The Holy Grail of 'Systems for Machine Learning'
| Autor: | Ignacio Arnaldo, Kalyan Veeramachaneni |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Focus (computing)
business.industry Computer science Geography Planning and Development 02 engineering and technology Machine learning computer.software_genre Holy Grail Workflow Software deployment 020204 information systems 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences 020201 artificial intelligence & image processing Use case Artificial intelligence business computer Water Science and Technology Cyber threats |
| Zdroj: | ACM SIGKDD Explorations Newsletter. 21:39-47 |
| ISSN: | 1931-0153 1931-0145 |
| Popis: | Although there is a large corpus of research focused on using machine learning to detect cyber threats, the solutions presented are rarely actually adopted in the real world. In this paper, we discuss the challenges that currently limit the adoption of machine learning in security operations, with a special focus on label acquisition, model deployment, and the integration of model findings into existing investigation workflows. Moreover, we posit that the conventional approach to the development of machine learning models, whereby researchers work offline on representative datasets to develop accurate models, is not valid for many cybersecurity use cases. Instead, a different approach is needed: to integrate the creation and maintenance of machine learning models into security operations themselves. |
| Databáze: | OpenAIRE |
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