ADS-B Crowd-Sensor Network and Two-Step Kalman Filter for GNSS and ADS-B Cyber-Attack Detection
| Autor: | Gheorghe Sirbu, Mauro Leonardi |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Settore ING-INF/03
Spoofing attack Computer science intrusion detection Real-time computing Jamming 02 engineering and technology Intrusion detection system TP1-1185 security spoofing Biochemistry Article localization Analytical Chemistry crowd sourced network Extended Kalman filter 0203 mechanical engineering EKF 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Instrumentation jamming 020301 aerospace & aeronautics GNSS Chemical technology Kalman filter Atomic and Molecular Physics and Optics GNSS applications 020201 artificial intelligence & image processing False alarm Wireless sensor network ADS-B |
| Zdroj: | Sensors, Vol 21, Iss 4992, p 4992 (2021) Sensors Volume 21 Issue 15 Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| Popis: | Automatic Dependent Surveillance-Broadcast is an Air Traffic Control system in which aircraft transmit their own information (identity, position, velocity, etc.) to ground sensors for surveillance purposes. This system has many advantages compared to the classical surveillance radars: easy and low-cost implementation, high accuracy of data, and low renewal time, but also limitations: dependency on the Global Navigation Satellite System, a simple unencrypted and unauthenticated protocol. For these reasons, the system is exposed to attacks like jamming/spoofing of the on-board GNSS receiver or false ADS-B messages’ injection. After a mathematical model derivation of different types of attacks, we propose the use of a crowd sensor network capable of estimating the Time Difference Of Arrival of the ADS-B messages together with a two-step Kalman filter to detect these attacks (on-board GNSS/ADS-B tampering, false ADS-B message injection, GNSS Spoofing/Jamming). Tests with real data and simulations showed that the algorithm can detect all these attacks with a very high probability of detection and low probability of false alarm. |
| Databáze: | OpenAIRE |
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