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You searched for subject:(anomaly detection). Showing records 1 – 30 of 636 total matches.

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University of Illinois – Urbana-Champaign

1. Yu, Xiao. Filtering and refinement: a two-stage approach for efficient and effective anomaly detection.

Degree: MS, 0112, 2011, University of Illinois – Urbana-Champaign

Anomaly detection is an important data mining task. Most existing methods treat anomalies as inconsistencies and spend the majority amount of time on modeling normal… (more)

Subjects/Keywords: anomaly detection

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APA (6th Edition):

Yu, X. (2011). Filtering and refinement: a two-stage approach for efficient and effective anomaly detection. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/24511

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Yu, Xiao. “Filtering and refinement: a two-stage approach for efficient and effective anomaly detection.” 2011. Thesis, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/24511.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Yu, Xiao. “Filtering and refinement: a two-stage approach for efficient and effective anomaly detection.” 2011. Web. 18 Apr 2021.

Vancouver:

Yu X. Filtering and refinement: a two-stage approach for efficient and effective anomaly detection. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2011. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/24511.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yu X. Filtering and refinement: a two-stage approach for efficient and effective anomaly detection. [Thesis]. University of Illinois – Urbana-Champaign; 2011. Available from: http://hdl.handle.net/2142/24511

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

2. Pukkawanna, Sirikarn. Unsupervised Anomaly Detection in Massive Traffic Using S-transform and Renyi Divergence : S変換とRenyiダイバージェンスを用いた大規模トラフィックにおける教師なし異常検知; S ヘンカン ト Renyi ダイバージェンス オ モチイタ ダイキボ トラフィック ニ オケル キョウシ ナシ イジョウ ケンチ.

Degree: 博士(工学), Nara Institute of Science and Technology / 奈良先端科学技術大学院大学

Subjects/Keywords: anomaly detection

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APA (6th Edition):

Pukkawanna, S. (n.d.). Unsupervised Anomaly Detection in Massive Traffic Using S-transform and Renyi Divergence : S変換とRenyiダイバージェンスを用いた大規模トラフィックにおける教師なし異常検知; S ヘンカン ト Renyi ダイバージェンス オ モチイタ ダイキボ トラフィック ニ オケル キョウシ ナシ イジョウ ケンチ. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/10079

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Pukkawanna, Sirikarn. “Unsupervised Anomaly Detection in Massive Traffic Using S-transform and Renyi Divergence : S変換とRenyiダイバージェンスを用いた大規模トラフィックにおける教師なし異常検知; S ヘンカン ト Renyi ダイバージェンス オ モチイタ ダイキボ トラフィック ニ オケル キョウシ ナシ イジョウ ケンチ.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed April 18, 2021. http://hdl.handle.net/10061/10079.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Pukkawanna, Sirikarn. “Unsupervised Anomaly Detection in Massive Traffic Using S-transform and Renyi Divergence : S変換とRenyiダイバージェンスを用いた大規模トラフィックにおける教師なし異常検知; S ヘンカン ト Renyi ダイバージェンス オ モチイタ ダイキボ トラフィック ニ オケル キョウシ ナシ イジョウ ケンチ.” Web. 18 Apr 2021.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Pukkawanna S. Unsupervised Anomaly Detection in Massive Traffic Using S-transform and Renyi Divergence : S変換とRenyiダイバージェンスを用いた大規模トラフィックにおける教師なし異常検知; S ヘンカン ト Renyi ダイバージェンス オ モチイタ ダイキボ トラフィック ニ オケル キョウシ ナシ イジョウ ケンチ. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2021 Apr 18]. Available from: http://hdl.handle.net/10061/10079.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

Pukkawanna S. Unsupervised Anomaly Detection in Massive Traffic Using S-transform and Renyi Divergence : S変換とRenyiダイバージェンスを用いた大規模トラフィックにおける教師なし異常検知; S ヘンカン ト Renyi ダイバージェンス オ モチイタ ダイキボ トラフィック ニ オケル キョウシ ナシ イジョウ ケンチ. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/10079

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.


University of Alberta

3. Mueller, David A. Time Series Discords.

Degree: MS, Department of Computing Science, 2013, University of Alberta

 Time series discords, as introduced in by Keogh et al. [5] is described as the subsequence in the time series which is maximally different from… (more)

Subjects/Keywords: Anomaly Detection; Discord; Time Series

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APA (6th Edition):

Mueller, D. A. (2013). Time Series Discords. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/g445cd805

Chicago Manual of Style (16th Edition):

Mueller, David A. “Time Series Discords.” 2013. Masters Thesis, University of Alberta. Accessed April 18, 2021. https://era.library.ualberta.ca/files/g445cd805.

MLA Handbook (7th Edition):

Mueller, David A. “Time Series Discords.” 2013. Web. 18 Apr 2021.

Vancouver:

Mueller DA. Time Series Discords. [Internet] [Masters thesis]. University of Alberta; 2013. [cited 2021 Apr 18]. Available from: https://era.library.ualberta.ca/files/g445cd805.

Council of Science Editors:

Mueller DA. Time Series Discords. [Masters Thesis]. University of Alberta; 2013. Available from: https://era.library.ualberta.ca/files/g445cd805


Colorado State University

4. Siegel, Barry W. Spatiotemporal anomaly detection: streaming architecture and algorithms.

Degree: PhD, Systems Engineering, 2020, Colorado State University

Anomaly detection is the science of identifying one or more rare or unexplainable samples or events in a dataset or data stream. The field of… (more)

Subjects/Keywords: artificial intelligence; anomaly detection

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APA (6th Edition):

Siegel, B. W. (2020). Spatiotemporal anomaly detection: streaming architecture and algorithms. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/211792

Chicago Manual of Style (16th Edition):

Siegel, Barry W. “Spatiotemporal anomaly detection: streaming architecture and algorithms.” 2020. Doctoral Dissertation, Colorado State University. Accessed April 18, 2021. http://hdl.handle.net/10217/211792.

MLA Handbook (7th Edition):

Siegel, Barry W. “Spatiotemporal anomaly detection: streaming architecture and algorithms.” 2020. Web. 18 Apr 2021.

Vancouver:

Siegel BW. Spatiotemporal anomaly detection: streaming architecture and algorithms. [Internet] [Doctoral dissertation]. Colorado State University; 2020. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/10217/211792.

Council of Science Editors:

Siegel BW. Spatiotemporal anomaly detection: streaming architecture and algorithms. [Doctoral Dissertation]. Colorado State University; 2020. Available from: http://hdl.handle.net/10217/211792


Royal Holloway, University of London

5. Smith, James. The efficiency of conformal predictors for anomaly detection.

Degree: PhD, 2016, Royal Holloway, University of London

 This thesis explores the application of conformal prediction to the anomaly detection domain. Anomaly detection is a large area of research in machine learning and… (more)

Subjects/Keywords: conformal predictors; anomaly detection

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APA (6th Edition):

Smith, J. (2016). The efficiency of conformal predictors for anomaly detection. (Doctoral Dissertation). Royal Holloway, University of London. Retrieved from https://pure.royalholloway.ac.uk/portal/en/publications/the-efficiency-of-conformal-predictors-for-anomaly-detection(d68cdec7-7d84-414b-9498-0c8539bb57b8).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.792583

Chicago Manual of Style (16th Edition):

Smith, James. “The efficiency of conformal predictors for anomaly detection.” 2016. Doctoral Dissertation, Royal Holloway, University of London. Accessed April 18, 2021. https://pure.royalholloway.ac.uk/portal/en/publications/the-efficiency-of-conformal-predictors-for-anomaly-detection(d68cdec7-7d84-414b-9498-0c8539bb57b8).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.792583.

MLA Handbook (7th Edition):

Smith, James. “The efficiency of conformal predictors for anomaly detection.” 2016. Web. 18 Apr 2021.

Vancouver:

Smith J. The efficiency of conformal predictors for anomaly detection. [Internet] [Doctoral dissertation]. Royal Holloway, University of London; 2016. [cited 2021 Apr 18]. Available from: https://pure.royalholloway.ac.uk/portal/en/publications/the-efficiency-of-conformal-predictors-for-anomaly-detection(d68cdec7-7d84-414b-9498-0c8539bb57b8).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.792583.

Council of Science Editors:

Smith J. The efficiency of conformal predictors for anomaly detection. [Doctoral Dissertation]. Royal Holloway, University of London; 2016. Available from: https://pure.royalholloway.ac.uk/portal/en/publications/the-efficiency-of-conformal-predictors-for-anomaly-detection(d68cdec7-7d84-414b-9498-0c8539bb57b8).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.792583


Uppsala University

6. Blomquist, Hanna. Anomaly detection with Machine learning : Quality assurance of statistical data in the Aid community.

Degree: Computing Science, 2015, Uppsala University

  The overall purpose of this study was to find a way to identify incorrect data in Sida’s statistics about their contributions. A contribution is… (more)

Subjects/Keywords: Anomaly detection; Machine learning

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APA (6th Edition):

Blomquist, H. (2015). Anomaly detection with Machine learning : Quality assurance of statistical data in the Aid community. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-260380

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Blomquist, Hanna. “Anomaly detection with Machine learning : Quality assurance of statistical data in the Aid community.” 2015. Thesis, Uppsala University. Accessed April 18, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-260380.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Blomquist, Hanna. “Anomaly detection with Machine learning : Quality assurance of statistical data in the Aid community.” 2015. Web. 18 Apr 2021.

Vancouver:

Blomquist H. Anomaly detection with Machine learning : Quality assurance of statistical data in the Aid community. [Internet] [Thesis]. Uppsala University; 2015. [cited 2021 Apr 18]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-260380.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Blomquist H. Anomaly detection with Machine learning : Quality assurance of statistical data in the Aid community. [Thesis]. Uppsala University; 2015. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-260380

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Delft University of Technology

7. Anastasakis, Michael (author). Application-level Network Behavior Analysis and Anomaly Detection using Density Based Clustering.

Degree: 2017, Delft University of Technology

Nowadays, organization networks are facing an increased number of different attacks and existing intrusion and anomaly detection systems fail to keep up. By focusing on… (more)

Subjects/Keywords: clustering; anomaly detection; density clustering

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APA (6th Edition):

Anastasakis, M. (. (2017). Application-level Network Behavior Analysis and Anomaly Detection using Density Based Clustering. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:a332673e-0fab-499e-ae08-cf6ff5ce8197

Chicago Manual of Style (16th Edition):

Anastasakis, Michael (author). “Application-level Network Behavior Analysis and Anomaly Detection using Density Based Clustering.” 2017. Masters Thesis, Delft University of Technology. Accessed April 18, 2021. http://resolver.tudelft.nl/uuid:a332673e-0fab-499e-ae08-cf6ff5ce8197.

MLA Handbook (7th Edition):

Anastasakis, Michael (author). “Application-level Network Behavior Analysis and Anomaly Detection using Density Based Clustering.” 2017. Web. 18 Apr 2021.

Vancouver:

Anastasakis M(. Application-level Network Behavior Analysis and Anomaly Detection using Density Based Clustering. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Apr 18]. Available from: http://resolver.tudelft.nl/uuid:a332673e-0fab-499e-ae08-cf6ff5ce8197.

Council of Science Editors:

Anastasakis M(. Application-level Network Behavior Analysis and Anomaly Detection using Density Based Clustering. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:a332673e-0fab-499e-ae08-cf6ff5ce8197


Delft University of Technology

8. MANGANAHALLI JAYAPRAKASH, Sandesh (author). Behaviour Modelling and Anomaly Detection in Smart-Home IoT Devices.

Degree: 2019, Delft University of Technology

The usage of Internet of Things (IoT) devices has been exponentially increasing and their security is often overlooked. Hackers exploit the vulnerabilities present to perform… (more)

Subjects/Keywords: Anomaly Detection; state machines; IoT

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APA (6th Edition):

MANGANAHALLI JAYAPRAKASH, S. (. (2019). Behaviour Modelling and Anomaly Detection in Smart-Home IoT Devices. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9f226a82-a1bc-4e91-a2c1-73122d227ac5

Chicago Manual of Style (16th Edition):

MANGANAHALLI JAYAPRAKASH, Sandesh (author). “Behaviour Modelling and Anomaly Detection in Smart-Home IoT Devices.” 2019. Masters Thesis, Delft University of Technology. Accessed April 18, 2021. http://resolver.tudelft.nl/uuid:9f226a82-a1bc-4e91-a2c1-73122d227ac5.

MLA Handbook (7th Edition):

MANGANAHALLI JAYAPRAKASH, Sandesh (author). “Behaviour Modelling and Anomaly Detection in Smart-Home IoT Devices.” 2019. Web. 18 Apr 2021.

Vancouver:

MANGANAHALLI JAYAPRAKASH S(. Behaviour Modelling and Anomaly Detection in Smart-Home IoT Devices. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Apr 18]. Available from: http://resolver.tudelft.nl/uuid:9f226a82-a1bc-4e91-a2c1-73122d227ac5.

Council of Science Editors:

MANGANAHALLI JAYAPRAKASH S(. Behaviour Modelling and Anomaly Detection in Smart-Home IoT Devices. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:9f226a82-a1bc-4e91-a2c1-73122d227ac5


University of Florida

9. Lyons, Princess P. Anomaly and Target Detection in Synthetic Aperture SONAR.

Degree: MS, Electrical and Computer Engineering, 2019, University of Florida

 Automated anomaly and target detection are commonly used as a prescreening step within a larger target detection and target classification framework to find regions of… (more)

Subjects/Keywords: anomaly  – detection  – machine-learning  – target

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APA (6th Edition):

Lyons, P. P. (2019). Anomaly and Target Detection in Synthetic Aperture SONAR. (Masters Thesis). University of Florida. Retrieved from https://ufdc.ufl.edu/UFE0056170

Chicago Manual of Style (16th Edition):

Lyons, Princess P. “Anomaly and Target Detection in Synthetic Aperture SONAR.” 2019. Masters Thesis, University of Florida. Accessed April 18, 2021. https://ufdc.ufl.edu/UFE0056170.

MLA Handbook (7th Edition):

Lyons, Princess P. “Anomaly and Target Detection in Synthetic Aperture SONAR.” 2019. Web. 18 Apr 2021.

Vancouver:

Lyons PP. Anomaly and Target Detection in Synthetic Aperture SONAR. [Internet] [Masters thesis]. University of Florida; 2019. [cited 2021 Apr 18]. Available from: https://ufdc.ufl.edu/UFE0056170.

Council of Science Editors:

Lyons PP. Anomaly and Target Detection in Synthetic Aperture SONAR. [Masters Thesis]. University of Florida; 2019. Available from: https://ufdc.ufl.edu/UFE0056170


University of Toronto

10. Saeed, Ghanbari. Semantic-aware Anomaly Detection in Distributed Storage Systems.

Degree: 2012, University of Toronto

As distributed storage systems become central to business operations, increasing their reliability becomes an essential requirement. Towards this goal, distributed storage systems come equipped with… (more)

Subjects/Keywords: Storage Systems; Anomaly Detection; 0984

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APA (6th Edition):

Saeed, G. (2012). Semantic-aware Anomaly Detection in Distributed Storage Systems. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/69046

Chicago Manual of Style (16th Edition):

Saeed, Ghanbari. “Semantic-aware Anomaly Detection in Distributed Storage Systems.” 2012. Doctoral Dissertation, University of Toronto. Accessed April 18, 2021. http://hdl.handle.net/1807/69046.

MLA Handbook (7th Edition):

Saeed, Ghanbari. “Semantic-aware Anomaly Detection in Distributed Storage Systems.” 2012. Web. 18 Apr 2021.

Vancouver:

Saeed G. Semantic-aware Anomaly Detection in Distributed Storage Systems. [Internet] [Doctoral dissertation]. University of Toronto; 2012. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1807/69046.

Council of Science Editors:

Saeed G. Semantic-aware Anomaly Detection in Distributed Storage Systems. [Doctoral Dissertation]. University of Toronto; 2012. Available from: http://hdl.handle.net/1807/69046


University of Waterloo

11. Zhao, Qiang. Auto-Encoder based Deep Representation Model for Image Anomaly Detection.

Degree: 2020, University of Waterloo

 Image anomaly detection is to distinguish a small portion of images that are different from the user-defined normal ones. In this work, we focus on… (more)

Subjects/Keywords: auto-encoder; anomaly detection

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zhao, Q. (2020). Auto-Encoder based Deep Representation Model for Image Anomaly Detection. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/16204

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Zhao, Qiang. “Auto-Encoder based Deep Representation Model for Image Anomaly Detection.” 2020. Thesis, University of Waterloo. Accessed April 18, 2021. http://hdl.handle.net/10012/16204.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Zhao, Qiang. “Auto-Encoder based Deep Representation Model for Image Anomaly Detection.” 2020. Web. 18 Apr 2021.

Vancouver:

Zhao Q. Auto-Encoder based Deep Representation Model for Image Anomaly Detection. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/10012/16204.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhao Q. Auto-Encoder based Deep Representation Model for Image Anomaly Detection. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/16204

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Sydney

12. Toth, Edward. Detecting Group Deviations .

Degree: 2018, University of Sydney

 Pointwise anomaly detection and change detection focus on the study of individual data instances however group deviation research involves groups or collections of observations. Data… (more)

Subjects/Keywords: group; anomaly; detection; deviation

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APA (6th Edition):

Toth, E. (2018). Detecting Group Deviations . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/20713

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Toth, Edward. “Detecting Group Deviations .” 2018. Thesis, University of Sydney. Accessed April 18, 2021. http://hdl.handle.net/2123/20713.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Toth, Edward. “Detecting Group Deviations .” 2018. Web. 18 Apr 2021.

Vancouver:

Toth E. Detecting Group Deviations . [Internet] [Thesis]. University of Sydney; 2018. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2123/20713.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Toth E. Detecting Group Deviations . [Thesis]. University of Sydney; 2018. Available from: http://hdl.handle.net/2123/20713

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Sydney

13. Raghavendra, Chalapathy. Deep Learning for Anomaly Detection .

Degree: 2019, University of Sydney

Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. Anomaly detection or outlier detection is an unsupervised… (more)

Subjects/Keywords: outlier; anomaly detection; deep autoencoder

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APA (6th Edition):

Raghavendra, C. (2019). Deep Learning for Anomaly Detection . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/20853

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Raghavendra, Chalapathy. “Deep Learning for Anomaly Detection .” 2019. Thesis, University of Sydney. Accessed April 18, 2021. http://hdl.handle.net/2123/20853.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Raghavendra, Chalapathy. “Deep Learning for Anomaly Detection .” 2019. Web. 18 Apr 2021.

Vancouver:

Raghavendra C. Deep Learning for Anomaly Detection . [Internet] [Thesis]. University of Sydney; 2019. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2123/20853.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Raghavendra C. Deep Learning for Anomaly Detection . [Thesis]. University of Sydney; 2019. Available from: http://hdl.handle.net/2123/20853

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Colorado State University

14. Haefner, Kyle. Behavioral complexity analysis of networked systems to identify malware attacks.

Degree: PhD, Computer Science, 2020, Colorado State University

 Internet of Things (IoT) environments are often composed of a diverse set of devices that span a broad range of functionality, making them a challenge… (more)

Subjects/Keywords: cyber-security; anomaly-detection; IoT

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APA (6th Edition):

Haefner, K. (2020). Behavioral complexity analysis of networked systems to identify malware attacks. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/219593

Chicago Manual of Style (16th Edition):

Haefner, Kyle. “Behavioral complexity analysis of networked systems to identify malware attacks.” 2020. Doctoral Dissertation, Colorado State University. Accessed April 18, 2021. http://hdl.handle.net/10217/219593.

MLA Handbook (7th Edition):

Haefner, Kyle. “Behavioral complexity analysis of networked systems to identify malware attacks.” 2020. Web. 18 Apr 2021.

Vancouver:

Haefner K. Behavioral complexity analysis of networked systems to identify malware attacks. [Internet] [Doctoral dissertation]. Colorado State University; 2020. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/10217/219593.

Council of Science Editors:

Haefner K. Behavioral complexity analysis of networked systems to identify malware attacks. [Doctoral Dissertation]. Colorado State University; 2020. Available from: http://hdl.handle.net/10217/219593


Delft University of Technology

15. Sikkes, Louis (author). Performance Evaluation of Vehicle Routing Heuristics.

Degree: 2019, Delft University of Technology

 This thesis has researched the automation of performance evaluation of vehicle routing heuristics. The trade-off between solution quality, which is composed of multiple variables, and… (more)

Subjects/Keywords: heuristics; performance evaluation; anomaly detection

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Sikkes, L. (. (2019). Performance Evaluation of Vehicle Routing Heuristics. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:5c6b7284-99bc-41ab-bca5-4aeddb677b4e

Chicago Manual of Style (16th Edition):

Sikkes, Louis (author). “Performance Evaluation of Vehicle Routing Heuristics.” 2019. Masters Thesis, Delft University of Technology. Accessed April 18, 2021. http://resolver.tudelft.nl/uuid:5c6b7284-99bc-41ab-bca5-4aeddb677b4e.

MLA Handbook (7th Edition):

Sikkes, Louis (author). “Performance Evaluation of Vehicle Routing Heuristics.” 2019. Web. 18 Apr 2021.

Vancouver:

Sikkes L(. Performance Evaluation of Vehicle Routing Heuristics. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Apr 18]. Available from: http://resolver.tudelft.nl/uuid:5c6b7284-99bc-41ab-bca5-4aeddb677b4e.

Council of Science Editors:

Sikkes L(. Performance Evaluation of Vehicle Routing Heuristics. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:5c6b7284-99bc-41ab-bca5-4aeddb677b4e

16. Huang, Chengqiang. Featured anomaly detection methods and applications.

Degree: PhD, 2018, University of Exeter

Anomaly detection is a fundamental research topic that has been widely investigated. From critical industrial systems, e.g., network intrusion detection systems, to people’s daily activities,… (more)

Subjects/Keywords: 004; anomaly detection; novelty detection; data description

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APA (6th Edition):

Huang, C. (2018). Featured anomaly detection methods and applications. (Doctoral Dissertation). University of Exeter. Retrieved from http://hdl.handle.net/10871/34351

Chicago Manual of Style (16th Edition):

Huang, Chengqiang. “Featured anomaly detection methods and applications.” 2018. Doctoral Dissertation, University of Exeter. Accessed April 18, 2021. http://hdl.handle.net/10871/34351.

MLA Handbook (7th Edition):

Huang, Chengqiang. “Featured anomaly detection methods and applications.” 2018. Web. 18 Apr 2021.

Vancouver:

Huang C. Featured anomaly detection methods and applications. [Internet] [Doctoral dissertation]. University of Exeter; 2018. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/10871/34351.

Council of Science Editors:

Huang C. Featured anomaly detection methods and applications. [Doctoral Dissertation]. University of Exeter; 2018. Available from: http://hdl.handle.net/10871/34351


Delft University of Technology

17. Wijnands, K.J. (author). Using endpoints process information for malicious behavior detection.

Degree: 2015, Delft University of Technology

 In the last years the impact of malware has become a huge problem. Each year, more and more new malware samples are discovered [2]. And… (more)

Subjects/Keywords: anomaly detection; process trees; heatmaps; malware detection

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wijnands, K. J. (. (2015). Using endpoints process information for malicious behavior detection. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:e1678077-9056-47ac-82e6-2762bfb40a63

Chicago Manual of Style (16th Edition):

Wijnands, K J (author). “Using endpoints process information for malicious behavior detection.” 2015. Masters Thesis, Delft University of Technology. Accessed April 18, 2021. http://resolver.tudelft.nl/uuid:e1678077-9056-47ac-82e6-2762bfb40a63.

MLA Handbook (7th Edition):

Wijnands, K J (author). “Using endpoints process information for malicious behavior detection.” 2015. Web. 18 Apr 2021.

Vancouver:

Wijnands KJ(. Using endpoints process information for malicious behavior detection. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2021 Apr 18]. Available from: http://resolver.tudelft.nl/uuid:e1678077-9056-47ac-82e6-2762bfb40a63.

Council of Science Editors:

Wijnands KJ(. Using endpoints process information for malicious behavior detection. [Masters Thesis]. Delft University of Technology; 2015. Available from: http://resolver.tudelft.nl/uuid:e1678077-9056-47ac-82e6-2762bfb40a63


Virginia Tech

18. Shu, Xiaokui. Threat Detection in Program Execution and Data Movement: Theory and Practice.

Degree: PhD, Computer Science and Applications, 2016, Virginia Tech

 Program attacks are one of the oldest and fundamental cyber threats. They compromise the confidentiality of data, the integrity of program logic, and the availability… (more)

Subjects/Keywords: Cybersecurity; Program Anomaly Detection; Data Leak Detection

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Shu, X. (2016). Threat Detection in Program Execution and Data Movement: Theory and Practice. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/71463

Chicago Manual of Style (16th Edition):

Shu, Xiaokui. “Threat Detection in Program Execution and Data Movement: Theory and Practice.” 2016. Doctoral Dissertation, Virginia Tech. Accessed April 18, 2021. http://hdl.handle.net/10919/71463.

MLA Handbook (7th Edition):

Shu, Xiaokui. “Threat Detection in Program Execution and Data Movement: Theory and Practice.” 2016. Web. 18 Apr 2021.

Vancouver:

Shu X. Threat Detection in Program Execution and Data Movement: Theory and Practice. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/10919/71463.

Council of Science Editors:

Shu X. Threat Detection in Program Execution and Data Movement: Theory and Practice. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/71463


Delft University of Technology

19. Mahabir, Ashvant (author). Blind Graph Topology Change Detection: A Graph Signal Processing approach.

Degree: 2017, Delft University of Technology

Graphs are used to model irregular data structures and serve as models to represent/capture the interrelationships between data. The data in graphs are also referred… (more)

Subjects/Keywords: graph theory; Detection; Graphs; anomaly detection; blind

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APA (6th Edition):

Mahabir, A. (. (2017). Blind Graph Topology Change Detection: A Graph Signal Processing approach. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9b58f037-5c2a-4444-97f4-f8dacef6eb5f

Chicago Manual of Style (16th Edition):

Mahabir, Ashvant (author). “Blind Graph Topology Change Detection: A Graph Signal Processing approach.” 2017. Masters Thesis, Delft University of Technology. Accessed April 18, 2021. http://resolver.tudelft.nl/uuid:9b58f037-5c2a-4444-97f4-f8dacef6eb5f.

MLA Handbook (7th Edition):

Mahabir, Ashvant (author). “Blind Graph Topology Change Detection: A Graph Signal Processing approach.” 2017. Web. 18 Apr 2021.

Vancouver:

Mahabir A(. Blind Graph Topology Change Detection: A Graph Signal Processing approach. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Apr 18]. Available from: http://resolver.tudelft.nl/uuid:9b58f037-5c2a-4444-97f4-f8dacef6eb5f.

Council of Science Editors:

Mahabir A(. Blind Graph Topology Change Detection: A Graph Signal Processing approach. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:9b58f037-5c2a-4444-97f4-f8dacef6eb5f


Addis Ababa University

20. Yared, Hawulte. Hybrid Approach to Detect Fault Caused KPIs Anomaly in UMTS Cells .

Degree: 2020, Addis Ababa University

 Cellular networks usually suffer from failures or performance degradations due to several reasons, such as external interference, hardware/software bugs on network elements, power outages, or… (more)

Subjects/Keywords: Anomaly detection; correlational change anomaly; KPI; sudden drop anomaly; RTWP; Universal Mobile Telecommunications System (UMTS)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Yared, H. (2020). Hybrid Approach to Detect Fault Caused KPIs Anomaly in UMTS Cells . (Thesis). Addis Ababa University. Retrieved from http://etd.aau.edu.et/handle/123456789/21115

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Yared, Hawulte. “Hybrid Approach to Detect Fault Caused KPIs Anomaly in UMTS Cells .” 2020. Thesis, Addis Ababa University. Accessed April 18, 2021. http://etd.aau.edu.et/handle/123456789/21115.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Yared, Hawulte. “Hybrid Approach to Detect Fault Caused KPIs Anomaly in UMTS Cells .” 2020. Web. 18 Apr 2021.

Vancouver:

Yared H. Hybrid Approach to Detect Fault Caused KPIs Anomaly in UMTS Cells . [Internet] [Thesis]. Addis Ababa University; 2020. [cited 2021 Apr 18]. Available from: http://etd.aau.edu.et/handle/123456789/21115.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yared H. Hybrid Approach to Detect Fault Caused KPIs Anomaly in UMTS Cells . [Thesis]. Addis Ababa University; 2020. Available from: http://etd.aau.edu.et/handle/123456789/21115

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Waterloo

21. Cheng, Xi. Anomaly Detection and Fault Localization Using Runtime State Models.

Degree: 2016, University of Waterloo

 Software systems are impacting every aspect of our daily lives, making software failures expensive, even life endangering. Despite rigorous testing, software bugs inevitably exist, especially… (more)

Subjects/Keywords: Software Anomaly; Anomaly Detection; Fault Localization; Runtime States

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APA (6th Edition):

Cheng, X. (2016). Anomaly Detection and Fault Localization Using Runtime State Models. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/10519

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Cheng, Xi. “Anomaly Detection and Fault Localization Using Runtime State Models.” 2016. Thesis, University of Waterloo. Accessed April 18, 2021. http://hdl.handle.net/10012/10519.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Cheng, Xi. “Anomaly Detection and Fault Localization Using Runtime State Models.” 2016. Web. 18 Apr 2021.

Vancouver:

Cheng X. Anomaly Detection and Fault Localization Using Runtime State Models. [Internet] [Thesis]. University of Waterloo; 2016. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/10012/10519.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Cheng X. Anomaly Detection and Fault Localization Using Runtime State Models. [Thesis]. University of Waterloo; 2016. Available from: http://hdl.handle.net/10012/10519

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Carnegie Mellon University

22. Whiting, Mark E. Anomaly Classification Through Automated Shape Grammar Representation.

Degree: 2017, Carnegie Mellon University

 Statistical learning offers a trove of opportunities for problems where a large amount of data is available but falls short when data are limited. For… (more)

Subjects/Keywords: Anomaly detection; Grammar induction; Shape grammar

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APA (6th Edition):

Whiting, M. E. (2017). Anomaly Classification Through Automated Shape Grammar Representation. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/1035

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Whiting, Mark E. “Anomaly Classification Through Automated Shape Grammar Representation.” 2017. Thesis, Carnegie Mellon University. Accessed April 18, 2021. http://repository.cmu.edu/dissertations/1035.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Whiting, Mark E. “Anomaly Classification Through Automated Shape Grammar Representation.” 2017. Web. 18 Apr 2021.

Vancouver:

Whiting ME. Anomaly Classification Through Automated Shape Grammar Representation. [Internet] [Thesis]. Carnegie Mellon University; 2017. [cited 2021 Apr 18]. Available from: http://repository.cmu.edu/dissertations/1035.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Whiting ME. Anomaly Classification Through Automated Shape Grammar Representation. [Thesis]. Carnegie Mellon University; 2017. Available from: http://repository.cmu.edu/dissertations/1035

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Vanderbilt University

23. Mack, Daniel Leif Campana. Anomaly Detection from Complex Temporal Sequences in Large Data.

Degree: PhD, Computer Science, 2013, Vanderbilt University

 As systems become more complex and the amount of operational data collected from these systems increases proportionally, new challenges arise about how this data can… (more)

Subjects/Keywords: baseball; aviation safety; complexity measures; anomaly detection

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APA (6th Edition):

Mack, D. L. C. (2013). Anomaly Detection from Complex Temporal Sequences in Large Data. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/12087

Chicago Manual of Style (16th Edition):

Mack, Daniel Leif Campana. “Anomaly Detection from Complex Temporal Sequences in Large Data.” 2013. Doctoral Dissertation, Vanderbilt University. Accessed April 18, 2021. http://hdl.handle.net/1803/12087.

MLA Handbook (7th Edition):

Mack, Daniel Leif Campana. “Anomaly Detection from Complex Temporal Sequences in Large Data.” 2013. Web. 18 Apr 2021.

Vancouver:

Mack DLC. Anomaly Detection from Complex Temporal Sequences in Large Data. [Internet] [Doctoral dissertation]. Vanderbilt University; 2013. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1803/12087.

Council of Science Editors:

Mack DLC. Anomaly Detection from Complex Temporal Sequences in Large Data. [Doctoral Dissertation]. Vanderbilt University; 2013. Available from: http://hdl.handle.net/1803/12087


Texas A&M University

24. Lin, Sheng-Ya. Modeling and Detection of Content and Packet Flow Anomalies at Enterprise Network Gateway.

Degree: PhD, Computer Engineering, 2013, Texas A&M University

 This dissertation investigates modeling techniques and computing algorithms for detection of anomalous contents and traffic flows of ingress Internet traffic at an enterprise network gateway.… (more)

Subjects/Keywords: Network Anomaly Detection; Enterprise network gateway

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APA (6th Edition):

Lin, S. (2013). Modeling and Detection of Content and Packet Flow Anomalies at Enterprise Network Gateway. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/149307

Chicago Manual of Style (16th Edition):

Lin, Sheng-Ya. “Modeling and Detection of Content and Packet Flow Anomalies at Enterprise Network Gateway.” 2013. Doctoral Dissertation, Texas A&M University. Accessed April 18, 2021. http://hdl.handle.net/1969.1/149307.

MLA Handbook (7th Edition):

Lin, Sheng-Ya. “Modeling and Detection of Content and Packet Flow Anomalies at Enterprise Network Gateway.” 2013. Web. 18 Apr 2021.

Vancouver:

Lin S. Modeling and Detection of Content and Packet Flow Anomalies at Enterprise Network Gateway. [Internet] [Doctoral dissertation]. Texas A&M University; 2013. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1969.1/149307.

Council of Science Editors:

Lin S. Modeling and Detection of Content and Packet Flow Anomalies at Enterprise Network Gateway. [Doctoral Dissertation]. Texas A&M University; 2013. Available from: http://hdl.handle.net/1969.1/149307


Penn State University

25. Chen, Cheng Kai. Biomarkers Discovery Using Network Based Anomaly Detection.

Degree: 2019, Penn State University

 Identifying biomarkers is an important step in translating research advances in genomics into clinical practice. From a machine learning perspective, computational biomarker identification can be… (more)

Subjects/Keywords: Feature Selectionn; Anomaly Detection; Graph Similarity

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APA (6th Edition):

Chen, C. K. (2019). Biomarkers Discovery Using Network Based Anomaly Detection. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/16367cxc854

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Chen, Cheng Kai. “Biomarkers Discovery Using Network Based Anomaly Detection.” 2019. Thesis, Penn State University. Accessed April 18, 2021. https://submit-etda.libraries.psu.edu/catalog/16367cxc854.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Chen, Cheng Kai. “Biomarkers Discovery Using Network Based Anomaly Detection.” 2019. Web. 18 Apr 2021.

Vancouver:

Chen CK. Biomarkers Discovery Using Network Based Anomaly Detection. [Internet] [Thesis]. Penn State University; 2019. [cited 2021 Apr 18]. Available from: https://submit-etda.libraries.psu.edu/catalog/16367cxc854.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chen CK. Biomarkers Discovery Using Network Based Anomaly Detection. [Thesis]. Penn State University; 2019. Available from: https://submit-etda.libraries.psu.edu/catalog/16367cxc854

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Waikato

26. Mungro, Meenakshee. Rating the Significance of Detected Network Events .

Degree: 2014, University of Waikato

 Existing anomaly detection systems do not reliably produce accurate severity ratings for detected network events, which results in network operators wasting a large amount of… (more)

Subjects/Keywords: Data fusion; Anomaly detection; Network latency

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APA (6th Edition):

Mungro, M. (2014). Rating the Significance of Detected Network Events . (Masters Thesis). University of Waikato. Retrieved from http://hdl.handle.net/10289/8808

Chicago Manual of Style (16th Edition):

Mungro, Meenakshee. “Rating the Significance of Detected Network Events .” 2014. Masters Thesis, University of Waikato. Accessed April 18, 2021. http://hdl.handle.net/10289/8808.

MLA Handbook (7th Edition):

Mungro, Meenakshee. “Rating the Significance of Detected Network Events .” 2014. Web. 18 Apr 2021.

Vancouver:

Mungro M. Rating the Significance of Detected Network Events . [Internet] [Masters thesis]. University of Waikato; 2014. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/10289/8808.

Council of Science Editors:

Mungro M. Rating the Significance of Detected Network Events . [Masters Thesis]. University of Waikato; 2014. Available from: http://hdl.handle.net/10289/8808


University of Toronto

27. Bhattacharyya, Arnamoy. Phase Aware Performance Modeling for Cloud Workloads.

Degree: PhD, 2020, University of Toronto

 Cloud computing is gaining enormous popularity every day. But with the growing demand of cloud computing systems, comes the challenge of efficiently managing the vast… (more)

Subjects/Keywords: Anomaly Detection; Cloud Computing; Performance Modeling; 0464

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bhattacharyya, A. (2020). Phase Aware Performance Modeling for Cloud Workloads. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/101232

Chicago Manual of Style (16th Edition):

Bhattacharyya, Arnamoy. “Phase Aware Performance Modeling for Cloud Workloads.” 2020. Doctoral Dissertation, University of Toronto. Accessed April 18, 2021. http://hdl.handle.net/1807/101232.

MLA Handbook (7th Edition):

Bhattacharyya, Arnamoy. “Phase Aware Performance Modeling for Cloud Workloads.” 2020. Web. 18 Apr 2021.

Vancouver:

Bhattacharyya A. Phase Aware Performance Modeling for Cloud Workloads. [Internet] [Doctoral dissertation]. University of Toronto; 2020. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1807/101232.

Council of Science Editors:

Bhattacharyya A. Phase Aware Performance Modeling for Cloud Workloads. [Doctoral Dissertation]. University of Toronto; 2020. Available from: http://hdl.handle.net/1807/101232


Northeastern University

28. Lopez, Jose A. Anomaly detection using polynomial optimization methods.

Degree: PhD, Department of Electrical and Computer Engineering, 2016, Northeastern University

 This dissertation is concerned with the problem of detecting anomalies in the behavior of a system. This problem is encountered by most scientific communities and,… (more)

Subjects/Keywords: anomaly detection; machine learning; polynomial optimization

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APA (6th Edition):

Lopez, J. A. (2016). Anomaly detection using polynomial optimization methods. (Doctoral Dissertation). Northeastern University. Retrieved from http://hdl.handle.net/2047/D20237881

Chicago Manual of Style (16th Edition):

Lopez, Jose A. “Anomaly detection using polynomial optimization methods.” 2016. Doctoral Dissertation, Northeastern University. Accessed April 18, 2021. http://hdl.handle.net/2047/D20237881.

MLA Handbook (7th Edition):

Lopez, Jose A. “Anomaly detection using polynomial optimization methods.” 2016. Web. 18 Apr 2021.

Vancouver:

Lopez JA. Anomaly detection using polynomial optimization methods. [Internet] [Doctoral dissertation]. Northeastern University; 2016. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2047/D20237881.

Council of Science Editors:

Lopez JA. Anomaly detection using polynomial optimization methods. [Doctoral Dissertation]. Northeastern University; 2016. Available from: http://hdl.handle.net/2047/D20237881


Delft University of Technology

29. SHI, XIAOTONG (author). Anomaly detection and diagnosis in ASML event log using attentional LSTM network.

Degree: 2019, Delft University of Technology

In the ASML test system, all activity events of the test are continuously recorded in event logs, and these logs are intended to help people… (more)

Subjects/Keywords: Anomaly Detection; LSTM; Root cause analysis; ASML

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

SHI, X. (. (2019). Anomaly detection and diagnosis in ASML event log using attentional LSTM network. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:10964ba3-a16e-492b-90de-e5b866f480d9

Chicago Manual of Style (16th Edition):

SHI, XIAOTONG (author). “Anomaly detection and diagnosis in ASML event log using attentional LSTM network.” 2019. Masters Thesis, Delft University of Technology. Accessed April 18, 2021. http://resolver.tudelft.nl/uuid:10964ba3-a16e-492b-90de-e5b866f480d9.

MLA Handbook (7th Edition):

SHI, XIAOTONG (author). “Anomaly detection and diagnosis in ASML event log using attentional LSTM network.” 2019. Web. 18 Apr 2021.

Vancouver:

SHI X(. Anomaly detection and diagnosis in ASML event log using attentional LSTM network. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Apr 18]. Available from: http://resolver.tudelft.nl/uuid:10964ba3-a16e-492b-90de-e5b866f480d9.

Council of Science Editors:

SHI X(. Anomaly detection and diagnosis in ASML event log using attentional LSTM network. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:10964ba3-a16e-492b-90de-e5b866f480d9


Delft University of Technology

30. Lan, Yikai (author). Monitoring Release Logs at Adyen: Feature Extraction and Anomaly Detection.

Degree: 2018, Delft University of Technology

Monitoring the release logs of modern online software is a challenging topic because of the enormous amount of release logs and the complicated release process.… (more)

Subjects/Keywords: Log Analysis; Anomaly Detection; Machine Learning

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APA (6th Edition):

Lan, Y. (. (2018). Monitoring Release Logs at Adyen: Feature Extraction and Anomaly Detection. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9a35364f-89dc-4f31-84bd-072738b9c4e8

Chicago Manual of Style (16th Edition):

Lan, Yikai (author). “Monitoring Release Logs at Adyen: Feature Extraction and Anomaly Detection.” 2018. Masters Thesis, Delft University of Technology. Accessed April 18, 2021. http://resolver.tudelft.nl/uuid:9a35364f-89dc-4f31-84bd-072738b9c4e8.

MLA Handbook (7th Edition):

Lan, Yikai (author). “Monitoring Release Logs at Adyen: Feature Extraction and Anomaly Detection.” 2018. Web. 18 Apr 2021.

Vancouver:

Lan Y(. Monitoring Release Logs at Adyen: Feature Extraction and Anomaly Detection. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Apr 18]. Available from: http://resolver.tudelft.nl/uuid:9a35364f-89dc-4f31-84bd-072738b9c4e8.

Council of Science Editors:

Lan Y(. Monitoring Release Logs at Adyen: Feature Extraction and Anomaly Detection. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:9a35364f-89dc-4f31-84bd-072738b9c4e8

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