RPL rank based‐attack mitigation scheme in IoT environment

Abstract

Summary Due to the new technologies of the Internet of Things (IoT), connected objects have become smart interactive devices. However, despite their imminent advantages, whether for the daily life of users or in professional contexts, and the various security solutions proposed in literature in particular, the security of IoT networks is still a paramount issue given to the amount of confidential data that circulates through these technologies. In other words, among the IoT protocols that suffer from security issues, routing protocol for low power and lossy networks (RPL) is considered to be one of the most widely used protocols in the deployment of constrained IoT networks with limited resources (storage and energy constraints for example). On this basis, this article investigates in the first part a security flaw in the RPL protocol by carrying out an attack against the rank value of the RPL in an IoT environment. In the second part, a mitigation scheme to avoid it using a trust threshold is proposed. The malicious behavior of the attack and the effectiveness of the proposed scheme were assessed by detailed simulations using the Cooja simulator, in terms of successful detection rate, average network hops and global energy consumption. To conclude, a fair comparison between the different simulation results has been adopted using the Friedman statistical test, and a discussion has been presented with some future works concerning security against rank attack.

Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security

“…The simulation was run four times with 30, 60, 90, or 120 nodes. We compared the proposed mechanism (MMTM-RPL) with DCTM-RPL [28], RBAM-IoT [29], RPL-MRC [30], and DSH-RPL [25]. The number of nodes is shown on the x-axis, and the network lifetime is shown in minutes on the y-axis.…”

“…However, these proposed mechanisms have high message and energy overheads. A method for detecting rank attacks [29] targeted the latter process in RPL topologies. A trust threshold was established based on the rankings of surrounding nodes.…”

“…Figure 5 shows the average residual energy for 30, 60, 90, or 120 nodes in millijoules. Again, MMTM-RPL is compared with DCTM-RPL [28], RBAM-IoT [29], RPL-MRC [30], and DSH-RPL [25]. The number of nodes is shown on the x-axis and the average residual energy is shown on the y-axis for the three types of attack.…”

“…As mentioned earlier, the ratio of malicious and benign nodes is 1:100; i.e., we introduced malicious nodes chronologically as 3, 6, 9, and 12 in the network of 30, 60, 90, and 120 nodes. Again, MMTM-RPL is compared with DCTM-RPL [28], RBAM-IoT [29], RPL-MRC [30], and DSH-RPL [25]. The number of nodes is shown on the x-axis, and the number of messages is shown on the y-axis for the three types of attack.…”

“…It is calculated as the difference between the time when an attack was identified and the time when traffic began. We compared our proposed mechanism (MMTM-RPL) with DCTM-RPL [28], RBAM-IoT [29], RPL-MRC [30], and DSH-RPL [25]. The results are shown in Figure 8 for the three types of attack.…”