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You searched for subject:(Anomaly Detection). Showing records 1 – 30 of 456 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 January 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 Jan 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 Jan 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 January 26, 2020. https://era.library.ualberta.ca/files/g445cd805.

MLA Handbook (7th Edition):

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

Vancouver:

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


Texas A&M University

3. Vasistha, Daksh Kumar. Detecting Anomalies in Controller Area Network for Automobiles.

Degree: MS, Computer Engineering, 2017, Texas A&M University

 Availability of interfaces such as WI-FI, Bluetooth and Cellular networks, software components to control a vehicle?s functionality, and lack of security mechanisms in the Controller… (more)

Subjects/Keywords: CAN bus; Anomaly detection

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APA (6th Edition):

Vasistha, D. K. (2017). Detecting Anomalies in Controller Area Network for Automobiles. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/165769

Chicago Manual of Style (16th Edition):

Vasistha, Daksh Kumar. “Detecting Anomalies in Controller Area Network for Automobiles.” 2017. Masters Thesis, Texas A&M University. Accessed January 26, 2020. http://hdl.handle.net/1969.1/165769.

MLA Handbook (7th Edition):

Vasistha, Daksh Kumar. “Detecting Anomalies in Controller Area Network for Automobiles.” 2017. Web. 26 Jan 2020.

Vancouver:

Vasistha DK. Detecting Anomalies in Controller Area Network for Automobiles. [Internet] [Masters thesis]. Texas A&M University; 2017. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/1969.1/165769.

Council of Science Editors:

Vasistha DK. Detecting Anomalies in Controller Area Network for Automobiles. [Masters Thesis]. Texas A&M University; 2017. Available from: http://hdl.handle.net/1969.1/165769

4. 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 January 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 Jan 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 Jan 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.


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 January 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 Jan 2020.

Vancouver:

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


University of Sydney

6. 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 January 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 Jan 2020.

Vancouver:

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

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

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 January 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 Jan 2020.

Vancouver:

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

8. 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 January 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 Jan 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 Jan 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


Virginia Tech

9. 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 · 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 January 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 Jan 2020.

Vancouver:

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

10. 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 January 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 Jan 2020.

Vancouver:

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


University of Exeter

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

MLA Handbook (7th Edition):

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

Vancouver:

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

12. 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 January 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 Jan 2020.

Vancouver:

Doster TJ. Mathematical methods for anomaly grouping in hyperspectral images. [Internet] [Thesis]. Rochester Institute of Technology; 2009. [cited 2020 Jan 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

13. 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 January 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 Jan 2020.

Vancouver:

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

14. 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 January 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 Jan 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 Jan 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

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

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 January 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 Jan 2020.

Vancouver:

Chandola V. Anomaly detection for symbolic sequences and time series data. [Internet] [Doctoral dissertation]. University of Minnesota; 2009. [cited 2020 Jan 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

16. 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 January 26, 2020. http://hdl.handle.net/1928/9321.

MLA Handbook (7th Edition):

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

Vancouver:

Karlin J. Distributed Internet security and measurement. [Internet] [Doctoral dissertation]. University of New Mexico; 2009. [cited 2020 Jan 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


Texas A&M University

17. Taghavi, Travis. Machine Learning for Anomaly Detection in Overlapping Aerial Image Streams.

Degree: MS, Electrical Engineering, 2017, Texas A&M University

 This thesis work is an exploration into the application of machine learning to a current problem relating to streams of aerial images, with application to… (more)

Subjects/Keywords: Machine learning; anomaly detection; image processing

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APA (6th Edition):

Taghavi, T. (2017). Machine Learning for Anomaly Detection in Overlapping Aerial Image Streams. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/166018

Chicago Manual of Style (16th Edition):

Taghavi, Travis. “Machine Learning for Anomaly Detection in Overlapping Aerial Image Streams.” 2017. Masters Thesis, Texas A&M University. Accessed January 26, 2020. http://hdl.handle.net/1969.1/166018.

MLA Handbook (7th Edition):

Taghavi, Travis. “Machine Learning for Anomaly Detection in Overlapping Aerial Image Streams.” 2017. Web. 26 Jan 2020.

Vancouver:

Taghavi T. Machine Learning for Anomaly Detection in Overlapping Aerial Image Streams. [Internet] [Masters thesis]. Texas A&M University; 2017. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/1969.1/166018.

Council of Science Editors:

Taghavi T. Machine Learning for Anomaly Detection in Overlapping Aerial Image Streams. [Masters Thesis]. Texas A&M University; 2017. Available from: http://hdl.handle.net/1969.1/166018


The Ohio State University

18. 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 January 26, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1261540196.

MLA Handbook (7th Edition):

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

Vancouver:

Das M. Spatio-Temporal Anomaly Detection. [Internet] [Masters thesis]. The Ohio State University; 2009. [cited 2020 Jan 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

19. 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 January 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 Jan 2020.

Vancouver:

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

20. 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 January 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 Jan 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 Jan 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

21. 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 January 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 Jan 2020.

Vancouver:

Liu X. Prediction and Anomaly Detection Techniques for Spatial Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2013. [cited 2020 Jan 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


University of Adelaide

22. Alvarez Cid-Fuentes, Javier. Adaptive anomalous behavior identification in large-scale distributed systems.

Degree: 2017, University of Adelaide

 Distributed systems have become pervasive in current society. From laptops and mobile phones, to servers and data centers, most computers communicate and coordinate their activities… (more)

Subjects/Keywords: distributed systems; large-scale systems; anomaly detection

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APA (6th Edition):

Alvarez Cid-Fuentes, J. (2017). Adaptive anomalous behavior identification in large-scale distributed systems. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/112593

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):

Alvarez Cid-Fuentes, Javier. “Adaptive anomalous behavior identification in large-scale distributed systems.” 2017. Thesis, University of Adelaide. Accessed January 26, 2020. http://hdl.handle.net/2440/112593.

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

MLA Handbook (7th Edition):

Alvarez Cid-Fuentes, Javier. “Adaptive anomalous behavior identification in large-scale distributed systems.” 2017. Web. 26 Jan 2020.

Vancouver:

Alvarez Cid-Fuentes J. Adaptive anomalous behavior identification in large-scale distributed systems. [Internet] [Thesis]. University of Adelaide; 2017. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2440/112593.

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

Council of Science Editors:

Alvarez Cid-Fuentes J. Adaptive anomalous behavior identification in large-scale distributed systems. [Thesis]. University of Adelaide; 2017. Available from: http://hdl.handle.net/2440/112593

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


Delft University of Technology

23. 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 January 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 Jan 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 Jan 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

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

MLA Handbook (7th Edition):

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

Vancouver:

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

25. 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 January 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 Jan 2020.

Vancouver:

Mack DLC. Anomaly Detection from Complex Temporal Sequences in Large Data. [Internet] [Doctoral dissertation]. Vanderbilt University; 2013. [cited 2020 Jan 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

26. 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 January 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 Jan 2020.

Vancouver:

Xie M. Anomaly Detection in Wireless Sensor Networks. [Internet] [Doctoral dissertation]. University of New South Wales; 2013. [cited 2020 Jan 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

27. 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 January 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 Jan 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 Jan 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

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

MLA Handbook (7th Edition):

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

Vancouver:

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

29. 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 January 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 Jan 2020.

Vancouver:

Babaie TT. New Methods for Network Traffic Anomaly Detection . [Internet] [Thesis]. University of Sydney; 2014. [cited 2020 Jan 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


Georgia Tech

30. Puranik, Tejas Girish. A methodology for quantitative data-driven safety assessment for general aviation.

Degree: PhD, Aerospace Engineering, 2018, Georgia Tech

 The safety record of aviation operations has been steadily improving for the past few decades, however, accident rates in General Aviation (GA) have not improved… (more)

Subjects/Keywords: General aviation; Safety; Data-driven; Anomaly detection

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

APA (6th Edition):

Puranik, T. G. (2018). A methodology for quantitative data-driven safety assessment for general aviation. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59905

Chicago Manual of Style (16th Edition):

Puranik, Tejas Girish. “A methodology for quantitative data-driven safety assessment for general aviation.” 2018. Doctoral Dissertation, Georgia Tech. Accessed January 26, 2020. http://hdl.handle.net/1853/59905.

MLA Handbook (7th Edition):

Puranik, Tejas Girish. “A methodology for quantitative data-driven safety assessment for general aviation.” 2018. Web. 26 Jan 2020.

Vancouver:

Puranik TG. A methodology for quantitative data-driven safety assessment for general aviation. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/1853/59905.

Council of Science Editors:

Puranik TG. A methodology for quantitative data-driven safety assessment for general aviation. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/59905

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