Towards robust malware detection
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2018
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| Online Access: | http://hdl.handle.net/1721.1/119758 |
| _version_ | 1811085570098593792 |
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| author | Huang, Alex Yangyang |
| author2 | Abdullah Al-Dujaili and Una-May O'Reilly. |
| author_facet | Abdullah Al-Dujaili and Una-May O'Reilly. Huang, Alex Yangyang |
| author_sort | Huang, Alex Yangyang |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. |
| first_indexed | 2024-09-23T13:11:41Z |
| format | Thesis |
| id | mit-1721.1/119758 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T13:11:41Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1197582019-04-12T22:48:24Z Towards robust malware detection Huang, Alex Yangyang Abdullah Al-Dujaili and Una-May O'Reilly. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 45-48). A central challenge of malware detection using machine learning methods is the presence of adversarial variants, small changes to detectable malware that allow it to evade a model (i.e. be classified as benign). We take inspiration from adversarial variant generation methods in the continuous-valued image domain to introduce methods for malware in the binary domain. We incorporate these methods in the training of hardened models towards the goal of robustness against adversarial variants. Additionally, we provide visualization tools for analysis of hardened models. Our tools illustrate the difference in loss behavior between models trained with different methods, the effect of adversarial learning on the loss landscape of a model, and the effect of adversarial learning on the decision map of a model. The adversarial learning framework and the visualization tools in combination allow for the creation and understanding of robust models. by Alex Yangyang Huang. M. Eng. 2018-12-18T19:48:47Z 2018-12-18T19:48:47Z 2018 2018 Thesis http://hdl.handle.net/1721.1/119758 1078699210 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 48 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Huang, Alex Yangyang Towards robust malware detection |
| title | Towards robust malware detection |
| title_full | Towards robust malware detection |
| title_fullStr | Towards robust malware detection |
| title_full_unstemmed | Towards robust malware detection |
| title_short | Towards robust malware detection |
| title_sort | towards robust malware detection |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/119758 |
| work_keys_str_mv | AT huangalexyangyang towardsrobustmalwaredetection |