@inproceedings{224a9f72856c4373a5c6f9011d078909,
title = "Poster abstract: Themis: A data-driven approach to bot detection",
abstract = "We propose Themis, a bot detection approach based on the inference of the structure of time varying IP-to-IP communication with the Stochastic Block Model (SBM). Themis uses the inferred structure to detect and quantify abnormal behavior of individual hosts. The novelty of our approach is the use of probabilistic inference directly on host interactions to model normality. The challenges of our approach are the adaptation of the inference process to obtain usable outputs in a dynamic system, and the specification of abnormal behavior with respect to the inferred structure. Themis identifies infected hosts with accuracy larger 95 % and compares favorably against state of the art botnet detection mechanisms.",
keywords = "Bot Detection, Cyber Security, Probabilistic Inference, Stochastic Block Model, Unsupervised Learning",
author = "Patrick Kalmbach and Andreas Blenk and Wolfgang Kellerer and Stefan Schmid",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Conference on Computer Communications Workshops, INFOCOM 2018 ; Conference date: 15-04-2018 Through 19-04-2018",
year = "2018",
month = jul,
day = "6",
doi = "10.1109/INFCOMW.2018.8406870",
language = "English",
series = "INFOCOM 2018 - IEEE Conference on Computer Communications Workshops",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--2",
booktitle = "INFOCOM 2018 - IEEE Conference on Computer Communications Workshops",
}