TY - JOUR
T1 - Multiple intrusion detection in RPL based networks
AU - Belavagi, Manjula C.
AU - Muniyal, Balachandra
N1 - Publisher Copyright:
Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
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U2 - 10.11591/ijece.v10i1.pp467-476
DO - 10.11591/ijece.v10i1.pp467-476
M3 - Article
AN - SCOPUS:85073319829
SN - 2088-8708
VL - 10
SP - 467
EP - 476
JO - International Journal of Electrical and Computer Engineering
JF - International Journal of Electrical and Computer Engineering
IS - 1
ER -