SeReNity - Evidence-Based Security Response Centers


Universität Oldenburg
Fakultät II – Department für Informatik
Abteilung Safety-Security-Interaction
26111 Oldenburg


Ingrid Ahlhorn

+49 (0) 441 - 798 2426

A05 1-134

Uhlhornsweg 84,  26129 Oldenburg

SeReNity - Evidence-Based Security Response Centers

SeReNity - Evidence-Based Security Response Centers

(dies ist ein Projekt zusammen mit dem Forschungsteam an der Universität Twente in den Niederlanden)

Prompt and timely response to incoming cyber-attacks and incidents is a core requirement for business
continuity and safe operations for organizations operating at all levels (commercial, governmental,
military). The effectiveness of these measures is significantly limited (and oftentimes defeated altogether)
by the inefficiency of the attack identification and response process which is, effectively, a show-stopper
for all attack prevention and reaction activities. The cognitive-intensive, human-driven alarm analysis
procedures currently employed by Security Operation Centres are made ineffective (as opposed to only
inefficient) by the sheer amount of alarm data produced, and the lack of mechanisms to automatically and
soundly evaluate the arriving evidence to build operable risk-based metrics for incident response.

This project will build foundational technologies to achieve Security Response Centres (SRC) based on
three key components: (1) risk-based systems for alarm prioritization, (2) real-time, human-centric
procedures for alarm operationalization, and (3) technology integration in response operations.

In doing so, SeReNity will develop new techniques, methods, and systems at the intersection of the Design
and Defence domains to deliver operable and accurate procedures for efficient incident response. To
achieve this, this project will develop semantically and contextually rich alarm data to inform risk-based
metrics on the mounting evidence of incoming cyber-attacks (as opposed to firing an alarm for each match
of an IDS signature). SeReNity will achieve this by means of advanced techniques from machine learning
and information mining and extraction, to identify attack patterns in the network traffic, and automatically
identify threat types.

Importantly, SeReNity will develop new mechanisms and interfaces to present the gathered evidence to
SRC operators dynamically, and based on the specific threat (type) identified by the underlying technology.
To achieve this, this project unifies Dutch excellence in intrusion detection, threat intelligence, and humancomputer
interaction with an industry-leading partner operating in the market of tailored solutions for
Security Monitoring.

(Stand: 19.01.2024)  | 
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