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You searched for subject:(Adversarial attacks). Showing records 1 – 14 of 14 total matches.

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University of Illinois – Urbana-Champaign

1. Agarwal, Rishika. Adversarial attacks and defenses for generative models.

Degree: MS, Computer Science, 2019, University of Illinois – Urbana-Champaign

Adversarial Machine learning is a field of research lying at the intersection of Machine Learning and Security, which studies vulnerabilities of Machine learning models that… (more)

Subjects/Keywords: Adversarial Machine learning; generative models; attacks; defenses

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

APA (6th Edition):

Agarwal, R. (2019). Adversarial attacks and defenses for generative models. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/104943

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):

Agarwal, Rishika. “Adversarial attacks and defenses for generative models.” 2019. Thesis, University of Illinois – Urbana-Champaign. Accessed March 07, 2021. http://hdl.handle.net/2142/104943.

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

MLA Handbook (7th Edition):

Agarwal, Rishika. “Adversarial attacks and defenses for generative models.” 2019. Web. 07 Mar 2021.

Vancouver:

Agarwal R. Adversarial attacks and defenses for generative models. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2019. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/2142/104943.

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

Council of Science Editors:

Agarwal R. Adversarial attacks and defenses for generative models. [Thesis]. University of Illinois – Urbana-Champaign; 2019. Available from: http://hdl.handle.net/2142/104943

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


Texas A&M University

2. He, Yukun. Robust Dynamical Step Adversarial Training Defense for Deep Neural Networks.

Degree: MS, Computer Engineering, 2018, Texas A&M University

 Although machine learning (ML) algorithms show impressive performance on computer vision tasks, neural networks are still vulnerable to adversarial examples. Adversarial examples typically stay indistinguishable… (more)

Subjects/Keywords: Adversarial Attacks; Adversarial Training; PGD; Dynamical Step; Neural Network; Defense

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APA (6th Edition):

He, Y. (2018). Robust Dynamical Step Adversarial Training Defense for Deep Neural Networks. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/174606

Chicago Manual of Style (16th Edition):

He, Yukun. “Robust Dynamical Step Adversarial Training Defense for Deep Neural Networks.” 2018. Masters Thesis, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/174606.

MLA Handbook (7th Edition):

He, Yukun. “Robust Dynamical Step Adversarial Training Defense for Deep Neural Networks.” 2018. Web. 07 Mar 2021.

Vancouver:

He Y. Robust Dynamical Step Adversarial Training Defense for Deep Neural Networks. [Internet] [Masters thesis]. Texas A&M University; 2018. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/174606.

Council of Science Editors:

He Y. Robust Dynamical Step Adversarial Training Defense for Deep Neural Networks. [Masters Thesis]. Texas A&M University; 2018. Available from: http://hdl.handle.net/1969.1/174606


University of Michigan

3. Xiao, Chaowei. Machine Learning in Adversarial Environments.

Degree: PhD, Computer Science & Engineering, 2020, University of Michigan

 Machine Learning, especially Deep Neural Nets (DNNs), has achieved great success in a variety of applications. Unlike classical algorithms that could be formally analyzed, there… (more)

Subjects/Keywords: adversarial machine learning; security; adversarial attacks; adversarial examples; defense; Computer Science; Engineering

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APA (6th Edition):

Xiao, C. (2020). Machine Learning in Adversarial Environments. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/162944

Chicago Manual of Style (16th Edition):

Xiao, Chaowei. “Machine Learning in Adversarial Environments.” 2020. Doctoral Dissertation, University of Michigan. Accessed March 07, 2021. http://hdl.handle.net/2027.42/162944.

MLA Handbook (7th Edition):

Xiao, Chaowei. “Machine Learning in Adversarial Environments.” 2020. Web. 07 Mar 2021.

Vancouver:

Xiao C. Machine Learning in Adversarial Environments. [Internet] [Doctoral dissertation]. University of Michigan; 2020. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/2027.42/162944.

Council of Science Editors:

Xiao C. Machine Learning in Adversarial Environments. [Doctoral Dissertation]. University of Michigan; 2020. Available from: http://hdl.handle.net/2027.42/162944


Vanderbilt University

4. -4180-995X. Enhancing the Robustness of Deep Neural Networks Against Security Threats Using Radial Basis Functions.

Degree: MS, Computer Science, 2020, Vanderbilt University

 The ability of deep neural networks (DNNs) to achieve state-of-the-art performance on complicated tasks has increased their adoption in safety-critical cyber physical systems (CPS). However,… (more)

Subjects/Keywords: Neural Network Robustness; Anomalies; Adversarial Attacks; Poisoning Attacks; Radial Basis Functions

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APA (6th Edition):

-4180-995X. (2020). Enhancing the Robustness of Deep Neural Networks Against Security Threats Using Radial Basis Functions. (Masters Thesis). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/10082

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-4180-995X. “Enhancing the Robustness of Deep Neural Networks Against Security Threats Using Radial Basis Functions.” 2020. Masters Thesis, Vanderbilt University. Accessed March 07, 2021. http://hdl.handle.net/1803/10082.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-4180-995X. “Enhancing the Robustness of Deep Neural Networks Against Security Threats Using Radial Basis Functions.” 2020. Web. 07 Mar 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-4180-995X. Enhancing the Robustness of Deep Neural Networks Against Security Threats Using Radial Basis Functions. [Internet] [Masters thesis]. Vanderbilt University; 2020. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1803/10082.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-4180-995X. Enhancing the Robustness of Deep Neural Networks Against Security Threats Using Radial Basis Functions. [Masters Thesis]. Vanderbilt University; 2020. Available from: http://hdl.handle.net/1803/10082

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


San Jose State University

5. Mani, Nag. On Adversarial Attacks on Deep Learning Models.

Degree: MS, Computer Science, 2019, San Jose State University

  With recent advancements in the field of artificial intelligence, deep learning has created a niche in the technology space and is being actively used… (more)

Subjects/Keywords: image neural net attacks; adversarial retraining techniques; Artificial Intelligence and Robotics

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APA (6th Edition):

Mani, N. (2019). On Adversarial Attacks on Deep Learning Models. (Masters Thesis). San Jose State University. Retrieved from https://doi.org/10.31979/etd.49ee-sknc ; https://scholarworks.sjsu.edu/etd_projects/742

Chicago Manual of Style (16th Edition):

Mani, Nag. “On Adversarial Attacks on Deep Learning Models.” 2019. Masters Thesis, San Jose State University. Accessed March 07, 2021. https://doi.org/10.31979/etd.49ee-sknc ; https://scholarworks.sjsu.edu/etd_projects/742.

MLA Handbook (7th Edition):

Mani, Nag. “On Adversarial Attacks on Deep Learning Models.” 2019. Web. 07 Mar 2021.

Vancouver:

Mani N. On Adversarial Attacks on Deep Learning Models. [Internet] [Masters thesis]. San Jose State University; 2019. [cited 2021 Mar 07]. Available from: https://doi.org/10.31979/etd.49ee-sknc ; https://scholarworks.sjsu.edu/etd_projects/742.

Council of Science Editors:

Mani N. On Adversarial Attacks on Deep Learning Models. [Masters Thesis]. San Jose State University; 2019. Available from: https://doi.org/10.31979/etd.49ee-sknc ; https://scholarworks.sjsu.edu/etd_projects/742


University of Illinois – Chicago

6. Biradar, Sachin G. User-Centric Adversarial Perturbations to Protect Privacy in Social Networks.

Degree: 2019, University of Illinois – Chicago

 Social media has completely pervaded our lives in the past few years with increased accessibility to the internet. They have drastically changed the way people… (more)

Subjects/Keywords: Privacy; Social Networks; Adversarial Attacks; Fooling Algorithm; Graph Convolutional Networks

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

APA (6th Edition):

Biradar, S. G. (2019). User-Centric Adversarial Perturbations to Protect Privacy in Social Networks. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/23723

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):

Biradar, Sachin G. “User-Centric Adversarial Perturbations to Protect Privacy in Social Networks.” 2019. Thesis, University of Illinois – Chicago. Accessed March 07, 2021. http://hdl.handle.net/10027/23723.

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

MLA Handbook (7th Edition):

Biradar, Sachin G. “User-Centric Adversarial Perturbations to Protect Privacy in Social Networks.” 2019. Web. 07 Mar 2021.

Vancouver:

Biradar SG. User-Centric Adversarial Perturbations to Protect Privacy in Social Networks. [Internet] [Thesis]. University of Illinois – Chicago; 2019. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10027/23723.

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

Council of Science Editors:

Biradar SG. User-Centric Adversarial Perturbations to Protect Privacy in Social Networks. [Thesis]. University of Illinois – Chicago; 2019. Available from: http://hdl.handle.net/10027/23723

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


University of Washington

7. Burkard, Cody. Can Intelligent Hyperparameter Selection Improve Resistance to Adversarial Examples?.

Degree: 2017, University of Washington

 Convolutional Neural Networks and Deep Learning classification systems in general have been shown to be vulnerable to attack by specially crafted data samples that appear… (more)

Subjects/Keywords: Adversarial Examples; Adversarial Machine Learning; Evasion Attacks; Machine Learning; Neural Networks; Security; Computer science; To Be Assigned

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APA (6th Edition):

Burkard, C. (2017). Can Intelligent Hyperparameter Selection Improve Resistance to Adversarial Examples?. (Thesis). University of Washington. Retrieved from http://hdl.handle.net/1773/39901

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):

Burkard, Cody. “Can Intelligent Hyperparameter Selection Improve Resistance to Adversarial Examples?.” 2017. Thesis, University of Washington. Accessed March 07, 2021. http://hdl.handle.net/1773/39901.

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

MLA Handbook (7th Edition):

Burkard, Cody. “Can Intelligent Hyperparameter Selection Improve Resistance to Adversarial Examples?.” 2017. Web. 07 Mar 2021.

Vancouver:

Burkard C. Can Intelligent Hyperparameter Selection Improve Resistance to Adversarial Examples?. [Internet] [Thesis]. University of Washington; 2017. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1773/39901.

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

Council of Science Editors:

Burkard C. Can Intelligent Hyperparameter Selection Improve Resistance to Adversarial Examples?. [Thesis]. University of Washington; 2017. Available from: http://hdl.handle.net/1773/39901

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


University of Toronto

8. Bose, Avishek. Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization.

Degree: 2018, University of Toronto

Adversarial attacks involve adding, small, often imperceptible, perturbations to inputs with the goal of getting a machine learning model to misclassifying them. While many different… (more)

Subjects/Keywords: Adversarial Attacks; Computer Vision; Face Detection; Faster R-CNN; Machine Learning; Neural Networks; 0800

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

APA (6th Edition):

Bose, A. (2018). Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/91439

Chicago Manual of Style (16th Edition):

Bose, Avishek. “Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization.” 2018. Masters Thesis, University of Toronto. Accessed March 07, 2021. http://hdl.handle.net/1807/91439.

MLA Handbook (7th Edition):

Bose, Avishek. “Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization.” 2018. Web. 07 Mar 2021.

Vancouver:

Bose A. Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization. [Internet] [Masters thesis]. University of Toronto; 2018. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1807/91439.

Council of Science Editors:

Bose A. Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization. [Masters Thesis]. University of Toronto; 2018. Available from: http://hdl.handle.net/1807/91439


King Abdullah University of Science and Technology

9. Alfarra, Motasem. Applications of Tropical Geometry in Deep Neural Networks.

Degree: Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, 2020, King Abdullah University of Science and Technology

 This thesis tackles the problem of understanding deep neural network with piece- wise linear activation functions. We leverage tropical geometry, a relatively new field in… (more)

Subjects/Keywords: Deep Learning; Deep Neural Networks; Tropical Geometry; Network Pruning; Lottery Ticket Hypothesis; Adversarial Attacks

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

APA (6th Edition):

Alfarra, M. (2020). Applications of Tropical Geometry in Deep Neural Networks. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/662473

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):

Alfarra, Motasem. “Applications of Tropical Geometry in Deep Neural Networks.” 2020. Thesis, King Abdullah University of Science and Technology. Accessed March 07, 2021. http://hdl.handle.net/10754/662473.

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

MLA Handbook (7th Edition):

Alfarra, Motasem. “Applications of Tropical Geometry in Deep Neural Networks.” 2020. Web. 07 Mar 2021.

Vancouver:

Alfarra M. Applications of Tropical Geometry in Deep Neural Networks. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2020. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10754/662473.

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

Council of Science Editors:

Alfarra M. Applications of Tropical Geometry in Deep Neural Networks. [Thesis]. King Abdullah University of Science and Technology; 2020. Available from: http://hdl.handle.net/10754/662473

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


University of Melbourne

10. Yang, Tianci. Multi-observer approach for estimation and control under adversarial attacks.

Degree: 2019, University of Melbourne

 Traditional control systems composed of interconnected controllers, sensors, and actuators use point-to-point communication architectures. This is no longer suitable when new requirements  – such as… (more)

Subjects/Keywords: networked control systems; cyber-physical systems; adversarial attacks; sensor attacks; actuator attacks; control systems; multi-observer; attack detection and isolation; secure estimation

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APA (6th Edition):

Yang, T. (2019). Multi-observer approach for estimation and control under adversarial attacks. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/227777

Chicago Manual of Style (16th Edition):

Yang, Tianci. “Multi-observer approach for estimation and control under adversarial attacks.” 2019. Doctoral Dissertation, University of Melbourne. Accessed March 07, 2021. http://hdl.handle.net/11343/227777.

MLA Handbook (7th Edition):

Yang, Tianci. “Multi-observer approach for estimation and control under adversarial attacks.” 2019. Web. 07 Mar 2021.

Vancouver:

Yang T. Multi-observer approach for estimation and control under adversarial attacks. [Internet] [Doctoral dissertation]. University of Melbourne; 2019. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/11343/227777.

Council of Science Editors:

Yang T. Multi-observer approach for estimation and control under adversarial attacks. [Doctoral Dissertation]. University of Melbourne; 2019. Available from: http://hdl.handle.net/11343/227777


Delft University of Technology

11. Lelekas, Ioannis (author). Top-Down Networks: A coarse-to-fine reimagination of CNNs.

Degree: 2020, Delft University of Technology

 Biological vision adopts a coarse-to-fine information processing pathway, from initial visual detection and binding of salient features of a visual scene, to the enhanced and… (more)

Subjects/Keywords: Computer Vision; Deep Learning; Convolutional Neural Networks; Top-Down; Fine-to-Coarse; Coarse-to-Fine; Adversarial attacks; Adversarial robustness; Gradcam; Object localization

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APA (6th Edition):

Lelekas, I. (. (2020). Top-Down Networks: A coarse-to-fine reimagination of CNNs. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:11888a7b-1e54-424d-9daa-8ff48de58345

Chicago Manual of Style (16th Edition):

Lelekas, Ioannis (author). “Top-Down Networks: A coarse-to-fine reimagination of CNNs.” 2020. Masters Thesis, Delft University of Technology. Accessed March 07, 2021. http://resolver.tudelft.nl/uuid:11888a7b-1e54-424d-9daa-8ff48de58345.

MLA Handbook (7th Edition):

Lelekas, Ioannis (author). “Top-Down Networks: A coarse-to-fine reimagination of CNNs.” 2020. Web. 07 Mar 2021.

Vancouver:

Lelekas I(. Top-Down Networks: A coarse-to-fine reimagination of CNNs. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Mar 07]. Available from: http://resolver.tudelft.nl/uuid:11888a7b-1e54-424d-9daa-8ff48de58345.

Council of Science Editors:

Lelekas I(. Top-Down Networks: A coarse-to-fine reimagination of CNNs. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:11888a7b-1e54-424d-9daa-8ff48de58345


George Mason University

12. Bhat, Sahil. Defense Against Cache Based Micro-architectural Side Channel Attacks .

Degree: George Mason University

 To overcome the performance overheads incurred by the traditional software-based malware detection techniques, Hardware-assisted Malware Detection (HMD) using machine learning (ML) classi ers has emerged… (more)

Subjects/Keywords: side channel attacks; malware detection; hardware performance counters; hardware security; Flush + Reload; adversarial

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APA (6th Edition):

Bhat, S. (n.d.). Defense Against Cache Based Micro-architectural Side Channel Attacks . (Thesis). George Mason University. Retrieved from http://hdl.handle.net/1920/11489

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):

Bhat, Sahil. “Defense Against Cache Based Micro-architectural Side Channel Attacks .” Thesis, George Mason University. Accessed March 07, 2021. http://hdl.handle.net/1920/11489.

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):

Bhat, Sahil. “Defense Against Cache Based Micro-architectural Side Channel Attacks .” Web. 07 Mar 2021.

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

Vancouver:

Bhat S. Defense Against Cache Based Micro-architectural Side Channel Attacks . [Internet] [Thesis]. George Mason University; [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1920/11489.

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:

Bhat S. Defense Against Cache Based Micro-architectural Side Channel Attacks . [Thesis]. George Mason University; Available from: http://hdl.handle.net/1920/11489

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

13. Chen, Lingwei. Intelligent Malware Detection Using File-to-file Relations and Enhancing its Security against Adversarial Attacks.

Degree: PhD, Lane Department of Computer Science and Electrical Engineering, 2019, West Virginia University

  With computing devices and the Internet being indispensable in people's everyday life, malware has posed serious threats to their security, making its detection of… (more)

Subjects/Keywords: Malware Detection; Machine Learning; Data Mining; File-to-file Relations; Adversarial Attacks and Defenses; Information Security

…4.17 Different scenarios of the adversarial attacks. With the direction of the inward arrow… …the adversarial attacks are depicted with the knowledge of (X, D̂), (X, D… …of AdvAttack and other adversarial attacks: OriginalClassifier (0), different… …adversarial attacks (Method 1 - 4) and AdvAttack (5)… …opponents. To be resilient against the malware attacks, we analyze the general adversarial… 

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

APA (6th Edition):

Chen, L. (2019). Intelligent Malware Detection Using File-to-file Relations and Enhancing its Security against Adversarial Attacks. (Doctoral Dissertation). West Virginia University. Retrieved from https://doi.org/10.33915/etd.3844 ; https://researchrepository.wvu.edu/etd/3844

Chicago Manual of Style (16th Edition):

Chen, Lingwei. “Intelligent Malware Detection Using File-to-file Relations and Enhancing its Security against Adversarial Attacks.” 2019. Doctoral Dissertation, West Virginia University. Accessed March 07, 2021. https://doi.org/10.33915/etd.3844 ; https://researchrepository.wvu.edu/etd/3844.

MLA Handbook (7th Edition):

Chen, Lingwei. “Intelligent Malware Detection Using File-to-file Relations and Enhancing its Security against Adversarial Attacks.” 2019. Web. 07 Mar 2021.

Vancouver:

Chen L. Intelligent Malware Detection Using File-to-file Relations and Enhancing its Security against Adversarial Attacks. [Internet] [Doctoral dissertation]. West Virginia University; 2019. [cited 2021 Mar 07]. Available from: https://doi.org/10.33915/etd.3844 ; https://researchrepository.wvu.edu/etd/3844.

Council of Science Editors:

Chen L. Intelligent Malware Detection Using File-to-file Relations and Enhancing its Security against Adversarial Attacks. [Doctoral Dissertation]. West Virginia University; 2019. Available from: https://doi.org/10.33915/etd.3844 ; https://researchrepository.wvu.edu/etd/3844

14. Sanchez Vicarte, Jose Rodrigo. Game of threads: Enabling asynchronous poisoning attacks.

Degree: MS, Electrical & Computer Engr, 2019, University of Illinois – Urbana-Champaign

 As data sizes continue to grow at an unprecedented rate, machine learning training is being forced to adopt asynchronous training algorithms to maintain performance and… (more)

Subjects/Keywords: adversarial machine learning trusted execution environment SGX operating systems multi-processing asynchronous stochastic gradient descent poisoning attacks machine learning

…implications of asynchronous training algorithms on machine learning model integrity in adversarial… …TEEs should in theory defeat integrity attacks given even a supervisor level adversary. For… …conventional poisoning attacks [25, 26, 27, 28, 29, 30], which attempt to influence model… …TEEs, this work introduces asynchronous poisoning attacks (APAs), which show how… …integrity attacks are still possible even when asynchronous training runs within TEEs.1 APAs are… 

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APA (6th Edition):

Sanchez Vicarte, J. R. (2019). Game of threads: Enabling asynchronous poisoning attacks. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/106293

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):

Sanchez Vicarte, Jose Rodrigo. “Game of threads: Enabling asynchronous poisoning attacks.” 2019. Thesis, University of Illinois – Urbana-Champaign. Accessed March 07, 2021. http://hdl.handle.net/2142/106293.

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

MLA Handbook (7th Edition):

Sanchez Vicarte, Jose Rodrigo. “Game of threads: Enabling asynchronous poisoning attacks.” 2019. Web. 07 Mar 2021.

Vancouver:

Sanchez Vicarte JR. Game of threads: Enabling asynchronous poisoning attacks. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2019. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/2142/106293.

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

Council of Science Editors:

Sanchez Vicarte JR. Game of threads: Enabling asynchronous poisoning attacks. [Thesis]. University of Illinois – Urbana-Champaign; 2019. Available from: http://hdl.handle.net/2142/106293

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

.