The Routing Protocol for low power Lossy networks (RPL) is a critical operational component of low power wireless personal area networks using IPv6 (6LoWPANs). In this paper we propose a Reinforcement Learning (RL) based IDS to detect various attacks on RPL in 6LoWPANs, including several unaddressed by current research. The proposed scheme can also detect previously unseen attacks and the presence of mobile intruders. The scheme is well suited to the resource constrained environments of our target networks.
<p>The Routing Protocol for low power Lossy networks (RPL) is a
critical operational component of low power wireless personal area networks
using IPv6 (6LoWPANs). In this paper we propose a Reinforcement Learning (RL)
based IDS to detect various attacks on RPL in 6LoWPANs, including several
unaddressed by current research. The proposed scheme can also detect previously
unseen attacks and the presence of mobile intruders. The scheme is well suited
to the resource constrained environments of our target networks.</p><br>
IPv6 over Low-powered Wireless Personal Area Networks (6LoWPAN) have grown in importance in recent years, with the Routing Protocol for Low Power and Lossy Networks (RPL) emerging as a major enabler. However, RPL can be subject to attack, with severe consequences. Most proposed IDSs have been limited to specific RPL attacks and typically assume a stationary environment. In this article, we propose the first adaptive hybrid IDS to efficiently detect and identify a wide range of RPL attacks (including DIO Suppression, Increase Rank, and Worst Parent attacks, which have been overlooked in the literature) in evolving data environments. We apply our framework to networks under various levels of node mobility and maliciousness. We experiment with several incremental machine learning (ML) approaches and various 'concept-drift detection' mechanisms (e.g. ADWIN, DDM, and EDDM) to determine the best underlying settings for the proposed scheme.
<p>The Routing Protocol for low power Lossy networks (RPL) is a
critical operational component of low power wireless personal area networks
using IPv6 (6LoWPANs). In this paper we propose a Reinforcement Learning (RL)
based IDS to detect various attacks on RPL in 6LoWPANs, including several
unaddressed by current research. The proposed scheme can also detect previously
unseen attacks and the presence of mobile intruders. The scheme is well suited
to the resource constrained environments of our target networks.</p><br>
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