Random Number Generator Using Sensors for Drone

Abstract

In recent years, drones have been widely used in many areas such as farming, movie making, surveillance and delivery. So, there is a need to protect these drones against security attacks including hijacking, spying and theft of stored data through the utilization of security mechanisms. Cryptographic keys are needed to operate these security mechanisms, and they must be generated by using random number generators which create unpredictable and non-regenerable random numbers. However, existing random number generators used in drones are not tailored for drones specifically as they use random sources generated on a desktop, not a drone. Recently, random number generators utilizing sensors in mobile phones and IoT (Internet of Things) devices have been studied, but are not appropriate for drones. In this paper, considering that drone sensors must be applied to flight and stationary modes, we proposed a drone specific random number generator called DroneRNG and implemented it. Then, we showed that our DroneRNG passed all of the NIST randomization tests and possesses better statistical properties and unpredictability than random number generators that are currently used in drones.

Sensor-Based Entropy Source Analysis and Validation for Use in IoT Environments

“…In experiment 10, we analyzed the performance and power consumption of the proposed solution. This kind of analyze was performed before in several papers [28,30,32]. A comparison with the results presented in these studies is difficult to achieve because both the throughput and power consumption are calculated relatively to full entropy bits generated and none of the studies presented use our entropy estimation methodology.…”

“…These aspects may rise doubts on the accuracy of the results obtained using other types of entropy estimation methodologies. In other papers [30][31][32][33], the authors chose to evaluate entropy after processing the raw data with different algorithms. By doing this, they can obtain overestimated values for entropy, depending on the statistical properties of the used algorithms.…”

“…Seong-Min Cho et al propose [30] a random number generator, specially designed for drones, with seed extracted from data sensors used for flight: accelerometer, gyroscope and barometer. They used only bits with entropy for seeding the RNG.…”

“…In this paper, we analyzed and validated a sensor-based entropy source. This approach, in which random data is collected from sensors, has also been addressed in several papers [27][28][29][30][31][32][33][34][35][36]. The test methodology used in the analysis of entropy sources is varied and often incomplete.…”

A New Image Encryption Algorithm Based on Multi Chaotic System

“…As a random key, a chaotic sequence can be used, which can produce same encryption effect as first time, and it cannot, in principle, be broken [5]. In the field of information security, chaotic encryption technology has thus been widely used, especially in the field of image encryption [6] [7]. The technology for image scrambling (shuffling) is to enhance the robustness of the hidden carrier by rearranging the matrix of the image pixel and removing the image matrix association [8].…”