In this paper, we propose RECOUP, a reliable group communication routing protocol for IoT networks. RECOUP efficiently uses a low-overhead cluster-based multicast routing technique on top of the RPL protocol. RECOUP increases the probability of message delivery to the intended destination(s), irrespective of the network size and faults (such as broken links or non-responsive nodes), and in the presence of misbehaving nodes.We show that the cluster-based routing mechanism of RECOUP remains robust in presence of various topology (i.e., rank and sybil) and data communication (i.e., blackhole, wormhole, and jamming) attacks targeting the IoT networking infrastructure.An implementation of RECOUP is realized in Contiki. Our results show the effectiveness of RECOUP over state-of-art protocols concerning packet delivery ratio to 25 , end-to-end delay down to 100 ms, low radio transmissions required for per packet delivery to 6 mJ, and most importantly, it improves the robustness and scalability of data communication process in thewhole network.
Authors in @cite_2 investigates the forwarding misbehaviour (i.e., selective packet discarding) and propose a countermeasure of the same in LLNs running with RPL protocol. The basic idea is to monitor the forwarding (mis)behaviour of each node to observe the packet loss rate, and then compare the packet loss rate of the parent node with the neighbor nodes. To ensure that the packet loss is due to misbehaviour and not due to bad channel quality, the nodes use one time retransmission techniques. Similarly, using the monitoring information of the nodes about the data packets forwarding, a trust-based intrusion detection system based on RPL is presented in @cite_27 , to countermeasure mobile sybil attacks in IoT networks.
@cite_27 @cite_2
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Low power and lossy networks (LLNs) are rapidly emerging as an important part of ubiquitous computing, and serving as a major building block for the communication infrastructure in the presence of Internet-of-Things (IoT). Routing protocol for low power and lossy networks (RPL) is a novel routing protocol standardized to enable the integration of resources-constrained devices into the Internet. However, due to the shared radio medium, the lack of physical protection and security requirements of inherent routing protocol, low power and lossy networks are admittedly threatened by diverse Denial-of-Service (DoS) attacks that primarily disrupt network protocols and interfere with on-going communications. In this paper, we propose a monitor-based approach, called CMD, to mitigate forwarding misbehaviors in LLNs running with RPL, where single or multiple malicious nodes randomly or strategically drop any incoming Data packet. The basic idea of the CMD is that each node monitors the forwarding behaviors of the preferred parent node to observe the packet loss rate, compares the observation result with the collected packet loss rate from one-hop neighbor nodes, and detects the forwarding misbehaviors of the preferred parent node. We evaluate the proposed scheme through extensive simulation experiments using OMNeT++ and compare its performance with the original RPL protocol and the existing two-step detection scheme. The simulation results show that the proposed scheme can not only improve the detection rate and packet delivery ratio (PDR) but also can reduce the energy consumption and isolation latency. The next-generation Internet promises to provide a fundamental shift in the underlying architecture to support dynamic deployment of network protocols. With the introduction of programmability and dynamic protocol deployment in routers, potential vulnerabilities and attacks are expected to increase. In this paper, we consider the problem of detecting packet forwarding misbehavior in routers. Specifically, we focus on an attack scenario, where a router selectively drops packets destined for another node. Detecting such an attack is challenging since it requires differentiating malicious packet drops from congestion-based packet losses. We propose a controller-based misbehavior detection technique that effectively detects malicious routers using a hash-based delay sampling and verification. We provide a performance analysis of the detection accuracy and quantify the performance overhead of our system. Our results show that our technique provides accurate detection with low sampling rates. In Sybil attacks, a physical adversary takes multiple fabricated or stolen identities to maliciously manipulate the network. These attacks are very harmful for Internet of Things (IoT) applications. In this paper we implemented and evaluated the performance of RPL (Routing Protocol for Low-Power and Lossy Networks) routing protocol under mobile sybil attacks, namely SybM, with respect to control overhead, packet delivery and energy consumption. In SybM attacks, Sybil nodes take the advantage of their mobility and the weakness of RPL to handle identity and mobility, to flood the network with fake control messages from different locations. To counter these type of attacks we propose a trust-based intrusion detection system based on RPL. Some misbehavior detection and reputation systems in mobile ad-hoc networks rely on the dissemination of information of observed behavior, which makes them vulnerable to false accusations. This vulnerability could be removed by forbidding the dissemination of information on observed behavior in the first place, but, as we show here, this has more drawbacks than a solution that allows dissemination and copes with false accusations. We propose a method for reducing the impact of false accusations. In our approach, nodes collect first-hand information about the behavior of other nodes by direct observation. In addition, nodes maintain a rating about every other node that they care about, in the form of a continuous variable per node. From time to time nodes exchange their first-hand information with others, but, using the Bayesian approach we designed and present in this paper, only second-hand information that is not incompatible with the current rating is accepted. Ratings are slightly modified by accepted information. The reputation of a given node is the collection of ratings maintained by others about this node. By means of simulation we evaluated the robustness of our approach against several types of adversaries that spread false information, and its efficiency at detecting malicious nodes. The simulation results indicate that our system largely reduces the impact of false accusations, while still benefiting from the accelerated detection of malicious nodes provided by second-hand information. We also found that when information dissemination is not used, the time until malicious nodes are detected can be unacceptable.