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

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.

Publication Details

Journal
Universitätsbibliothek Tübingen
Published
2025-04-03
DOI
https://doi.org/10.15496/publikation-105109
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Universitätsbibliothek Tübingen
Network Security and Intrusion Detection
article

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

article en

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.

Universitätsbibliothek Tübingen
Peace, Justice and strong institutions
Openalex Percentile: Top 53%
Network Security and Intrusion Detection
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.