Joint detection of malicious domains and infected clients
- Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps, and data volumes are observable. The detection problems are coupled, because infected clients tend to interact with malicious domains. Traffic data can be collected at a large scale, and antivirus tools can be used to identify infected clients in retrospect. Domains, by contrast, have to be labeled individually after forensic analysis. We explore transfer learning based on sluice networks; this allows the detection models to bootstrap each other. In a large-scale experimental study, we find that the model outperforms known reference models and detects previously unknown malware, previously unknown malware families, and previously unknown malicious domains.
Verfasserangaben: | Paul PrasseORCiDGND, Rene Knaebel, Lukas Machlica, Tomas PevnyORCiD, Tobias SchefferORCiD |
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DOI: | https://doi.org/10.1007/s10994-019-05789-z |
ISSN: | 0885-6125 |
ISSN: | 1573-0565 |
Titel des übergeordneten Werks (Englisch): | Machine learning |
Verlag: | Springer |
Verlagsort: | Dordrecht |
Publikationstyp: | Wissenschaftlicher Artikel |
Sprache: | Englisch |
Datum der Erstveröffentlichung: | 25.02.2019 |
Erscheinungsjahr: | 2019 |
Datum der Freischaltung: | 18.11.2020 |
Freies Schlagwort / Tag: | Computer security; Https traffic; Machine learning; Neural networks; Traffic data |
Band: | 108 |
Ausgabe: | 8-9 |
Seitenanzahl: | 16 |
Erste Seite: | 1353 |
Letzte Seite: | 1368 |
Fördernde Institution: | Cisco RD |
Organisationseinheiten: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik und Computational Science |
DDC-Klassifikation: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme |