Melola Discovery

← Melola Discovery

Open access Jurnal SINTA 1

Multiple intrusion detection in RPL based networks

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 10 No. 1 (2020)

DOI
10.11591/ijece.v10i1.pp467-476
ISSN
2088-8708, 2088-8708
Published
2020-02-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

Routing Protocol for Low Power and Lossy Networks based networks consists of large number of tiny sensor nodes with limited resources. These nodes are directly connected to the Internet through the border router. Hence these nodes are susceptible to different types of attacks. The possible attacks are rank attack, selective forwarding, worm hole and Denial of service attack. These attacks can be effectively identified by intrusion detection system model. The paper focuses on identification of multiple intrusions by considering the network size as 10, 40 and 100 nodes and adding 10%, 20% and 30% of malicious nodes to the considered network. Experiments are simulated using Cooja simulator on Contiki operating system. Behavior of the network is observed based on the percentage of inconsistency achieved, energy consumption, accuracy and false positive rate. Experimental results show that multiple intrusions can be detected effectively by machine learning techniques.

Collaboration network of the authors

11 authors · 21 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Manipal Academy of Higher Education
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Nisha P. ShettyManipal Academy of Higher Education3
Arshia AnandManipal Academy of Higher Education1
Sushant KumarManipal Academy of Higher Education1
Sushant PrabhuManipal Academy of Higher Education1
Saleh MowlaManipal Academy of Higher Education1
Chetana PujariManipal Academy of Higher Education1
C. B. ChandrakalaManipal Academy of Higher Education1
Nandini ProothiManipal Academy of Higher Education1
Bhavya GopalManipal Academy of Higher Education1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Multiple intrusion detection in RPL based networks</dc:title><dc:creator>Belavagi, Manjula C.</dc:creator><dc:creator>Muniyal, Balachandra</dc:creator><dc:description>&lt;jats:p&gt;&amp;lt;span lang="EN-US"&amp;gt;Routing Protocol for Low Power and Lossy Networks based networks consists of large number of tiny sensor nodes with limited resources. These nodes are directly connected to the Internet through the border router. Hence these nodes are susceptible to different types of attacks. The possible attacks are rank attack, selective forwarding, worm hole and Denial of service attack. These attacks can be effectively identified by intrusion detection system model. The paper focuses on identification of multiple intrusions by considering the network size as 10, 40 and 100 nodes and adding 10%, 20% and 30% of malicious nodes to the considered network. Experiments are simulated using Cooja simulator on Contiki operating system. Behavior of the network is observed based on the percentage of inconsistency achieved, energy consumption, accuracy and false positive rate. Experimental results show that multiple intrusions can be detected effectively by machine learning techniques.&amp;lt;/span&amp;gt;&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2020-02-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v10i1.pp467-476</dc:identifier><dc:identifier>10.11591/ijece.v10i1.pp467-476</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 10 No 1 (2020); 467</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/19067/13462</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>