Title: Entropy-Based Characterization of Internet Background Radiation
Language: English
Authors: Iglesias Vazquez, Félix 
Zseby, Tanja 
Category: Original Research Article
Issue Date: 2015
Journal: Entropy
ISSN: 1099-4300
Network security requires real-time monitoring of network traffic in order to detect new and unexpected attacks. Attack detection methods based on deep packet inspection are time consuming and costly, due to their high computational demands. This paper proposes a fast, lightweight method to distinguish different attack types observed in an IP darkspace monitor. The method is based on entropy measures of traffic-flow features and machine learning techniques. The explored data belongs to a portion of the Internet background radiation from a large IP darkspace, i.e., real traffic captures that exclusively contain unsolicited traffic, ongoing attacks, attack preparation activities and attack aftermaths. Results from an in-depth traffic analysis based on packet headers and content are used as a reference to label data and to evaluate the quality of the entropy-based classification. Full IP darkspace traffic captures from a three-week observation period in April, 2012, are used to compare the entropy-based classification with the in-depth traffic analysis. Results show that several traffic types present a high correlation to the respective traffic-flow entropy signals and can even fit polynomial regression models. Therefore, sudden changes in traffic types caused by new attacks or attack preparation activities can be identified based on entropy variations.
Keywords: network security; information entropy; time series analysis; supervised classification; signal modeling
DOI: 10.3390/e17010074
Library ID: AC11359410
URN: urn:nbn:at:at-ubtuw:3-48
Organisation: E389 - Institute of Telecommunications 
Publication Type: Article
Appears in Collections:Article

Files in this item:

Page view(s)

checked on Jul 29, 2021


checked on Jul 29, 2021

Google ScholarTM


This item is licensed under a Creative Commons License Creative Commons