A Feedforward Neural Network based Model to Predict Sub-optimal Path Attack in IoT-LLNs
Rashmi Sahay, G. Geethakumari, Barsha Mitra · 2020
The Internet of Things achieves its vision to connect all physical devices to the Internet through the Low power and Lossy Networks (LLNs). The LLNs comprise constrained devices like sensors, actuators and RFIDs. Since the IoT environment involves large scale deployment of sensor networks, routing becomes an essential requirement. IPv6 Routing Protocol over Low Power and Lossy Network (RPL) is the most popular routing protocol suggested by the Internet Engineering Task Force (IETF) for the IoT-LLN environment. RPL facilitates communication among the sensor nodes in the IoT-LLNs by organizing them in the form of a Destination Oriented Directed Acyclic Graph (DODAG). The term destination-oriented is derived from the fact that data traffic from all the sensor nodes is destined towards the sink (root) node, which acts as a bridge between the LLNs and the intended IoT application. According to RPL, a node chooses its parent from a set of neighboring nodes based on the rank value advertised by them. The rank of any node is a numeric value which is estimated through an objective function and reflects the path quality offered by a parent node to the sink node. Lesser the rank value, the higher is the path quality in terms of the objective function. In a sub-optimal path attack, a malicious node intentionally chooses a sub-optimal path to the sink node by selecting a parent node with a higher rank value. Since the motive of the attacker node is always to select an inferior parent node, the attack is named as the Worst Parent Attack. In this paper, we analyze the impact of the Worst Parent Attack on the overall performance of the IoT-LLNs. Following the analysis, we propose a mechanism based on the Feedforward Neural Network to predict the worst parent attack in RPL supported IoT-LLNs and to identify the source of the attack.