Detecting False Data Injections in Images Collected by Drones: A Deep Learning Approach

Drones are gaining high popularity for their beneficial use in civilian applications and smart cities. Capable of being structured in networks, they can be used to collect several types of data, such as images, and be sent to centers for further processing. At the same time, they also become a new target for multiple types of attacks, among them False Data Injection (FDI), Denial of Service, GPS Spoofing, etc. Therefore, designing new systems and defense mechanisms against these attacks becomes urgent and necessary. In this paper, we emphasize the dangerous nature of the so-called False Data Injection (FDI) and describe a method based on deep learning for its detection. Considered a severe and powerful attack, an injection of false data into the data (images) collected by the drones can considerably alter a final decision that the processing center may take. To fight against this attack, our proposal relies on image analysis and classification using a deep learning approach. After scaling the received image to fit the classifier, using nearest neighbor interpolation (NNI), we feed a convolutional neural network (CNN) to perform image classification. At the end, we compare each class of classification results to a neighborhood using the Mahalanobis distance. Numerical results obtained on the existing dataset [1] demonstrate that our proposal performs well, regardless of the image size, showing an accuracy of 99.21%, a precision of 99.12%, a recall of 99.05%, and an F-score of 0.992%.