Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
DETONAR-Light: An IoT Network Intrusion Detection Using DETONAR without a Sniffer Network
Uppsala University, Sweden.
RISE Research Institutes of Sweden, Digital Systems, Data Science.ORCID iD: 0000-0003-4044-4207
Università di Bologna, Italy.
NTNU, Norway.
Show others and affiliations
2024 (English)In: Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349, Vol. 14399 LNCS, p. 198-213Article in journal (Refereed) Published
Abstract [en]

The Internet of Things is expanding and since IoT devices and IoT networks are used in many crucial areas in modern societies, ranging from security and military applications to healthcare monitoring and production efficiency, the need to secure these devices is of great importance. Intrusion detection systems (IDS) play a significant role in securing IoT networks as their goal is to detect intruders that have gained access to one or several IoT nodes. While most IDS have been designed to detect a specific or at most a few attacks, the DETONAR framework detects multiple attacks. However, it is run on a designated sniffer network which adds additional cost in terms of hardware and maintenance. In this paper, we propose DETONAR-Light, adapting DETONAR to run using data collected at a border router rather than on sniffer logs. Our experiments show that this is possible almost without any decrease of detection and attack classification rate for many attacks

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2024. Vol. 14399 LNCS, p. 198-213
Keywords [en]
Internet of things; Intrusion detection; Network security; Production efficiency; Additional costs; Attack classifications; Border routers; Classification rates; Healthcare monitoring; Intrusion Detection Systems; Network intrusion detection; Production efficiency; Military applications
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-72877DOI: 10.1007/978-3-031-54129-2_12Scopus ID: 2-s2.0-85188663361OAI: oai:DiVA.org:ri-72877DiVA, id: diva2:1854699
Conference
International Workshops which were held in conjunction with 28th European Symposium on Research in Computer Security, ESORICS 2023. The Hague, Neherlands. 25 September 2023 through 29 September 2023
Available from: 2024-04-26 Created: 2024-04-26 Last updated: 2024-07-28Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Finne, NiclasVoigt, Thiemo

Search in DiVA

By author/editor
Finne, NiclasVoigt, Thiemo
By organisation
Data Science
In the same journal
Lecture Notes in Computer Science
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 297 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf