A Lightweight Cooperative Intrusion Detection System for RPL-based IoT
The successful deployment of an Intrusion Detection System (IDS) in the Internet of Things (IoT) is subject to two primary criteria: the detection method and the deployment strategy. IDS schemes should take into account that IoT devices often have limited resources. Thus, IDS should be limited in de...
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Published in | Wireless personal communications Vol. 134; no. 4; pp. 2235 - 2258 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
New York
Springer US
01.02.2024
Springer Nature B.V |
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Online Access | Get full text |
ISSN | 0929-6212 1572-834X |
DOI | 10.1007/s11277-024-11009-2 |
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Abstract | The successful deployment of an Intrusion Detection System (IDS) in the Internet of Things (IoT) is subject to two primary criteria: the detection method and the deployment strategy. IDS schemes should take into account that IoT devices often have limited resources. Thus, IDS should be limited in devices’ memory and power usage. In this paper, we design, implement, and evaluate an effective cross-layer lightweight IDS scheme for the IoT (RPL-IDS). The proposed IDS scheme cooperates with the RPL routing protocol using its selected parents as distributed agents. A lightweight artificial neural network (ANN) model is deployed in these agents to detect malicious traffic and collaborates with a centralized system. According to the topology built by the Routing Protocol for Low-Power and Lossy Networks (RPL), these agents are automatically selected, i.e., the routers (parents) of the topology are chosen to act as IDS agents. We implemented RPL-IDS using the Contiki operating system and then comprehensively evaluated it with the Cooja simulator. Experimental results indicate that RPL-IDS is lightweight and can be deployed on devices with limited resources. Most state-of-the-art IDS schemes do not consider the limitation of resources of IoT devices, making them impractical for deployment in many IoT applications. Furthermore, the proposed RPL-IDS demonstrated one of the highest detection rates in the literature while incurring an insignificant energy overload, allowing for scalability in large-scale networks. |
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AbstractList | The successful deployment of an Intrusion Detection System (IDS) in the Internet of Things (IoT) is subject to two primary criteria: the detection method and the deployment strategy. IDS schemes should take into account that IoT devices often have limited resources. Thus, IDS should be limited in devices’ memory and power usage. In this paper, we design, implement, and evaluate an effective cross-layer lightweight IDS scheme for the IoT (RPL-IDS). The proposed IDS scheme cooperates with the RPL routing protocol using its selected parents as distributed agents. A lightweight artificial neural network (ANN) model is deployed in these agents to detect malicious traffic and collaborates with a centralized system. According to the topology built by the Routing Protocol for Low-Power and Lossy Networks (RPL), these agents are automatically selected, i.e., the routers (parents) of the topology are chosen to act as IDS agents. We implemented RPL-IDS using the Contiki operating system and then comprehensively evaluated it with the Cooja simulator. Experimental results indicate that RPL-IDS is lightweight and can be deployed on devices with limited resources. Most state-of-the-art IDS schemes do not consider the limitation of resources of IoT devices, making them impractical for deployment in many IoT applications. Furthermore, the proposed RPL-IDS demonstrated one of the highest detection rates in the literature while incurring an insignificant energy overload, allowing for scalability in large-scale networks. |
Author | Perazzo, Pericle Ravi, Vinayakumar Azzaoui, Hanane Alazab, Mamoun Boukhamla, Akram Zine Eddine |
Author_xml | – sequence: 1 givenname: Hanane orcidid: 0000-0002-7503-6775 surname: Azzaoui fullname: Azzaoui, Hanane email: azzaoui.hanane1994@gmail.com organization: Laboratoire d’INtelligence Artificielle et des Technologies de l’Information (LINATI), University of Kasdi Merbah – sequence: 2 givenname: Akram Zine Eddine surname: Boukhamla fullname: Boukhamla, Akram Zine Eddine organization: Laboratoire d’INtelligence Artificielle et des Technologies de l’Information (LINATI), University of Kasdi Merbah – sequence: 3 givenname: Pericle surname: Perazzo fullname: Perazzo, Pericle organization: Department of Information Engineering, University of Pisa – sequence: 4 givenname: Mamoun surname: Alazab fullname: Alazab, Mamoun organization: Charles Darwin University – sequence: 5 givenname: Vinayakumar surname: Ravi fullname: Ravi, Vinayakumar organization: Center for Artificial Intelligence, Prince Mohammad Bin Fahd University |
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CitedBy_id | crossref_primary_10_1080_23742917_2024_2447124 crossref_primary_10_1016_j_iot_2025_101531 crossref_primary_10_1016_j_comnet_2025_111185 crossref_primary_10_1007_s10462_024_10907_y |
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Snippet | The successful deployment of an Intrusion Detection System (IDS) in the Internet of Things (IoT) is subject to two primary criteria: the detection method and... |
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SubjectTerms | Artificial neural networks Communications Engineering Computer Communication Networks Engineering Internet of Things Intrusion detection systems Lightweight Memory devices Network topologies Networks Power management Routers Routing (telecommunications) Signal,Image and Speech Processing |
Title | A Lightweight Cooperative Intrusion Detection System for RPL-based IoT |
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