Bernhards Blumbergs

dblp:183/8437 · DBLP profile ↗
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5ranked-venue papers
2as first author
2since 2021 · last 2024
0000-0001-9679-6282ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 4 · 2 first-author · 2 since 2021
YearPublicationVenuePosition
2024 Black Sheep Wall: Towards Multiple Vantage Point-Based Information Space Situational Awareness
Bernhards Blumbergs
SECRYPT1
2022 Industrial and Automation Control System Cyber Range Prototype for Offensive Capability Development
Austris Uljans, Bernhards Blumbergs
ICISSP2
2019 Remote Exploit Development for Cyber Red Team Computer Network Operations Targeting Industrial Control Systems
abstract
Cyber red teaming and its techniques, tactics and procedures have to be constantly developed to identify, counter and respond to sophisticated threats targeting critical infrastructures. This paper focuses on cyber red team technical arsenal development within conducted fast paced computer network operation case studies against the critical infrastructure operators. Technical attack details are revealed, attack tool released publicly and countermeasures proposed for the critical vulnerabilities found in the industrial devices and highly used communication protocols throughout the Europe. The exploits are developed in a reference system, verified in real cyber red teaming operations, responsibly disclosed to involved entities, and integrated within international cyber defence exercise adversary campaigns.
Bernhards Blumbergs
ICISSP1
2019 TED: A Container based Tool to Perform Security Risk Assessment for ELF Binaries
Daniele Mucci, Bernhards Blumbergs
ICISSP2
2018 An unsupervised framework for detecting anomalous messages from syslog log files
abstract
System logs provide valuable information about the health status of IT systems and computer networks. Therefore, log file monitoring has been identified as an important system and network management technique. While many solutions have been developed for monitoring known log messages, the detection of previously unknown error conditions has remained a difficult problem. In this paper, we present a novel data mining based framework for detecting anomalous log messages from syslog- based system log files. We also describe the implementation and performance of the framework in a large organizational network.
Risto Vaarandi, Bernhards Blumbergs, Markus Kont
NOMS2