The growing interest for the Internet of Things has resulted in the large-scale deployment of Low power and Lossy Networks, such as wireless sensor networks and home automation systems. Today, Low Power and Lossy Networks (LLNs) represent one of the most interesting research areas. They include Wireless Personal Area Networks (WPANs), low-power Power Line Communication (PLC) networks and Wireless Sensor Networks (WSNs). Such networks are often optimized to save energy, support traffic patterns different from the standard unicast communication, run routing protocols over link layers with restricted frame-sizes and many others. These networks have strong constraints in terms of resources (energy, memory, and power) and their communication links are by nature characterized by a high loss rate and a low throughput. Moreover the traffic patterns are not simply point-to-point, but in many cases the devices communicate according to a point-to-multipoint or multipoint-to-point schema. Existing routing protocols for wired networks (OSPF, IS-IS) and for ad-hoc networks (AODV, OLSR) are not suitable to deal with all these requirements. The IETF ROLL working group has proposed a new routing protocol called RPL (Routing Protocol for Low power and Lossy Networks) based on IPv6 and specifically designed for these environments. These RPL-based networks may be exposed to a large variety of attacks, but the deployment of security mechanisms may also be quite expensive in terms of resources.
In LLN’s where the energy is considered to be a weighted constraint, a security anomaly in the network is not affordable; one of the most troublesome with in those anomalies is sinkhole, which could exhaust the individual node to whole network. In sinkhole attack a malicious node advertises an artificial beneficial routing path and attracts many nearby nodes to route traffic through it. In this Thesis we defined different strategies for launching sinkhole attacks. We proposed a hash-based authentication for DODAG root messages such as DIO and behavioral anomaly detection with adjudicated verification for nodes that are suspected to be compromised. Finally, we demonstrate the attack and present some implementation details that emphasize the little effort that an attacker would need to put in order to break into a realistic sensor network.