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Detection of malicious packet dropping attacks in RPL-based internet of things

Authors: Taekyoung Kwon; KyoungHoon Kim; Sooyeon Shin;

Detection of malicious packet dropping attacks in RPL-based internet of things

Abstract

The routing protocol for low-power and lossy networks (RPL) is an IPv6-based routing protocol optimised for internet of things (IoT) environments. However, it is susceptible to malicious packet dropping attacks. If a node with a lower rank that is closer to the root node attempts a malicious packet dropping, it may disrupt basic data transmission or even the entire IoT application service. In this paper, we present a novel detection method for malicious packet dropping attacks against RPL-based networks. The proposed method is based on the anomaly intrusion detection system and detects malicious packet dropping in the presence of normal packet losses. We evaluate the performance of the method on Contiki's network simulator, Cooja. The evaluation results show that the method has good performance in detecting malicious packet dropping attacks. In every case, the successful detection rate is greater than 94% and the false alarm rate is less than 3%.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Top 10%
Average
Average
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