Poisoning Cyberattacks to Design of Artificial Intelligence
The MIT Lincoln Laboratory project Poisoning Cyberattacks to Design of Artificial Intelligence (PoCyDAIn) aims to develop a framework for assessing the impact of poisoning attacks on cyber-ML systems.
| Format: | Article |
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| Language: | en_US |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/1721.1/153202 |
| _version_ | 1811078324902952960 |
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| collection | MIT |
| description | The MIT Lincoln Laboratory project Poisoning Cyberattacks to Design of Artificial Intelligence (PoCyDAIn) aims to develop a framework for assessing the impact of poisoning attacks on cyber-ML systems. |
| first_indexed | 2024-09-23T10:57:48Z |
| format | Article |
| id | mit-1721.1/153202 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:57:48Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | mit-1721.1/1532022023-12-19T03:43:59Z Poisoning Cyberattacks to Design of Artificial Intelligence Machine Learning The MIT Lincoln Laboratory project Poisoning Cyberattacks to Design of Artificial Intelligence (PoCyDAIn) aims to develop a framework for assessing the impact of poisoning attacks on cyber-ML systems. 2023-12-18T21:48:25Z 2023-12-18T21:48:25Z 2023-12-18 Article https://hdl.handle.net/1721.1/153202 en_US The Bulletin; Attribution-NoDerivs 3.0 United States http://creativecommons.org/licenses/by-nd/3.0/us/ application/pdf |
| spellingShingle | Machine Learning Poisoning Cyberattacks to Design of Artificial Intelligence |
| title | Poisoning Cyberattacks to Design of Artificial Intelligence |
| title_full | Poisoning Cyberattacks to Design of Artificial Intelligence |
| title_fullStr | Poisoning Cyberattacks to Design of Artificial Intelligence |
| title_full_unstemmed | Poisoning Cyberattacks to Design of Artificial Intelligence |
| title_short | Poisoning Cyberattacks to Design of Artificial Intelligence |
| title_sort | poisoning cyberattacks to design of artificial intelligence |
| topic | Machine Learning |
| url | https://hdl.handle.net/1721.1/153202 |