DnD: Filtering False Data Injection Attacks in Wireless Sensor Network
Wireless sensor network is an emerging field nowadays that integrates the sensors, network communication and information perception together. The sensors read the real time data and send it to a centralized control unit for data processing that helps us to bring out the essential information for learning and prediction. Suppose the sensor sends the false data, it affects the data processing at the centralized control unit which leads to ineffective learning and wrong prediction. Generally the false data are generated by the compromised node or malicious node in the network and sends them constantly in the network. This attack consumes the limited resources of WSN and decreases the available bandwidth of the network considerably. Hence it is necessary to detect and prevent such a malicious node from the network. Statistical En-Route Filtering (SEF) and Dynamic En-Route Filtering (DEF) are used to detect the false data injection attack. But these techniques are robust in detecting the false data but they fail to detect the malicious nodes which generate the false data. Therefore in this paper, we propose the DnD (Detect and Drop) technique that not even detecting the false data but also preventing the malicious node to send the false data by isolating them from the network. In DnD the a key pool is generated and assigned to each sensor node for encrypting the data and a ID pool is generated and assigned to each sensor node that helps the sink node to validate the sensor node. Using them, the false data and the source that sends the false data are detected. The experimental results show that the performance is increased by a significant margin.