented earlier in the accounting literature. To better understand the underlying causes of the growth anomalies, I decompose TA growth into NOA growth …
-
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 …
-
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 …
-
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 …
-
Nonparametric Anomaly Detection and Secure Communication
… theoretically provable security, i.e., anomaly detection with vanishing probability of error and guaranteed secure communication with vanishing leakage rate at eavesdroppers.</p> <p>First, the anomaly detection problem is investigated, in which typical and anomalous patterns (i.e., …
-
Law Enforcement’s Social Media Punitive Anomaly
<p>This research is a descriptive study of the misuse of social media in law enforcement from 2011 to present. The research will use a content analysis of social media policies coupled with survey of 10 questions administered anonymously to students at the Department of Criminal Justice Training. …
-
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 …
-
Sequential anomaly detection under sampling constraints
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01
-
Dynamic anomaly detection in sensor networks
… 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 change as quickly as possible, subject to constraints on the frequency of false alarm events. One important application of the …
-
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 …
-
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 …
Page 1 of 58