Prevention of False Data Injection in Dataset using ML and DL

Abstract

IoT is an interconnected and allotted network of embedded systems communicating through wired and wireless communication technology. It is defined as network of physical objects or things empowered with limited computation storage and conversation capabilities as well as embedded with electronics (sensors and actuators). Nowadays there are literally masses of hundreds of Internet of things (IoT) gadgets easily available to the customers. those consist of security cameras, smart home and smart speaker systems, smart toys and infant monitors, drones, domestic appliances, routers and internet gateways, and basically some other hardware products which can transmit information and be controlled over the net. FDIA is an attack this will result in a catastrophic outcomes. False information injection attack can be executed in each dynamic and static datasets, as a present device we easily discover and prevent in a static environment. in which with the present machine even the dynamic records is made static after which detects FDIA’s. FDIA in a static environment is the existing machine, in which we seek to have few solutions for the FDIA in a dynamic environment or for time collection information. So, to enhance protection in dynamic environment is our proposed challenge towards FDIA and we create a brand-new version known as “PdD” model – Predictive Dynamic version. Proposed framework performs crucial role in live streaming records in terms of heterogeneous environment (dynamic nature). So, we use GRU set of rules because it offers better RMSE value than different AI algorithms. This detects every time there may be a few FDI attack.