The Internet of Things (IoT) refers to connecting physical items with software and sensors which enable them to connect and allocate data. This technology also authorizes the collection and allocation of data from a vast network, revealing the path for improved systems of automation. New security issues have arisen because of the transversal and pervasive nature of IoT systems and the numerous substances engaged in their implementation. One such vulnerability is the False Data Injection Attack (FDIA) which fabricates or manipulates the data to deceive the sensors of the device to show erroneous readings. The FDIA is the most destructive to data networks such as IoT. Most current systems for dealing with this attack ignore data verification, particularly on the clustering. The objective of a successful FDIA is to cause severe device damage and profit financially. For this, the attacker needs to identify some specific targets in the system that should be exploited. In this research, we suggest a mechanism to induce FDIA, as well as detection and mitigation strategies such as Sec-IoT and CONsensus-based Data FIlteriNg for IoT (CONFINIT) to prevent future FDIA from emerging and disrupting IoT.