Flow-Data Gathering Using NetFlow Sensors for Fitting Malicious-Traffic Detection Models.

ABSTRACT: Advanced persistent threats (APTs) are a growing concern in cybersecurity. Many companies and governments have reported incidents related to these threats. Throughout the life cycle of an APT, one of the most commonly used techniques for gaining access is network attacks. Tools based on machine learning are effective in detecting these attacks. However, researchers usually have problems with finding suitable datasets for fitting their models. The problem is even harder when flow data are required. In this paper, we describe a framework to gather flow datasets using a NetFlow sensor. We also present the Docker-based framework for gathering netflow data (DOROTHEA), a Docker-based solution implementing the above framework. This tool aims to easily generate taggable network traffic to build su

SUBMITTER: Campazas-Vega A 

PROVIDER: S-EPMC7766632 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature