Beyond gut instincts: Understanding, rating and comparing self-learning IDSs

Author

Wurzenberger, Markus ; Skopik, Florian ; Settanni, Giuseppe ; Fiedler, Roman

Author_Institution

Digital Safety & Security Dept., AIT Austrian Inst. of Technol., Vienna, Austria

fYear

2015

fDate

8-9 June 2015

Firstpage

1

Lastpage

1

Abstract

Today ICT networks are the economy´s vital backbone. While their complexity continuously evolves, sophisticated and targeted cyber attacks such as Advanced Persistent Threats (APTs) become increasingly fatal for organizations. Numerous highly developed Intrusion Detection Systems (IDSs) promise to detect certain characteristics of APTs, but no mechanism which allows to rate, compare and evaluate them with respect to specific customer infrastructures is currently available. In this paper, we present BAESE, a system which enables vendor independent and objective rating and comparison of IDSs based on small sets of customer network data.

Keywords

security of data; APT; BAESE system; ICT networks; advanced persistent threats; customer infrastructures; customer network data; cyber attacks; economy vital backbone; intrusion detection systems; self-learning IDS; Analytical models; Complexity theory; Data models; Intrusion detection; Organizations; Safety;

fLanguage

English

Publisher

ieee

Conference_Titel

Cyber Situational Awareness, Data Analytics and Assessment (CyberSA), 2015 International Conference on

Conference_Location

London

Type

conf

DOI

10.1109/CyberSA.2015.7166117

Filename

7166117