Detection of Anomalies in the Robotic System Based on the Calculation of Kullback-Leibler Divergence

Basan, Elena; Basan, Alexander; Makarevich, Oleg

doi:10.1109/cyberc.2019.00064

Cited by 3 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is a simple and versatile tool that reduces the task of describing the statistical difference between two datasets to a single number. Any discrete process or dataset can be modeled using the Probability Mass Function (PMF) [16]. Given It can be seen from Figure 2 that when the attack began, the number of satellites fixed began to change dramatically as shown by the gray line.…”

Section: Preliminary Researchmentioning

confidence: 99%

“…It is a simple and versatile tool that reduces the task of describing the statistical difference between two datasets to a single number. Any discrete process or dataset can be modeled using the Probability Mass Function (PMF) [16]. Given data samples without anomalies, the empirical PMF characterizing "normal" behavior can be estimated using a histogram.…”

Section: Preliminary Researchmentioning

confidence: 99%

“…A large time difference between the clock readings of the receiver and the satellite may indicate spoofing. However, this method requires further study [13,16].…”

mentioning

confidence: 99%

“…In our study, we need to detect the rare occurrence of cyber-physical parameter peak values that do not occur during normal operation of a UAV. Therefore, we also used the Poisson distribution [16,41]. The Poisson model usually describes a scheme of rare events [40,41].…”

mentioning

confidence: 99%

See 3 more Smart Citations

GPS-Spoofing Attack Detection Technology for UAVs Based on Kullback–Leibler Divergence

Basan

Nekrasov

et al. 2021

Drones

Self Cite

View full text Add to dashboard Cite

Here, we developed a method for detecting cyber security attacks aimed at spoofing the Global Positioning System (GPS) signal of an Unmanned Aerial Vehicle (UAV). Most methods for detecting UAV anomalies indicative of an attack use machine learning or other such methods that compare normal behavior with abnormal behavior. Such approaches require large amounts of data and significant “training” time to prepare and implement the system. Instead, we consider a new approach based on other mathematical methods for detecting UAV anomalies without the need to first collect a large amount of data and describe normal behavior patterns. Doing so can simplify the process of creating an anomaly detection system, which can further facilitate easier implementation of intrusion detection systems in UAVs. This article presents issues related to ensuring the information security of UAVs. Development of the GPS spoofing detection method for UAVs is then described, based on a preliminary study that made it possible to form a mathematical apparatus for solving the problem. We then explain the necessary analysis of parameters and methods of data normalization, and the analysis of the Kullback—Leibler divergence measure needed to detect anomalies in UAV systems.

show abstract

Section: Preliminary Researchmentioning

confidence: 99%

“…It is a simple and versatile tool that reduces the task of describing the statistical difference between two datasets to a single number. Any discrete process or dataset can be modeled using the Probability Mass Function (PMF) [16]. Given data samples without anomalies, the empirical PMF characterizing "normal" behavior can be estimated using a histogram.…”

Section: Preliminary Researchmentioning

confidence: 99%

“…A large time difference between the clock readings of the receiver and the satellite may indicate spoofing. However, this method requires further study [13,16].…”

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations