Pitfalls in Machine Learning for Computer Security.
This article presents ten common pitfalls of machine learning in the context of computer security. Pitfalls are included from all stages of the machine learning process including data snooping, inappropriate baseline, and base rate fallacy. Next, the prevalence of each pitfall was assessed using thi...
| Publicado en: | Communications of the ACM Vol. 67; no. 11; pp. 104 - 113 |
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| Autores principales: | , , , , , , , |
| Formato: | Artículo |
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Association for Computing Machinery
Nov2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=180795999&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 180795999 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Nov2024 vid: 67 iid: 11 pid: 68 pub: Association for Computing Machinery artinfo: ui: 180795999 10.1145/3643456 ppf: 104 ppct: 9 formats: tig: atl: Pitfalls in Machine Learning for Computer Security. aug: au: Arp, Daniel Quiring, Erwin Pendlebury, Feargus Warnecke, Alexander Pierazzi, Fabio Wressnegger, Christian Cavallaro, Lorenzo Rieck, Konrad affil: The Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany Technische Universität Berlin, Berlin, Germany Ruhr University Bochum, Bochum, Germany International Computer Science Institute (ICSI), Berkeley, USA University College London, London, England, United Kingdom King's College London, London, England, United Kingdom KASTEL Security Research Labs, Karlsruhe, Germany Karlsruhe Institute of Technology, Karlsruhe, Germany su: Machine learning Computer security Malware Systems design Acquisition of data Intrusion detection systems (Computer security) sug: subj: Machine learning Computer security Malware Systems design Acquisition of data Intrusion detection systems (Computer security) ab: This article presents ten common pitfalls of machine learning in the context of computer security. Pitfalls are included from all stages of the machine learning process including data snooping, inappropriate baseline, and base rate fallacy. Next, the prevalence of each pitfall was assessed using thirty security papers published in the last ten years. Then, an impact analysis is presented of these pitfalls in four different security fields, including vulnerability and network intrusion detection. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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