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You searched for subject:(Malware classification). Showing records 1 – 22 of 22 total matches.

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NSYSU

1. Zeng, Ling-Ming. Malware Classification Based on File and Registry Activities.

Degree: Master, Information Management, 2012, NSYSU

 Cyber criminals are trying to steal personal information from victimâs machine to acquire more benefits by using malware. Antivirus is the most commonly used tool… (more)

Subjects/Keywords: Malware; Malware Classification; SVM

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APA (6th Edition):

Zeng, L. (2012). Malware Classification Based on File and Registry Activities. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0912112-145949

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):

Zeng, Ling-Ming. “Malware Classification Based on File and Registry Activities.” 2012. Thesis, NSYSU. Accessed December 16, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0912112-145949.

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

MLA Handbook (7th Edition):

Zeng, Ling-Ming. “Malware Classification Based on File and Registry Activities.” 2012. Web. 16 Dec 2019.

Vancouver:

Zeng L. Malware Classification Based on File and Registry Activities. [Internet] [Thesis]. NSYSU; 2012. [cited 2019 Dec 16]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0912112-145949.

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

Council of Science Editors:

Zeng L. Malware Classification Based on File and Registry Activities. [Thesis]. NSYSU; 2012. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0912112-145949

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


NSYSU

2. Chou, Chun-han. Malware Detection System Based on API Log Data Mining.

Degree: Master, Computer Science and Engineering, 2013, NSYSU

 As information technology improves, the Internet is involved in every area in our daily life. When the mobile devices and cloud computing technology start to… (more)

Subjects/Keywords: Classification; System Call; API; Data Mining; Malware

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APA (6th Edition):

Chou, C. (2013). Malware Detection System Based on API Log Data Mining. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0714113-160717

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):

Chou, Chun-han. “Malware Detection System Based on API Log Data Mining.” 2013. Thesis, NSYSU. Accessed December 16, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0714113-160717.

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

MLA Handbook (7th Edition):

Chou, Chun-han. “Malware Detection System Based on API Log Data Mining.” 2013. Web. 16 Dec 2019.

Vancouver:

Chou C. Malware Detection System Based on API Log Data Mining. [Internet] [Thesis]. NSYSU; 2013. [cited 2019 Dec 16]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0714113-160717.

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

Council of Science Editors:

Chou C. Malware Detection System Based on API Log Data Mining. [Thesis]. NSYSU; 2013. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0714113-160717

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


Mississippi State University

3. Glendowne, Dae. Automating malware detection in Windows memory images using machine learning.

Degree: PhD, Computer Science and Engineering, 2015, Mississippi State University

  Malicious software, or malware, is often employed as a tool to maintain access to previously compromised systems. It enables the intruders to utilize system… (more)

Subjects/Keywords: memory analysis; malware classification; DLL injection

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APA (6th Edition):

Glendowne, D. (2015). Automating malware detection in Windows memory images using machine learning. (Doctoral Dissertation). Mississippi State University. Retrieved from http://sun.library.msstate.edu/ETD-db/theses/available/etd-03312015-121708/ ;

Chicago Manual of Style (16th Edition):

Glendowne, Dae. “Automating malware detection in Windows memory images using machine learning.” 2015. Doctoral Dissertation, Mississippi State University. Accessed December 16, 2019. http://sun.library.msstate.edu/ETD-db/theses/available/etd-03312015-121708/ ;.

MLA Handbook (7th Edition):

Glendowne, Dae. “Automating malware detection in Windows memory images using machine learning.” 2015. Web. 16 Dec 2019.

Vancouver:

Glendowne D. Automating malware detection in Windows memory images using machine learning. [Internet] [Doctoral dissertation]. Mississippi State University; 2015. [cited 2019 Dec 16]. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-03312015-121708/ ;.

Council of Science Editors:

Glendowne D. Automating malware detection in Windows memory images using machine learning. [Doctoral Dissertation]. Mississippi State University; 2015. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-03312015-121708/ ;


University of New Mexico

4. Anderson, Blake. Integrating Multiple Data Views for Improved Malware Analysis.

Degree: Department of Computer Science, 2014, University of New Mexico

 Malicious software (malware) has become a prominent fixture in computing. There have been many methods developed over the years to combat the spread of malware,… (more)

Subjects/Keywords: Malware; Support Vector Machine; Classification; Clustering; Phylogeny

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APA (6th Edition):

Anderson, B. (2014). Integrating Multiple Data Views for Improved Malware Analysis. (Doctoral Dissertation). University of New Mexico. Retrieved from http://hdl.handle.net/1928/24289

Chicago Manual of Style (16th Edition):

Anderson, Blake. “Integrating Multiple Data Views for Improved Malware Analysis.” 2014. Doctoral Dissertation, University of New Mexico. Accessed December 16, 2019. http://hdl.handle.net/1928/24289.

MLA Handbook (7th Edition):

Anderson, Blake. “Integrating Multiple Data Views for Improved Malware Analysis.” 2014. Web. 16 Dec 2019.

Vancouver:

Anderson B. Integrating Multiple Data Views for Improved Malware Analysis. [Internet] [Doctoral dissertation]. University of New Mexico; 2014. [cited 2019 Dec 16]. Available from: http://hdl.handle.net/1928/24289.

Council of Science Editors:

Anderson B. Integrating Multiple Data Views for Improved Malware Analysis. [Doctoral Dissertation]. University of New Mexico; 2014. Available from: http://hdl.handle.net/1928/24289


University of Cincinnati

5. Subramanian, Nandita. Analysis of Rank Distance for Malware Classification.

Degree: MS, Engineering and Applied Science: Computer Science, 2016, University of Cincinnati

 Malicious Cyber Adversaries may compromise the security of a system by denying access to legitimate users. This is often coupled with immeasurable loss of confidential… (more)

Subjects/Keywords: Computer Science; Rank Distance; Malware Classification; Mutual Information; Text Mining; Similarity Measures; Windows Malware

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APA (6th Edition):

Subramanian, N. (2016). Analysis of Rank Distance for Malware Classification. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479823187035784

Chicago Manual of Style (16th Edition):

Subramanian, Nandita. “Analysis of Rank Distance for Malware Classification.” 2016. Masters Thesis, University of Cincinnati. Accessed December 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479823187035784.

MLA Handbook (7th Edition):

Subramanian, Nandita. “Analysis of Rank Distance for Malware Classification.” 2016. Web. 16 Dec 2019.

Vancouver:

Subramanian N. Analysis of Rank Distance for Malware Classification. [Internet] [Masters thesis]. University of Cincinnati; 2016. [cited 2019 Dec 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479823187035784.

Council of Science Editors:

Subramanian N. Analysis of Rank Distance for Malware Classification. [Masters Thesis]. University of Cincinnati; 2016. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479823187035784


University of Delaware

6. De La Rosa, Leonardo. Improving the effectiveness and efficiency of dynamic malware analysis using machine learning .

Degree: 2018, University of Delaware

 The malware threat landscape is constantly evolving, with upwards of one million new variants being released every day. Traditional approaches for detecting and classifying malware(more)

Subjects/Keywords: Applied sciences; Dynamic analysis; Important capabilities; Machine learning; Malware classification; Malware detection; Static analysis

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APA (6th Edition):

De La Rosa, L. (2018). Improving the effectiveness and efficiency of dynamic malware analysis using machine learning . (Doctoral Dissertation). University of Delaware. Retrieved from http://udspace.udel.edu/handle/19716/23984

Chicago Manual of Style (16th Edition):

De La Rosa, Leonardo. “Improving the effectiveness and efficiency of dynamic malware analysis using machine learning .” 2018. Doctoral Dissertation, University of Delaware. Accessed December 16, 2019. http://udspace.udel.edu/handle/19716/23984.

MLA Handbook (7th Edition):

De La Rosa, Leonardo. “Improving the effectiveness and efficiency of dynamic malware analysis using machine learning .” 2018. Web. 16 Dec 2019.

Vancouver:

De La Rosa L. Improving the effectiveness and efficiency of dynamic malware analysis using machine learning . [Internet] [Doctoral dissertation]. University of Delaware; 2018. [cited 2019 Dec 16]. Available from: http://udspace.udel.edu/handle/19716/23984.

Council of Science Editors:

De La Rosa L. Improving the effectiveness and efficiency of dynamic malware analysis using machine learning . [Doctoral Dissertation]. University of Delaware; 2018. Available from: http://udspace.udel.edu/handle/19716/23984


Indian Institute of Science

7. Saradha, R. Malware Analysis using Profile Hidden Markov Models and Intrusion Detection in a Stream Learning Setting.

Degree: 2014, Indian Institute of Science

 In the last decade, a lot of machine learning and data mining based approaches have been used in the areas of intrusion detection, malware detection… (more)

Subjects/Keywords: Malware (Malicious Software); Malware, Cyber Attacks; Malware Analysis; Profile Hidden Markov Models; Intrusion Detection Systems; Data Mining; Malware Classification and Clustering; Machine Learning; Malware Detection; Cyber Attacks; Stream-based Learning; Polymorphic Malware Detection; Huffman Encoding; Stream Algorithms; Computer Science

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APA (6th Edition):

Saradha, R. (2014). Malware Analysis using Profile Hidden Markov Models and Intrusion Detection in a Stream Learning Setting. (Thesis). Indian Institute of Science. Retrieved from http://hdl.handle.net/2005/3129

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):

Saradha, R. “Malware Analysis using Profile Hidden Markov Models and Intrusion Detection in a Stream Learning Setting.” 2014. Thesis, Indian Institute of Science. Accessed December 16, 2019. http://hdl.handle.net/2005/3129.

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

MLA Handbook (7th Edition):

Saradha, R. “Malware Analysis using Profile Hidden Markov Models and Intrusion Detection in a Stream Learning Setting.” 2014. Web. 16 Dec 2019.

Vancouver:

Saradha R. Malware Analysis using Profile Hidden Markov Models and Intrusion Detection in a Stream Learning Setting. [Internet] [Thesis]. Indian Institute of Science; 2014. [cited 2019 Dec 16]. Available from: http://hdl.handle.net/2005/3129.

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

Council of Science Editors:

Saradha R. Malware Analysis using Profile Hidden Markov Models and Intrusion Detection in a Stream Learning Setting. [Thesis]. Indian Institute of Science; 2014. Available from: http://hdl.handle.net/2005/3129

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


NSYSU

8. Yang, Chia-hui. Code Classification Based on Structure Similarity.

Degree: Master, Information Management, 2012, NSYSU

 Automatically classifying malware variants source code is the most important research issue in the field of digital forensics. By means of malware classification, we can… (more)

Subjects/Keywords: Malware Classification; Source Code; Static Analysis; Structure Similarity

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APA (6th Edition):

Yang, C. (2012). Code Classification Based on Structure Similarity. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0914112-155523

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):

Yang, Chia-hui. “Code Classification Based on Structure Similarity.” 2012. Thesis, NSYSU. Accessed December 16, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0914112-155523.

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

MLA Handbook (7th Edition):

Yang, Chia-hui. “Code Classification Based on Structure Similarity.” 2012. Web. 16 Dec 2019.

Vancouver:

Yang C. Code Classification Based on Structure Similarity. [Internet] [Thesis]. NSYSU; 2012. [cited 2019 Dec 16]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0914112-155523.

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

Council of Science Editors:

Yang C. Code Classification Based on Structure Similarity. [Thesis]. NSYSU; 2012. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0914112-155523

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


Kennesaw State University

9. Luo, Jhu-Sin. Malware Image Classification using Machine Learning with Local Binary Pattern.

Degree: MSCS, Computer Science, 2018, Kennesaw State University

Malware classification is a critical part in the cybersecurity. Traditional methodologies for the malware classification typically use static analysis and dynamic analysis to identify… (more)

Subjects/Keywords: malware; classification; machine learning; visualization; local binary pattern; Information Security

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APA (6th Edition):

Luo, J. (2018). Malware Image Classification using Machine Learning with Local Binary Pattern. (Thesis). Kennesaw State University. Retrieved from https://digitalcommons.kennesaw.edu/cs_etd/16

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):

Luo, Jhu-Sin. “Malware Image Classification using Machine Learning with Local Binary Pattern.” 2018. Thesis, Kennesaw State University. Accessed December 16, 2019. https://digitalcommons.kennesaw.edu/cs_etd/16.

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

MLA Handbook (7th Edition):

Luo, Jhu-Sin. “Malware Image Classification using Machine Learning with Local Binary Pattern.” 2018. Web. 16 Dec 2019.

Vancouver:

Luo J. Malware Image Classification using Machine Learning with Local Binary Pattern. [Internet] [Thesis]. Kennesaw State University; 2018. [cited 2019 Dec 16]. Available from: https://digitalcommons.kennesaw.edu/cs_etd/16.

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

Council of Science Editors:

Luo J. Malware Image Classification using Machine Learning with Local Binary Pattern. [Thesis]. Kennesaw State University; 2018. Available from: https://digitalcommons.kennesaw.edu/cs_etd/16

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


University of Toledo

10. Kulkarni, Keyur. Android Malware Detection through Permission and App Component Analysis using Machine Learning Algorithms.

Degree: MS, Engineering (Computer Science), 2018, University of Toledo

 Improvement in technology has inevitably altered the tactic of criminals to thievery. In recent times, information is the real commodity and it is thus subject(more)

Subjects/Keywords: Computer Science; Android, malware, application security, static features, classification

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APA (6th Edition):

Kulkarni, K. (2018). Android Malware Detection through Permission and App Component Analysis using Machine Learning Algorithms. (Masters Thesis). University of Toledo. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=toledo1525454213460236

Chicago Manual of Style (16th Edition):

Kulkarni, Keyur. “Android Malware Detection through Permission and App Component Analysis using Machine Learning Algorithms.” 2018. Masters Thesis, University of Toledo. Accessed December 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1525454213460236.

MLA Handbook (7th Edition):

Kulkarni, Keyur. “Android Malware Detection through Permission and App Component Analysis using Machine Learning Algorithms.” 2018. Web. 16 Dec 2019.

Vancouver:

Kulkarni K. Android Malware Detection through Permission and App Component Analysis using Machine Learning Algorithms. [Internet] [Masters thesis]. University of Toledo; 2018. [cited 2019 Dec 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=toledo1525454213460236.

Council of Science Editors:

Kulkarni K. Android Malware Detection through Permission and App Component Analysis using Machine Learning Algorithms. [Masters Thesis]. University of Toledo; 2018. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=toledo1525454213460236


George Mason University

11. Randive, Onkar. Analyzing Hardware Based Malware Detectors Using Machine Learning Techniques .

Degree: George Mason University

 Growth of malware has been a serious problem in the technology community and would continue to grow with new advances in technology. Traditional software-based malware(more)

Subjects/Keywords: hardware performance counters; machine learning; malware detection; malware classification

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APA (6th Edition):

Randive, O. (n.d.). Analyzing Hardware Based Malware Detectors Using Machine Learning Techniques . (Thesis). George Mason University. Retrieved from http://hdl.handle.net/1920/11456

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):

Randive, Onkar. “Analyzing Hardware Based Malware Detectors Using Machine Learning Techniques .” Thesis, George Mason University. Accessed December 16, 2019. http://hdl.handle.net/1920/11456.

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):

Randive, Onkar. “Analyzing Hardware Based Malware Detectors Using Machine Learning Techniques .” Web. 16 Dec 2019.

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

Vancouver:

Randive O. Analyzing Hardware Based Malware Detectors Using Machine Learning Techniques . [Internet] [Thesis]. George Mason University; [cited 2019 Dec 16]. Available from: http://hdl.handle.net/1920/11456.

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:

Randive O. Analyzing Hardware Based Malware Detectors Using Machine Learning Techniques . [Thesis]. George Mason University; Available from: http://hdl.handle.net/1920/11456

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


Northeastern University

12. Mankin, Jennifer Eligius. Classification of malware persistence mechanisms using low-artifact disk instrumentation.

Degree: PhD, Department of Electrical and Computer Engineering, 2013, Northeastern University

 The proliferation of malware in recent years has motivated the need for tools to analyze, classify, and understand intrusions. Current research in analyzing malware focuses… (more)

Subjects/Keywords: Computer security; Digital forensics; File systems; Malware analysis; Malware classification; Computer Engineering; Digital Communications and Networking; Hardware Systems

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APA (6th Edition):

Mankin, J. E. (2013). Classification of malware persistence mechanisms using low-artifact disk instrumentation. (Doctoral Dissertation). Northeastern University. Retrieved from http://hdl.handle.net/2047/d20004863

Chicago Manual of Style (16th Edition):

Mankin, Jennifer Eligius. “Classification of malware persistence mechanisms using low-artifact disk instrumentation.” 2013. Doctoral Dissertation, Northeastern University. Accessed December 16, 2019. http://hdl.handle.net/2047/d20004863.

MLA Handbook (7th Edition):

Mankin, Jennifer Eligius. “Classification of malware persistence mechanisms using low-artifact disk instrumentation.” 2013. Web. 16 Dec 2019.

Vancouver:

Mankin JE. Classification of malware persistence mechanisms using low-artifact disk instrumentation. [Internet] [Doctoral dissertation]. Northeastern University; 2013. [cited 2019 Dec 16]. Available from: http://hdl.handle.net/2047/d20004863.

Council of Science Editors:

Mankin JE. Classification of malware persistence mechanisms using low-artifact disk instrumentation. [Doctoral Dissertation]. Northeastern University; 2013. Available from: http://hdl.handle.net/2047/d20004863

13. Roth, Robin. An Evaluation of Machine Learning Approaches for Hierarchical Malware Classification.

Degree: 2019, , Department of Computer Science

  With an evermore growing threat of new malware that keeps growing in both number and complexity, the necessity for improvement in automatic detection and… (more)

Subjects/Keywords: Machine Learning; Hierarchical Malware Classification; Static Malware Analysis; Mnemonic N-grams; Other Computer and Information Science; Annan data- och informationsvetenskap

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APA (6th Edition):

Roth, R. (2019). An Evaluation of Machine Learning Approaches for Hierarchical Malware Classification. (Thesis). , Department of Computer Science. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18260

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):

Roth, Robin. “An Evaluation of Machine Learning Approaches for Hierarchical Malware Classification.” 2019. Thesis, , Department of Computer Science. Accessed December 16, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18260.

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

MLA Handbook (7th Edition):

Roth, Robin. “An Evaluation of Machine Learning Approaches for Hierarchical Malware Classification.” 2019. Web. 16 Dec 2019.

Vancouver:

Roth R. An Evaluation of Machine Learning Approaches for Hierarchical Malware Classification. [Internet] [Thesis]. , Department of Computer Science; 2019. [cited 2019 Dec 16]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18260.

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

Council of Science Editors:

Roth R. An Evaluation of Machine Learning Approaches for Hierarchical Malware Classification. [Thesis]. , Department of Computer Science; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18260

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

14. Pektaş, Abdurrahman. Behavior based malware classification using online machine learning : Classification des logiciels malveillants basée sur le comportement à l'aide de l'apprentissage automatique en ligne.

Degree: Docteur es, Informatique, 2015, Grenoble Alpes

 Les malwares, autrement dit programmes malicieux ont grandement évolué ces derniers temps et sont devenus une menace majeure pour les utilisateurs grand public, les entreprises… (more)

Subjects/Keywords: Logiciel malveillant; Analyse comportemental; Classification; Apprentissage automatique en ligne; Malware; Behavioral analysis; Classification; Automated; 004

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APA (6th Edition):

Pektaş, A. (2015). Behavior based malware classification using online machine learning : Classification des logiciels malveillants basée sur le comportement à l'aide de l'apprentissage automatique en ligne. (Doctoral Dissertation). Grenoble Alpes. Retrieved from http://www.theses.fr/2015GREAM065

Chicago Manual of Style (16th Edition):

Pektaş, Abdurrahman. “Behavior based malware classification using online machine learning : Classification des logiciels malveillants basée sur le comportement à l'aide de l'apprentissage automatique en ligne.” 2015. Doctoral Dissertation, Grenoble Alpes. Accessed December 16, 2019. http://www.theses.fr/2015GREAM065.

MLA Handbook (7th Edition):

Pektaş, Abdurrahman. “Behavior based malware classification using online machine learning : Classification des logiciels malveillants basée sur le comportement à l'aide de l'apprentissage automatique en ligne.” 2015. Web. 16 Dec 2019.

Vancouver:

Pektaş A. Behavior based malware classification using online machine learning : Classification des logiciels malveillants basée sur le comportement à l'aide de l'apprentissage automatique en ligne. [Internet] [Doctoral dissertation]. Grenoble Alpes; 2015. [cited 2019 Dec 16]. Available from: http://www.theses.fr/2015GREAM065.

Council of Science Editors:

Pektaş A. Behavior based malware classification using online machine learning : Classification des logiciels malveillants basée sur le comportement à l'aide de l'apprentissage automatique en ligne. [Doctoral Dissertation]. Grenoble Alpes; 2015. Available from: http://www.theses.fr/2015GREAM065


University of New Mexico

15. Darling, Michael. A Lexical Approach for Classifying Malicious URLs.

Degree: Electrical and Computer Engineering, 2015, University of New Mexico

 Given the continuous growth of illicit activities on the Internet, there is a need for intelligent systems to identify malicious web pages. It has been… (more)

Subjects/Keywords: Machine Learning; Malware Detection; Classification; Malicious Web Pages; Supervised Learning; Natural Language Processing

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APA (6th Edition):

Darling, M. (2015). A Lexical Approach for Classifying Malicious URLs. (Masters Thesis). University of New Mexico. Retrieved from http://hdl.handle.net/1928/30327

Chicago Manual of Style (16th Edition):

Darling, Michael. “A Lexical Approach for Classifying Malicious URLs.” 2015. Masters Thesis, University of New Mexico. Accessed December 16, 2019. http://hdl.handle.net/1928/30327.

MLA Handbook (7th Edition):

Darling, Michael. “A Lexical Approach for Classifying Malicious URLs.” 2015. Web. 16 Dec 2019.

Vancouver:

Darling M. A Lexical Approach for Classifying Malicious URLs. [Internet] [Masters thesis]. University of New Mexico; 2015. [cited 2019 Dec 16]. Available from: http://hdl.handle.net/1928/30327.

Council of Science Editors:

Darling M. A Lexical Approach for Classifying Malicious URLs. [Masters Thesis]. University of New Mexico; 2015. Available from: http://hdl.handle.net/1928/30327


Brno University of Technology

16. Holop, Patrik. Classification of Potentially Malicious File Clusters via Machine Learning .

Degree: 2019, Brno University of Technology

 Táto práca navrhuje alternatívu súčasných metód klasifikácie malvéru na úrovni súborov, ktoré sú často založené na detekcii špecifických postupností bytov v daných súboroch. Experimentáciou bolo… (more)

Subjects/Keywords: strojové učenie; zhlukovanie; klasifikácia; antivírus; analýza; malvér; machine learning; clustering; classification; antivirus; analysis; malware

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

APA (6th Edition):

Holop, P. (2019). Classification of Potentially Malicious File Clusters via Machine Learning . (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/180236

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):

Holop, Patrik. “Classification of Potentially Malicious File Clusters via Machine Learning .” 2019. Thesis, Brno University of Technology. Accessed December 16, 2019. http://hdl.handle.net/11012/180236.

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

MLA Handbook (7th Edition):

Holop, Patrik. “Classification of Potentially Malicious File Clusters via Machine Learning .” 2019. Web. 16 Dec 2019.

Vancouver:

Holop P. Classification of Potentially Malicious File Clusters via Machine Learning . [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2019 Dec 16]. Available from: http://hdl.handle.net/11012/180236.

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

Council of Science Editors:

Holop P. Classification of Potentially Malicious File Clusters via Machine Learning . [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/180236

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


Brno University of Technology

17. Plaskoň, Pavol. Vysoce výkonná platforma pro účely výzkumu malwaru .

Degree: 2019, Brno University of Technology

 V antivírusových firmách sa denne analyzuje veľké množstvo súborov. Pre podporu ich analýzy a klasifikácie sa používajú rôzne automatizované nástroje. Detekčné definície pre detekciu a… (more)

Subjects/Keywords: analýza malvéru; detekčné definície; klasifikácia; škálovateľnosť; tag; malware analysis; detection definitions; classification; scalability; tagging

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

APA (6th Edition):

Plaskoň, P. (2019). Vysoce výkonná platforma pro účely výzkumu malwaru . (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/180428

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):

Plaskoň, Pavol. “Vysoce výkonná platforma pro účely výzkumu malwaru .” 2019. Thesis, Brno University of Technology. Accessed December 16, 2019. http://hdl.handle.net/11012/180428.

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

MLA Handbook (7th Edition):

Plaskoň, Pavol. “Vysoce výkonná platforma pro účely výzkumu malwaru .” 2019. Web. 16 Dec 2019.

Vancouver:

Plaskoň P. Vysoce výkonná platforma pro účely výzkumu malwaru . [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2019 Dec 16]. Available from: http://hdl.handle.net/11012/180428.

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

Council of Science Editors:

Plaskoň P. Vysoce výkonná platforma pro účely výzkumu malwaru . [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/180428

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


RMIT University

18. Sun, L. REFORM: A framework for malware packer analysis using information theory and statistical methods.

Degree: 2010, RMIT University

Malware (malicious software) is a term used to describe computer viruses, Trojan horses, and other pieces of software that are used to attack computer systems.… (more)

Subjects/Keywords: Fields of Research; malware; packer; packer analysis; packer classification; unpack; pattern recognition; information theory; statistical method

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

APA (6th Edition):

Sun, L. (2010). REFORM: A framework for malware packer analysis using information theory and statistical methods. (Thesis). RMIT University. Retrieved from http://researchbank.rmit.edu.au/view/rmit:4934

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):

Sun, L. “REFORM: A framework for malware packer analysis using information theory and statistical methods.” 2010. Thesis, RMIT University. Accessed December 16, 2019. http://researchbank.rmit.edu.au/view/rmit:4934.

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

MLA Handbook (7th Edition):

Sun, L. “REFORM: A framework for malware packer analysis using information theory and statistical methods.” 2010. Web. 16 Dec 2019.

Vancouver:

Sun L. REFORM: A framework for malware packer analysis using information theory and statistical methods. [Internet] [Thesis]. RMIT University; 2010. [cited 2019 Dec 16]. Available from: http://researchbank.rmit.edu.au/view/rmit:4934.

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

Council of Science Editors:

Sun L. REFORM: A framework for malware packer analysis using information theory and statistical methods. [Thesis]. RMIT University; 2010. Available from: http://researchbank.rmit.edu.au/view/rmit:4934

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


KTH

19. Srinivaasan, Gayathri. Malicious Entity Categorization using Graph modelling.

Degree: Information and Communication Technology (ICT), 2016, KTH

Today, malware authors not only write malicious software but also employ obfuscation, polymorphism, packing and endless such evasive techniques to escape detection by Anti-Virus… (more)

Subjects/Keywords: malware; classification; graph modelling; graph mining; downloader; payload; URL; file sample; graph traversal; malware; klassificering; graf modellering; graf gruvdrift; dataöverföring; nyttolast; URL; fil prov; graf traverse; Computer and Information Sciences; Data- och informationsvetenskap

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

APA (6th Edition):

Srinivaasan, G. (2016). Malicious Entity Categorization using Graph modelling. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-202980

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):

Srinivaasan, Gayathri. “Malicious Entity Categorization using Graph modelling.” 2016. Thesis, KTH. Accessed December 16, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-202980.

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

MLA Handbook (7th Edition):

Srinivaasan, Gayathri. “Malicious Entity Categorization using Graph modelling.” 2016. Web. 16 Dec 2019.

Vancouver:

Srinivaasan G. Malicious Entity Categorization using Graph modelling. [Internet] [Thesis]. KTH; 2016. [cited 2019 Dec 16]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-202980.

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

Council of Science Editors:

Srinivaasan G. Malicious Entity Categorization using Graph modelling. [Thesis]. KTH; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-202980

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

20. 川口, 直人. マルウェアの機能による分類に関する研究.

Degree: Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学

Supervisor:面 和成

情報科学研究科

修士

Subjects/Keywords: マルウェア; Malware; 分類; Classification; 機械学習; Machine Learning

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

APA (6th Edition):

川口, . (n.d.). マルウェアの機能による分類に関する研究. (Thesis). Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10119/12931

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):

川口, 直人. “マルウェアの機能による分類に関する研究.” Thesis, Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学. Accessed December 16, 2019. http://hdl.handle.net/10119/12931.

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):

川口, 直人. “マルウェアの機能による分類に関する研究.” Web. 16 Dec 2019.

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

Vancouver:

川口 . マルウェアの機能による分類に関する研究. [Internet] [Thesis]. Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学; [cited 2019 Dec 16]. Available from: http://hdl.handle.net/10119/12931.

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:

川口 . マルウェアの機能による分類に関する研究. [Thesis]. Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学; Available from: http://hdl.handle.net/10119/12931

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 Central Florida

21. Siddiqui, Muazzam. Data Mining Methods For Malware Detection.

Degree: 2008, University of Central Florida

 This research investigates the use of data mining methods for malware (malicious programs) detection and proposed a framework as an alternative to the traditional signature… (more)

Subjects/Keywords: Data Mining; Malware Detection; Machine Learning; Classification; Instruction Sequences; Signature Extraction; Predictive Modeling; Supervised Learning; Unsupervised Learning; Feature Selection; Feature Reduction; Categorical Data Analysis

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

APA (6th Edition):

Siddiqui, M. (2008). Data Mining Methods For Malware Detection. (Doctoral Dissertation). University of Central Florida. Retrieved from https://stars.library.ucf.edu/etd/3709

Chicago Manual of Style (16th Edition):

Siddiqui, Muazzam. “Data Mining Methods For Malware Detection.” 2008. Doctoral Dissertation, University of Central Florida. Accessed December 16, 2019. https://stars.library.ucf.edu/etd/3709.

MLA Handbook (7th Edition):

Siddiqui, Muazzam. “Data Mining Methods For Malware Detection.” 2008. Web. 16 Dec 2019.

Vancouver:

Siddiqui M. Data Mining Methods For Malware Detection. [Internet] [Doctoral dissertation]. University of Central Florida; 2008. [cited 2019 Dec 16]. Available from: https://stars.library.ucf.edu/etd/3709.

Council of Science Editors:

Siddiqui M. Data Mining Methods For Malware Detection. [Doctoral Dissertation]. University of Central Florida; 2008. Available from: https://stars.library.ucf.edu/etd/3709

22. Siddiqui, Sana. Cognitive artificial intelligence – a complexity based machine learning approach for advanced cyber threats.

Degree: Electrical and Computer Engineering, 2016, University of Manitoba

 Application of machine intelligence is severely challenged in the domain of cyber security due to the surreptitious nature of advanced cyber threats which are persistent… (more)

Subjects/Keywords: Artificial Neural Network; Classification; Multiscale; Cognitive Intelligence; Dimensionality; Wavelets; Machine Intelligence; Fractals; Multifractals; Hebbian Learning; Instance Based Learners; Complexity Analysis; Packet Captures; Network Threats; Malware Detection; Machine Learning; Computational Intelligence; Cognitive Computing; Cognitive Informatics; Cyber Kill Chain; Cyber Threat; Cyber Security; Obfuscated Cyber Threats; Advanced Indistinguishable Threats

…16 1.2.1 Malware… …malware repository [103]........................ 37 Table 2: Processed statistics of… …45 Table 5: Classification performance k-NN - single scale vs. multiscale - Contagio… …65 Table 6: Classification performance ANN - single scale vs. multiscale - UNSW-NB15… …67 Table 7: Classification performance Hebbian - single scale vs. multiscale - Statistical… 

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

APA (6th Edition):

Siddiqui, S. (2016). Cognitive artificial intelligence – a complexity based machine learning approach for advanced cyber threats. (Masters Thesis). University of Manitoba. Retrieved from http://hdl.handle.net/1993/32282

Chicago Manual of Style (16th Edition):

Siddiqui, Sana. “Cognitive artificial intelligence – a complexity based machine learning approach for advanced cyber threats.” 2016. Masters Thesis, University of Manitoba. Accessed December 16, 2019. http://hdl.handle.net/1993/32282.

MLA Handbook (7th Edition):

Siddiqui, Sana. “Cognitive artificial intelligence – a complexity based machine learning approach for advanced cyber threats.” 2016. Web. 16 Dec 2019.

Vancouver:

Siddiqui S. Cognitive artificial intelligence – a complexity based machine learning approach for advanced cyber threats. [Internet] [Masters thesis]. University of Manitoba; 2016. [cited 2019 Dec 16]. Available from: http://hdl.handle.net/1993/32282.

Council of Science Editors:

Siddiqui S. Cognitive artificial intelligence – a complexity based machine learning approach for advanced cyber threats. [Masters Thesis]. University of Manitoba; 2016. Available from: http://hdl.handle.net/1993/32282

.