DissIdent: A Dissimilarity-based Approach for Improving the Identification of Unknown UAVs

In Unmanned Aerial Vehicles (UAVs), the real-time detection and identification of unauthorized UAVs is a significant challenge to be appropriately addressed. Currently, supervised-based learning models (e.g., Deep Neural Networks) can detect the presence of authorized UAVs with reasonable accuracy. Still, they can not handle properly the wide range of unknown signals in the airspace, mainly their categorization. Clustering techniques (e.g., DBSCAN) can be applied to identify and classify unfamiliar signals. However, the uncertainty regarding the nature of unknown sounds can lead to a large dimensional problem, hampering the performance of these techniques. Given these issues, we proposed DissIdent, a dissimilarity-based method for identifying unknown drones. Our approach takes advantage of the dissimilarity concept, in which a function of proximity maps extensive and multi-dimensional problems to a binary problem. DissIdent can identify patterns from different features through an intelligent workflow, mitigating the trade-off between the traceability and accuracy of massive multi-class problems. We carried out an extensive evaluation of DissIdent, comparing it with eight different approaches. The results pointed out DissIdent as a robust approach to detection and identification tasks, overcoming the compared methods. DissIdent addressed accuracy rates higher than 93% in all scenarios, presenting a concise detection and identification of unauthorized drones.