loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Michael Meinig 1 ; Peter Tröger 2 and Christoph Meinel 1

Affiliations: 1 Hasso-Plattner-Institute (HPI), University of Potsdam, 14482 Potsdam and Germany ; 2 Beuth University of Applied Science, 13353 Berlin and Germany

Keyword(s): Log Files, Anomaly Detection, Rough Sets, Uncertainty, Security.

Related Ontology Subjects/Areas/Topics: Computer-Supported Education ; Enterprise Information Systems ; Information Systems Analysis and Specification ; Information Technologies Supporting Learning ; Modeling of Distributed Systems ; Security ; Security and Privacy ; Software Metrics and Measurement

Abstract: Modern scalable information systems produce a constant stream of log records to describe their activities and current state. This data is increasingly used for online anomaly analysis, so that dependability problems such as security incidents can be detected while the system is running. Due to the constant scaling of many such systems, the amount of processed log data is a significant aspect to be considered in the choice of any anomaly detection approach. We therefore present a new idea for log data reduction called ‘rough logs’. It utilizes rough set theory for reducing the number of attributes being collected in log data for representing events in the system. We tested the approach in a large case study - the experiments showed that data reduction possibilities proposed by our approach remain valid even when the log information is modified due to anomalies happening in the system.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.144.248.24

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Meinig, M.; Tröger, P. and Meinel, C. (2019). Rough Logs: A Data Reduction Approach for Log Files. In Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 978-989-758-372-8; ISSN 2184-4992, SciTePress, pages 295-302. DOI: 10.5220/0007735102950302

@conference{iceis19,
author={Michael Meinig. and Peter Tröger. and Christoph Meinel.},
title={Rough Logs: A Data Reduction Approach for Log Files},
booktitle={Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2019},
pages={295-302},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007735102950302},
isbn={978-989-758-372-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - Rough Logs: A Data Reduction Approach for Log Files
SN - 978-989-758-372-8
IS - 2184-4992
AU - Meinig, M.
AU - Tröger, P.
AU - Meinel, C.
PY - 2019
SP - 295
EP - 302
DO - 10.5220/0007735102950302
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic