MalFIX: Using IPFIX for Scaling Threat Detection to High Data Rates

DSpace Repositorium (Manakin basiert)

Zur Kurzanzeige

dc.contributor.author Paradzik, Gabriel
dc.contributor.author Steinert, Benjamin
dc.contributor.author Steegmüller, Janik
dc.contributor.author Menth, Michael
dc.date.accessioned 2025-04-03T05:17:12Z
dc.date.available 2025-04-03T05:17:12Z
dc.date.issued 2025-04-03
dc.identifier.uri http://hdl.handle.net/10900/163779
dc.identifier.uri http://nbn-resolving.org/urn:nbn:de:bsz:21-dspace-1637796 de_DE
dc.identifier.uri http://dx.doi.org/10.15496/publikation-105109
dc.description.abstract Threat intelligence feeds provide up-to-date information about threat indicators, i.e., IP addresses, hostnames, etc. This information can be used to identify potentially malicious actors by scanning network traffic. In this paper, we present a high-performance architecture for threat detection that leverages openly available threat intelligence feeds. For that purpose, the open-source tool Maltrail has been modified to make it horizontally scalable and to handle IPFIX flow data. Maltrail was adapted to process IPFIX as input and generate IPFIXcompatible output that includes information about detected threats. These threats are then ingested into Apache Kafka, enabling further analysis and integration with other tools. Benchmark results highlight the scalability of this approach, with a peak processing speed of 300,000 flows per second on 32 CPU cores. en
dc.language.iso en de_DE
dc.publisher Universität Tübingen de_DE
dc.subject.ddc 004 de_DE
dc.title MalFIX: Using IPFIX for Scaling Threat Detection to High Data Rates en
dc.type Article de_DE
utue.publikation.fachbereich Informatik de_DE
utue.publikation.fakultaet 7 Mathematisch-Naturwissenschaftliche Fakultät de_DE
utue.publikation.noppn yes de_DE

Dateien:

Das Dokument erscheint in:

Zur Kurzanzeige