Machine learning in vulnerability assessment / Delfin Montuno.: D68-3/067-2019E-PDF
"Machine Learning (ML) is increasingly being applied in vulnerability assessment and more generally in providing cyber security. We review ML applications in both those areas by commercial vendors. We also review recent results in adversarial learning; since ML requires training data to be effective, it is susceptible to adversarial attacks in which that data is poisoned to impair the ML’s functionality or allow attackers to bypass it. As a result of this adversarial nature of the problem, we conclude that the automated nature of ML-based solutions increases the need for accurate ground truth input data, and that more research is required to ensure the safety and effectiveness of these approaches with human-machine cooperation in mind"--Abstract, page i.
Permanent link to this Catalogue record:
publications.gc.ca/pub?id=9.881081&sl=0
| Department/Agency | Defence R&D Canada. Ottawa Research Centre. Solan Networks. |
|---|---|
| Title | Machine learning in vulnerability assessment / Delfin Montuno. |
| Series title | Contract report ; DRDC-RDDC-2019-C067 |
| Publication type | Series - View Master Record |
| Language | [English] |
| Format | Electronic |
| Electronic document | |
| Note(s) | "Can unclassified." "April 2019." Title from cover. "PSPC Contract Number: W7714-115274/001/SV." Includes bibliographical references (pages 11-15). Includes abstract in French. |
| Publishing information | Ottawa : Defence Research and Development Canada = Recherche et développement pour la défense Canada, 2019. ©2018 |
| Author / Contributor | Montuno, Delfin Y., author. |
| Description | 1 online resource (18 pages). |
| Catalogue number |
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| Subject terms | Information technology Military technology |
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