Identification and Mitigation of Black Hole Attack in Wireless Sensor Networks

Abstract

Wireless sensor networks are made up of many inexpensive nodes geographically distributed over a certain area where the sensor node components include sensors, actuators, processing unit and storage, location tracking and power supply unit. These types of networks have large range of applications that include environmental pollution, vehicle and building safety etc. In sensor networks, the nodes are tiny in size, cheap and light weighted with narrow computing capability and often deployed in inaccessible areas where securing these kinds of networks usually leads towards the complexity. This issue of complexity made sensor networks more prone to the diverse security attacks, in which black hole attack or packet drop attack which is quite common and harmful attack that affects the network layer. In black hole attack the adversary controls the sensor node and drops the entire set of packets that are forwarded to it. In this research, the technique of knowledge based learning is used for detection and mitigation of those harmful nodes from the network responsible for activating the attack. For simulation, network simulator NS2 is used under Ubuntu 12.0.4 OS.