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You searched for subject:(anomaly detection). Showing records 1 – 30 of 464 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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

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 February 26, 2020. 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. 26 Feb 2020.

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 2020 Feb 26]. 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


University of Alberta

2. 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 February 26, 2020. https://era.library.ualberta.ca/files/g445cd805.

MLA Handbook (7th Edition):

Mueller, David A. “Time Series Discords.” 2013. Web. 26 Feb 2020.

Vancouver:

Mueller DA. Time Series Discords. [Internet] [Masters thesis]. University of Alberta; 2013. [cited 2020 Feb 26]. 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

3. 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 February 26, 2020. 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. 26 Feb 2020.

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 2020 Feb 26]. 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 Sydney

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

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 February 26, 2020. 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. 26 Feb 2020.

Vancouver:

Raghavendra C. Deep Learning for Anomaly Detection . [Internet] [Thesis]. University of Sydney; 2019. [cited 2020 Feb 26]. 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


University of Sydney

5. 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 February 26, 2020. 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. 26 Feb 2020.

Vancouver:

Toth E. Detecting Group Deviations . [Internet] [Thesis]. University of Sydney; 2018. [cited 2020 Feb 26]. 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


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 February 26, 2020. 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. 26 Feb 2020.

Vancouver:

Blomquist H. Anomaly detection with Machine learning : Quality assurance of statistical data in the Aid community. [Internet] [Thesis]. Uppsala University; 2015. [cited 2020 Feb 26]. 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


Royal Holloway, University of London

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

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 February 26, 2020. 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. 26 Feb 2020.

Vancouver:

Smith J. The efficiency of conformal predictors for anomaly detection. [Internet] [Doctoral dissertation]. Royal Holloway, University of London; 2016. [cited 2020 Feb 26]. 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


Virginia Tech

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

Degree: PhD, Computer Science, 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 (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 February 26, 2020. 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. 26 Feb 2020.

Vancouver:

Shu X. Threat Detection in Program Execution and Data Movement: Theory and Practice. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2020 Feb 26]. 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

9. Wijnands, K.J. 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 (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. “Using endpoints process information for malicious behavior detection:.” 2015. Masters Thesis, Delft University of Technology. Accessed February 26, 2020. http://resolver.tudelft.nl/uuid:e1678077-9056-47ac-82e6-2762bfb40a63.

MLA Handbook (7th Edition):

Wijnands, K J. “Using endpoints process information for malicious behavior detection:.” 2015. Web. 26 Feb 2020.

Vancouver:

Wijnands KJ. Using endpoints process information for malicious behavior detection:. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2020 Feb 26]. 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

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

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 February 26, 2020. http://hdl.handle.net/10871/34351.

MLA Handbook (7th Edition):

Huang, Chengqiang. “Featured anomaly detection methods and applications.” 2018. Web. 26 Feb 2020.

Vancouver:

Huang C. Featured anomaly detection methods and applications. [Internet] [Doctoral dissertation]. University of Exeter; 2018. [cited 2020 Feb 26]. 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


Rochester Institute of Technology

11. Doster, Timothy J. Mathematical methods for anomaly grouping in hyperspectral images.

Degree: School of Mathematical Sciences (COS), 2009, Rochester Institute of Technology

 The topological anomaly detection (TAD) algorithm differs from other anomaly detection algorithms in that it does not rely on the data's being normally distributed. We… (more)

Subjects/Keywords: Anomaly detection; Graph theory; Hyperspectral; Local linear embedding; Topological anomaly detection

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APA (6th Edition):

Doster, T. J. (2009). Mathematical methods for anomaly grouping in hyperspectral images. (Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/4990

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):

Doster, Timothy J. “Mathematical methods for anomaly grouping in hyperspectral images.” 2009. Thesis, Rochester Institute of Technology. Accessed February 26, 2020. https://scholarworks.rit.edu/theses/4990.

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

MLA Handbook (7th Edition):

Doster, Timothy J. “Mathematical methods for anomaly grouping in hyperspectral images.” 2009. Web. 26 Feb 2020.

Vancouver:

Doster TJ. Mathematical methods for anomaly grouping in hyperspectral images. [Internet] [Thesis]. Rochester Institute of Technology; 2009. [cited 2020 Feb 26]. Available from: https://scholarworks.rit.edu/theses/4990.

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

Council of Science Editors:

Doster TJ. Mathematical methods for anomaly grouping in hyperspectral images. [Thesis]. Rochester Institute of Technology; 2009. Available from: https://scholarworks.rit.edu/theses/4990

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


University of Waterloo

12. 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 February 26, 2020. 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. 26 Feb 2020.

Vancouver:

Cheng X. Anomaly Detection and Fault Localization Using Runtime State Models. [Internet] [Thesis]. University of Waterloo; 2016. [cited 2020 Feb 26]. 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


Texas A&M University

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

Degree: 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. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/149307

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):

Lin, Sheng-Ya. “Modeling and Detection of Content and Packet Flow Anomalies at Enterprise Network Gateway.” 2013. Thesis, Texas A&M University. Accessed February 26, 2020. http://hdl.handle.net/1969.1/149307.

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

MLA Handbook (7th Edition):

Lin, Sheng-Ya. “Modeling and Detection of Content and Packet Flow Anomalies at Enterprise Network Gateway.” 2013. Web. 26 Feb 2020.

Vancouver:

Lin S. Modeling and Detection of Content and Packet Flow Anomalies at Enterprise Network Gateway. [Internet] [Thesis]. Texas A&M University; 2013. [cited 2020 Feb 26]. Available from: http://hdl.handle.net/1969.1/149307.

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

Council of Science Editors:

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

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


University of Minnesota

14. Chandola, Varun. Anomaly detection for symbolic sequences and time series data.

Degree: PhD, Computer Science, 2009, University of Minnesota

 This thesis deals with the problem of anomaly detection for sequence data. Anomaly detection has been a widely researched problem in several application domains such… (more)

Subjects/Keywords: Anomaly Detection; Data Mining; Computer Science

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APA (6th Edition):

Chandola, V. (2009). Anomaly detection for symbolic sequences and time series data. (Doctoral Dissertation). University of Minnesota. Retrieved from http://purl.umn.edu/56597

Chicago Manual of Style (16th Edition):

Chandola, Varun. “Anomaly detection for symbolic sequences and time series data.” 2009. Doctoral Dissertation, University of Minnesota. Accessed February 26, 2020. http://purl.umn.edu/56597.

MLA Handbook (7th Edition):

Chandola, Varun. “Anomaly detection for symbolic sequences and time series data.” 2009. Web. 26 Feb 2020.

Vancouver:

Chandola V. Anomaly detection for symbolic sequences and time series data. [Internet] [Doctoral dissertation]. University of Minnesota; 2009. [cited 2020 Feb 26]. Available from: http://purl.umn.edu/56597.

Council of Science Editors:

Chandola V. Anomaly detection for symbolic sequences and time series data. [Doctoral Dissertation]. University of Minnesota; 2009. Available from: http://purl.umn.edu/56597


University of New Mexico

15. Karlin, Josh. Distributed Internet security and measurement.

Degree: Department of Computer Science, 2009, University of New Mexico

 The Internet has developed into an important economic, military, academic, and social resource. It is a complex network, comprised of tens of thousands of independently… (more)

Subjects/Keywords: Network Security; BGP; Anomaly Detection; Censorship

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APA (6th Edition):

Karlin, J. (2009). Distributed Internet security and measurement. (Doctoral Dissertation). University of New Mexico. Retrieved from http://hdl.handle.net/1928/9321

Chicago Manual of Style (16th Edition):

Karlin, Josh. “Distributed Internet security and measurement.” 2009. Doctoral Dissertation, University of New Mexico. Accessed February 26, 2020. http://hdl.handle.net/1928/9321.

MLA Handbook (7th Edition):

Karlin, Josh. “Distributed Internet security and measurement.” 2009. Web. 26 Feb 2020.

Vancouver:

Karlin J. Distributed Internet security and measurement. [Internet] [Doctoral dissertation]. University of New Mexico; 2009. [cited 2020 Feb 26]. Available from: http://hdl.handle.net/1928/9321.

Council of Science Editors:

Karlin J. Distributed Internet security and measurement. [Doctoral Dissertation]. University of New Mexico; 2009. Available from: http://hdl.handle.net/1928/9321


The Ohio State University

16. Das, Mahashweta. Spatio-Temporal Anomaly Detection.

Degree: MS, Computer Science and Engineering, 2009, The Ohio State University

 Recent advances in computational sciences have led to the generation and utilization of enormous amounts of spatio-temporal data in numerous scientific disciplines, such as wireless… (more)

Subjects/Keywords: Computer Science; Anomaly Detection; Spatio-Temporal Mining

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APA (6th Edition):

Das, M. (2009). Spatio-Temporal Anomaly Detection. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1261540196

Chicago Manual of Style (16th Edition):

Das, Mahashweta. “Spatio-Temporal Anomaly Detection.” 2009. Masters Thesis, The Ohio State University. Accessed February 26, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1261540196.

MLA Handbook (7th Edition):

Das, Mahashweta. “Spatio-Temporal Anomaly Detection.” 2009. Web. 26 Feb 2020.

Vancouver:

Das M. Spatio-Temporal Anomaly Detection. [Internet] [Masters thesis]. The Ohio State University; 2009. [cited 2020 Feb 26]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1261540196.

Council of Science Editors:

Das M. Spatio-Temporal Anomaly Detection. [Masters Thesis]. The Ohio State University; 2009. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1261540196


Carnegie Mellon University

17. 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 February 26, 2020. 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. 26 Feb 2020.

Vancouver:

Whiting ME. Anomaly Classification Through Automated Shape Grammar Representation. [Internet] [Thesis]. Carnegie Mellon University; 2017. [cited 2020 Feb 26]. 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


Virginia Tech

18. Zhang, Hao. Discovery of Triggering Relations and Its Applications in Network Security and Android Malware Detection.

Degree: PhD, Computer Science, 2015, Virginia Tech

 An increasing variety of malware, including spyware, worms, and bots, threatens data confidentiality and system integrity on computing devices ranging from backend servers to mobile… (more)

Subjects/Keywords: Network Security; Stealthy Malware; Anomaly Detection

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APA (6th Edition):

Zhang, H. (2015). Discovery of Triggering Relations and Its Applications in Network Security and Android Malware Detection. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/64246

Chicago Manual of Style (16th Edition):

Zhang, Hao. “Discovery of Triggering Relations and Its Applications in Network Security and Android Malware Detection.” 2015. Doctoral Dissertation, Virginia Tech. Accessed February 26, 2020. http://hdl.handle.net/10919/64246.

MLA Handbook (7th Edition):

Zhang, Hao. “Discovery of Triggering Relations and Its Applications in Network Security and Android Malware Detection.” 2015. Web. 26 Feb 2020.

Vancouver:

Zhang H. Discovery of Triggering Relations and Its Applications in Network Security and Android Malware Detection. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2020 Feb 26]. Available from: http://hdl.handle.net/10919/64246.

Council of Science Editors:

Zhang H. Discovery of Triggering Relations and Its Applications in Network Security and Android Malware Detection. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/64246


Virginia Tech

19. Liu, Xutong. Prediction and Anomaly Detection Techniques for Spatial Data.

Degree: PhD, Computer Science, 2013, Virginia Tech

 With increasing public sensitivity and concern on environmental issues, huge amounts of spatial data have been collected from location based social network applications to scientific… (more)

Subjects/Keywords: Spatial; Multivariate; Robust Inference; Anomaly Detection

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APA (6th Edition):

Liu, X. (2013). Prediction and Anomaly Detection Techniques for Spatial Data. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/23201

Chicago Manual of Style (16th Edition):

Liu, Xutong. “Prediction and Anomaly Detection Techniques for Spatial Data.” 2013. Doctoral Dissertation, Virginia Tech. Accessed February 26, 2020. http://hdl.handle.net/10919/23201.

MLA Handbook (7th Edition):

Liu, Xutong. “Prediction and Anomaly Detection Techniques for Spatial Data.” 2013. Web. 26 Feb 2020.

Vancouver:

Liu X. Prediction and Anomaly Detection Techniques for Spatial Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2013. [cited 2020 Feb 26]. Available from: http://hdl.handle.net/10919/23201.

Council of Science Editors:

Liu X. Prediction and Anomaly Detection Techniques for Spatial Data. [Doctoral Dissertation]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/23201


Delft University of Technology

20. Lion, W.F. Anomaly Detection in ACMS Data for Predictive Maintenance at KLM Engineering & Maintenance:.

Degree: 2016, Delft University of Technology

 This thesis investigates the opportunity to use the massive amounts of data coming from modern aircraft to predict maintenance tasks. The goal is to predict… (more)

Subjects/Keywords: ACMS; Predictive; KLM; Anomaly Detection; Subsequence; Maintenance

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APA (6th Edition):

Lion, W. F. (2016). Anomaly Detection in ACMS Data for Predictive Maintenance at KLM Engineering & Maintenance:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:b55c9a28-c034-4ca6-9360-294bc8938d72

Chicago Manual of Style (16th Edition):

Lion, W F. “Anomaly Detection in ACMS Data for Predictive Maintenance at KLM Engineering & Maintenance:.” 2016. Masters Thesis, Delft University of Technology. Accessed February 26, 2020. http://resolver.tudelft.nl/uuid:b55c9a28-c034-4ca6-9360-294bc8938d72.

MLA Handbook (7th Edition):

Lion, W F. “Anomaly Detection in ACMS Data for Predictive Maintenance at KLM Engineering & Maintenance:.” 2016. Web. 26 Feb 2020.

Vancouver:

Lion WF. Anomaly Detection in ACMS Data for Predictive Maintenance at KLM Engineering & Maintenance:. [Internet] [Masters thesis]. Delft University of Technology; 2016. [cited 2020 Feb 26]. Available from: http://resolver.tudelft.nl/uuid:b55c9a28-c034-4ca6-9360-294bc8938d72.

Council of Science Editors:

Lion WF. Anomaly Detection in ACMS Data for Predictive Maintenance at KLM Engineering & Maintenance:. [Masters Thesis]. Delft University of Technology; 2016. Available from: http://resolver.tudelft.nl/uuid:b55c9a28-c034-4ca6-9360-294bc8938d72


University of Waikato

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

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 February 26, 2020. http://hdl.handle.net/10289/8808.

MLA Handbook (7th Edition):

Mungro, Meenakshee. “Rating the Significance of Detected Network Events .” 2014. Web. 26 Feb 2020.

Vancouver:

Mungro M. Rating the Significance of Detected Network Events . [Internet] [Masters thesis]. University of Waikato; 2014. [cited 2020 Feb 26]. 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


Vanderbilt University

22. 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://etd.library.vanderbilt.edu/available/etd-04092013-182409/ ;

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 February 26, 2020. http://etd.library.vanderbilt.edu/available/etd-04092013-182409/ ;.

MLA Handbook (7th Edition):

Mack, Daniel Leif Campana. “Anomaly Detection from Complex Temporal Sequences in Large Data.” 2013. Web. 26 Feb 2020.

Vancouver:

Mack DLC. Anomaly Detection from Complex Temporal Sequences in Large Data. [Internet] [Doctoral dissertation]. Vanderbilt University; 2013. [cited 2020 Feb 26]. Available from: http://etd.library.vanderbilt.edu/available/etd-04092013-182409/ ;.

Council of Science Editors:

Mack DLC. Anomaly Detection from Complex Temporal Sequences in Large Data. [Doctoral Dissertation]. Vanderbilt University; 2013. Available from: http://etd.library.vanderbilt.edu/available/etd-04092013-182409/ ;


University of New South Wales

23. Xie, Miao. Anomaly Detection in Wireless Sensor Networks.

Degree: Engineering & Information Technology, 2013, University of New South Wales

 Wireless sensor networks (WSNs) are vulnerable to attacks and faults which are often linked to abnormal events. Unsupervisedanomaly detection techniques can be incorporated into WSNs… (more)

Subjects/Keywords: Distributed computing; Wireless sensor network; Anomaly detection

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APA (6th Edition):

Xie, M. (2013). Anomaly Detection in Wireless Sensor Networks. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/53290 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11985/SOURCE01?view=true

Chicago Manual of Style (16th Edition):

Xie, Miao. “Anomaly Detection in Wireless Sensor Networks.” 2013. Doctoral Dissertation, University of New South Wales. Accessed February 26, 2020. http://handle.unsw.edu.au/1959.4/53290 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11985/SOURCE01?view=true.

MLA Handbook (7th Edition):

Xie, Miao. “Anomaly Detection in Wireless Sensor Networks.” 2013. Web. 26 Feb 2020.

Vancouver:

Xie M. Anomaly Detection in Wireless Sensor Networks. [Internet] [Doctoral dissertation]. University of New South Wales; 2013. [cited 2020 Feb 26]. Available from: http://handle.unsw.edu.au/1959.4/53290 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11985/SOURCE01?view=true.

Council of Science Editors:

Xie M. Anomaly Detection in Wireless Sensor Networks. [Doctoral Dissertation]. University of New South Wales; 2013. Available from: http://handle.unsw.edu.au/1959.4/53290 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11985/SOURCE01?view=true


University of North Texas

24. Pannu, Husanbir Singh. Semi-supervised and Self-evolving Learning Algorithms with Application to Anomaly Detection in Cloud Computing.

Degree: 2012, University of North Texas

 Semi-supervised learning (SSL) is the most practical approach for classification among machine learning algorithms. It is similar to the humans way of learning and thus… (more)

Subjects/Keywords: Machine learning; anomaly detection; cloud computing

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APA (6th Edition):

Pannu, H. S. (2012). Semi-supervised and Self-evolving Learning Algorithms with Application to Anomaly Detection in Cloud Computing. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc177238/

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):

Pannu, Husanbir Singh. “Semi-supervised and Self-evolving Learning Algorithms with Application to Anomaly Detection in Cloud Computing.” 2012. Thesis, University of North Texas. Accessed February 26, 2020. https://digital.library.unt.edu/ark:/67531/metadc177238/.

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

MLA Handbook (7th Edition):

Pannu, Husanbir Singh. “Semi-supervised and Self-evolving Learning Algorithms with Application to Anomaly Detection in Cloud Computing.” 2012. Web. 26 Feb 2020.

Vancouver:

Pannu HS. Semi-supervised and Self-evolving Learning Algorithms with Application to Anomaly Detection in Cloud Computing. [Internet] [Thesis]. University of North Texas; 2012. [cited 2020 Feb 26]. Available from: https://digital.library.unt.edu/ark:/67531/metadc177238/.

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

Council of Science Editors:

Pannu HS. Semi-supervised and Self-evolving Learning Algorithms with Application to Anomaly Detection in Cloud Computing. [Thesis]. University of North Texas; 2012. Available from: https://digital.library.unt.edu/ark:/67531/metadc177238/

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


Northeastern University

25. 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 February 26, 2020. http://hdl.handle.net/2047/D20237881.

MLA Handbook (7th Edition):

Lopez, Jose A. “Anomaly detection using polynomial optimization methods.” 2016. Web. 26 Feb 2020.

Vancouver:

Lopez JA. Anomaly detection using polynomial optimization methods. [Internet] [Doctoral dissertation]. Northeastern University; 2016. [cited 2020 Feb 26]. 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


University of Sydney

26. Babaie, Tahereh Tara. New Methods for Network Traffic Anomaly Detection .

Degree: 2014, University of Sydney

 In this thesis we examine the efficacy of applying outlier detection techniques to understand the behaviour of anomalies in communication network traffic. We have identified… (more)

Subjects/Keywords: Anomaly Detection; Control Theory; Machine Learning; Network

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APA (6th Edition):

Babaie, T. T. (2014). New Methods for Network Traffic Anomaly Detection . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/12032

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):

Babaie, Tahereh Tara. “New Methods for Network Traffic Anomaly Detection .” 2014. Thesis, University of Sydney. Accessed February 26, 2020. http://hdl.handle.net/2123/12032.

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

MLA Handbook (7th Edition):

Babaie, Tahereh Tara. “New Methods for Network Traffic Anomaly Detection .” 2014. Web. 26 Feb 2020.

Vancouver:

Babaie TT. New Methods for Network Traffic Anomaly Detection . [Internet] [Thesis]. University of Sydney; 2014. [cited 2020 Feb 26]. Available from: http://hdl.handle.net/2123/12032.

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

Council of Science Editors:

Babaie TT. New Methods for Network Traffic Anomaly Detection . [Thesis]. University of Sydney; 2014. Available from: http://hdl.handle.net/2123/12032

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


University of Minnesota

27. Kulkarni, Akash. DeepFGSS: Anomalous Pattern Detection using Deep Learning.

Degree: MS, Computer Science, 2019, University of Minnesota

Anomaly detection refers to finding observations which do not conform to expected behavior. It is widely applied in many domains such as image processing, fraud… (more)

Subjects/Keywords: Anomaly detection; Autoencoder; Bayesian Network; Collective anomaly detection; Deep learning; Machine learning

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APA (6th Edition):

Kulkarni, A. (2019). DeepFGSS: Anomalous Pattern Detection using Deep Learning. (Masters Thesis). University of Minnesota. Retrieved from http://hdl.handle.net/11299/206159

Chicago Manual of Style (16th Edition):

Kulkarni, Akash. “DeepFGSS: Anomalous Pattern Detection using Deep Learning.” 2019. Masters Thesis, University of Minnesota. Accessed February 26, 2020. http://hdl.handle.net/11299/206159.

MLA Handbook (7th Edition):

Kulkarni, Akash. “DeepFGSS: Anomalous Pattern Detection using Deep Learning.” 2019. Web. 26 Feb 2020.

Vancouver:

Kulkarni A. DeepFGSS: Anomalous Pattern Detection using Deep Learning. [Internet] [Masters thesis]. University of Minnesota; 2019. [cited 2020 Feb 26]. Available from: http://hdl.handle.net/11299/206159.

Council of Science Editors:

Kulkarni A. DeepFGSS: Anomalous Pattern Detection using Deep Learning. [Masters Thesis]. University of Minnesota; 2019. Available from: http://hdl.handle.net/11299/206159


University of Auckland

28. Eimann, Raimund E. A. Network event detection with entropy measures.

Degree: 2008, University of Auckland

 Information measures may be used to estimate the amount of information emitted by discrete information sources. Network streams are an example for such discrete information… (more)

Subjects/Keywords: Information Theory; Entropy; Network Events; Anomaly Detection; Network Event Detection

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APA (6th Edition):

Eimann, R. E. A. (2008). Network event detection with entropy measures. (Doctoral Dissertation). University of Auckland. Retrieved from http://hdl.handle.net/2292/3427

Chicago Manual of Style (16th Edition):

Eimann, Raimund E A. “Network event detection with entropy measures.” 2008. Doctoral Dissertation, University of Auckland. Accessed February 26, 2020. http://hdl.handle.net/2292/3427.

MLA Handbook (7th Edition):

Eimann, Raimund E A. “Network event detection with entropy measures.” 2008. Web. 26 Feb 2020.

Vancouver:

Eimann REA. Network event detection with entropy measures. [Internet] [Doctoral dissertation]. University of Auckland; 2008. [cited 2020 Feb 26]. Available from: http://hdl.handle.net/2292/3427.

Council of Science Editors:

Eimann REA. Network event detection with entropy measures. [Doctoral Dissertation]. University of Auckland; 2008. Available from: http://hdl.handle.net/2292/3427

29. Borges, Nash. Robust Anomaly Detection with Applications to Acoustics and Graphs.

Degree: 2014, Johns Hopkins University

 Our goal is to develop a robust anomaly detector that can be incorporated into pattern recognition systems that may need to learn, but will never… (more)

Subjects/Keywords: Anomaly Detection; Divergence Estimation; Speech Activity Detection; Random Graphs

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APA (6th Edition):

Borges, N. (2014). Robust Anomaly Detection with Applications to Acoustics and Graphs. (Thesis). Johns Hopkins University. Retrieved from http://jhir.library.jhu.edu/handle/1774.2/36963

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):

Borges, Nash. “Robust Anomaly Detection with Applications to Acoustics and Graphs.” 2014. Thesis, Johns Hopkins University. Accessed February 26, 2020. http://jhir.library.jhu.edu/handle/1774.2/36963.

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

MLA Handbook (7th Edition):

Borges, Nash. “Robust Anomaly Detection with Applications to Acoustics and Graphs.” 2014. Web. 26 Feb 2020.

Vancouver:

Borges N. Robust Anomaly Detection with Applications to Acoustics and Graphs. [Internet] [Thesis]. Johns Hopkins University; 2014. [cited 2020 Feb 26]. Available from: http://jhir.library.jhu.edu/handle/1774.2/36963.

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

Council of Science Editors:

Borges N. Robust Anomaly Detection with Applications to Acoustics and Graphs. [Thesis]. Johns Hopkins University; 2014. Available from: http://jhir.library.jhu.edu/handle/1774.2/36963

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


University of Waikato

30. Löf, Andreas. Improving the Evaluation of Network Anomaly Detection Using a Data Fusion Approach .

Degree: 2013, University of Waikato

 Currently, the evaluation of network anomaly detection methods is often not repeatable. It is difficult to ascertain if different implementations of the same methods have… (more)

Subjects/Keywords: network anomaly detection; network capture; intrusion detection; data fusion

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APA (6th Edition):

Löf, A. (2013). Improving the Evaluation of Network Anomaly Detection Using a Data Fusion Approach . (Doctoral Dissertation). University of Waikato. Retrieved from http://hdl.handle.net/10289/8041

Chicago Manual of Style (16th Edition):

Löf, Andreas. “Improving the Evaluation of Network Anomaly Detection Using a Data Fusion Approach .” 2013. Doctoral Dissertation, University of Waikato. Accessed February 26, 2020. http://hdl.handle.net/10289/8041.

MLA Handbook (7th Edition):

Löf, Andreas. “Improving the Evaluation of Network Anomaly Detection Using a Data Fusion Approach .” 2013. Web. 26 Feb 2020.

Vancouver:

Löf A. Improving the Evaluation of Network Anomaly Detection Using a Data Fusion Approach . [Internet] [Doctoral dissertation]. University of Waikato; 2013. [cited 2020 Feb 26]. Available from: http://hdl.handle.net/10289/8041.

Council of Science Editors:

Löf A. Improving the Evaluation of Network Anomaly Detection Using a Data Fusion Approach . [Doctoral Dissertation]. University of Waikato; 2013. Available from: http://hdl.handle.net/10289/8041

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