TY  - INPR
A1  - Prasse, Paul
A1  - Gruben, Gerrit
A1  - Machlika, Lukas
A1  - Pevny, Tomas
A1  - Sofka, Michal
A1  - Scheffer, Tobias
T1  - Malware Detection by HTTPS Traffic Analysis
N2  - 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.
KW  - machine learning
KW  - computer security
Y1  - 2017
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-100942
ER  - 
TY  - JOUR
A1  - Prasse, Paul
A1  - Knaebel, Rene
A1  - Machlica, Lukas
A1  - Pevny, Tomas
A1  - Scheffer, Tobias
T1  - Joint detection of malicious domains and infected clients
JF  - Machine learning
N2  - 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.
KW  - Machine learning
KW  - Neural networks
KW  - Computer security
KW  - Traffic data
KW  - Https traffic
Y1  - 2019
U6  - https://doi.org/10.1007/s10994-019-05789-z
SN  - 0885-6125
SN  - 1573-0565
VL  - 108
IS  - 8-9
SP  - 1353
EP  - 1368
PB  - Springer
CY  - Dordrecht
ER  -