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

Submitted by abfox on Tue, 02/14/2017 - 1:52pm

Title	"Beyond gut instincts: Understanding, rating and comparing self-learning IDSs"
Publication Type	Conference Paper
Year of Publication	2015
Authors	M. Wurzenberger, F. Skopik, G. Settanni, R. Fiedler
Conference Name	2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA)
Date Published	June
Publisher	IEEE
ISBN Number	978-0-9932-3380-7
Accession Number	15311444
Keywords	advanced persistent threat, advanced persistent threats, Analytical models, APT, BAESE system, Complexity theory, customer infrastructures, customer network data, Cyber Attacks, Data models, economy vital backbone, ICT networks, Intrusion detection, Intrusion Detection Systems, Organizations, pubcrawl170101, Safety, security of data, self-learning IDS
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.
URL	: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7166117&isnumber=7166109
DOI	10.1109/CyberSA.2015.7166117
Citation Key	7166117

Groups:

Science of Security VO