Summary Today, with the growth of the Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs) can play a significant role in terms of stability, reliability, connectivity, and coverage. The integration of UAVs and IoT led to the Internet of Flying Things (IoFT). Internet of drones (IoD) is an infrastructure that provides access and control over the Internet between users and drones. One of the most critical challenges in IoFT and IoD is security. In this article, a systematic review is proposed to analyze the existing literature in the field of IoFT and IoD security. We explain the research methodology including the article selection process and the search queries. Thirteen articles were selected out of 166 articles that were published between 2019 and September 2021. Furthermore, the security approaches in the selected articles are classified into five main categories: security vulnerability and eavesdropping, security realization by Blockchain, secure trajectory and secure communication link, authentication and privacy approaches, and security‐enhanced resource allocation. The main ideas, advantages, disadvantages, the employed tools, and evaluation parameters of each selected article are also discussed in detail. Finally, we point out the open issues and orientations of future researches.

Summary Social networking services are online platforms that are distributed across different computers over long distances. Twitter is the most popular microblogging site that allows users to share their opinions and real‐world events. Due to its popularity and ease of use, Twitter has also attracted spammers. As a result, spam detection is one of the most critical problems. In order to provide a spam‐free environment, it is necessary to identify and filter spam tweets as well as their owners. A hybrid method, which is based on Synthetic Minority Over‐sampling TEchnique (SMOTE) and Differential Evolution (DE) strategies, is presented to enhance the spam detection rate in real Twitter datasets. SMOTE is applied to tackle the imbalanced class distribution of datasets, while DE is used to tune Random Forest (RF) hyperparameters. Compared with related work and based on evaluation results, the presented method significantly enhances the classification performance in imbalanced datasets. The detection rate of optimized RF with excellent F1‐score and Area Under the Receiver Operating Characteristic Curve (AUROC), which are 98.97% and 0.999, respectively, demonstrates the high efficiency of the proposed method.