Today, drone-based attacks represent serious threats to the security and safety of public infrastructures. For successfully detecting a malicious drone in a given zone, there are three phases: signal collection (sensing), features extraction and classifications. Signal collection can be performed using available sensing technologies such as radar, acoustics sensors and electro-optic technologies, among others. The classification phase is often achieved using general-purpose algorithms such as Naive Bayes and support vector machine (SVM). On the other hand, the features extraction phase is very problem-specific, and its performance depends on several factors such as the used sensory technology, environment, and the drone characteristics. Features engineering is a designing stage that aims at identifying the most distinctive information carriers which capture the drone's discriminative characteristics. In this paper, we present effective drones' features extraction techniques for the most popular sensory technologies available which are radar, RF analyzers, acoustic sensors, and electro-optic sensors. We focus on identifying the most distinctive features of drones and show how to extract them out of the collected signals.