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.

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