Refine
Has Fulltext
- yes (309)
Year of publication
Document Type
- Preprint (309) (remove)
Language
- English (299)
- German (7)
- Multiple languages (2)
- French (1)
Is part of the Bibliography
- no (309) (remove)
Keywords
- elliptic operators (9)
- boundary value problems (8)
- index (8)
- K-theory (7)
- manifolds with singularities (6)
- pseudodifferential operators (6)
- relative index (6)
- Atiyah-Patodi-Singer theory (5)
- Fredholm property (5)
- index theory (5)
Institute
- Institut für Mathematik (283)
- Interdisziplinäres Zentrum für Dynamik komplexer Systeme (26)
- Institut für Physik und Astronomie (20)
- Department Psychologie (1)
- Institut für Informatik und Computational Science (1)
- Institut für Religionswissenschaft (1)
- Institut für Slavistik (1)
- Mathematisch-Naturwissenschaftliche Fakultät (1)
In order to evade detection by network-traffic analysis, a growing proportion of malware uses the encrypted HTTPS protocol. We explore the problem of detecting malware on client computers based on HTTPS traffic analysis. In this setting, malware has to be detected based on the host IP address, ports, timestamp, and data volume information of TCP/IP packets that are sent and received by all the applications on the client. We develop a scalable protocol that allows us to collect network flows of known malicious and benign applications as training data and derive a malware-detection method based on a neural networks and sequence classification. We study the method's ability to detect known and new, unknown malware in a large-scale empirical study.