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Continued ubiquity of communication infrastructure across Internet of Things (IoT) ecosystems has seen persistent advances of dynamic, intelligent, virtualised sensing and actuation. This has led to effective interaction across the connected ecosystem of -things. Furthermore, this has enabled the creation of smart environments that has created the need for the development of different IoT protocols that support the relaying of information across billions of electronic devices over the Internet. That notwithstanding, the phenomenon of virtual sensors that are supported by IoT technologies like Wireless Sensor Networks (WSNs), RFID, WIFI, Bluetooth, ZigBee, IEEE 802.15.4, etc., emulates physical sensors, and enables more efficient resource management through the dynamic allocation of virtual sensor resources. A distinctive example of this has been the proposition of the Dynamic Intelligent Virtual Sensors (DIVS). This DIVS concept is a novel proposition that allows sensing to be done by the use of logical instances through the use of labeled data. This allows for making accurate predictions during data fusion. However, a potential security attack on DIVS may end up providing false labels during the User Feedback Process (UFP), which may interfere with the accuracy of DIVS. This paper investigates the threat landscape in DIVS when employed in IoT ecosystems, in order to identify the extent to which the severity of these threats may hinder accurate prediction of DIVS in IoT, based on labeled data.