scholarly journals ADS-B Crowd-Sensor Network and Two-Step Kalman Filter for GNSS and ADS-B Cyber-Attack Detection

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4992
Author(s):  
Mauro Leonardi ◽  
Gheorghe Sirbu
Keyword(s):  
Kalman Filter ◽  
Sensor Network ◽  
Traffic Control ◽  
Low Cost ◽  
Real Data ◽  
Satellite System ◽  
Attack Detection ◽  
Probability Of Detection ◽  
Cyber Attack ◽  
Probability Of False Alarm

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.

scholarly journals Fusion of Scores in a Detection Context Based on Alpha Integration

2015 ◽  
Vol 27 (9) ◽  
pp. 1983-2010 ◽  
Author(s):  
Antonio Soriano ◽  
Luis Vergara ◽  
Bouziane Ahmed ◽  
Addisson Salazar
Keyword(s):  
Simulated Data ◽  
Real Data ◽  
Optimization Methods ◽  
Probability Of Detection ◽  
Superior Performance ◽  
Least Mean Square ◽  
Detector Performance ◽  
Biometric Data ◽  
Probability Of Error ◽  
Probability Of False Alarm

We present a new method for fusing scores corresponding to different detectors (two-hypotheses case). It is based on alpha integration, which we have adapted to the detection context. Three optimization methods are presented: least mean square error, maximization of the area under the ROC curve, and minimization of the probability of error. Gradient algorithms are proposed for the three methods. Different experiments with simulated and real data are included. Simulated data consider the two-detector case to illustrate the factors influencing alpha integration and demonstrate the improvements obtained by score fusion with respect to individual detector performance. Two real data cases have been considered. In the first, multimodal biometric data have been processed. This case is representative of scenarios in which the probability of detection is to be maximized for a given probability of false alarm. The second case is the automatic analysis of electroencephalogram and electrocardiogram records with the aim of reproducing the medical expert detections of arousal during sleeping. This case is representative of scenarios in which probability of error is to be minimized. The general superior performance of alpha integration verifies the interest of optimizing the fusing parameters.

scholarly journals Tight Fusion of a Monocular Camera, MEMS-IMU, and Single-Frequency Multi-GNSS RTK for Precise Navigation in GNSS-Challenged Environments

2019 ◽  
Vol 11 (6) ◽  
pp. 610 ◽  
Author(s):  
Tuan Li ◽  
Hongping Zhang ◽  
Zhouzheng Gao ◽  
Xiaoji Niu ◽  
Naser El-sheimy
Keyword(s):  
Kalman Filter ◽  
Low Cost ◽  
Measurement Model ◽  
Satellite System ◽  
Single Frequency ◽  
Precise Position ◽  
Monocular Camera ◽  
Tightly Coupled ◽  
Self Driving Cars ◽  
Mems Imu

Precise position, velocity, and attitude is essential for self-driving cars and unmanned aerial vehicles (UAVs). The integration of global navigation satellite system (GNSS) real-time kinematics (RTK) and inertial measurement units (IMUs) is able to provide high-accuracy navigation solutions in open-sky conditions, but the accuracy will be degraded severely in GNSS-challenged environments, especially integrated with the low-cost microelectromechanical system (MEMS) IMUs. In order to navigate in GNSS-denied environments, the visual–inertial system has been widely adopted due to its complementary characteristics, but it suffers from error accumulation. In this contribution, we tightly integrate the raw measurements from the single-frequency multi-GNSS RTK, MEMS-IMU, and monocular camera through the extended Kalman filter (EKF) to enhance the navigation performance in terms of accuracy, continuity, and availability. The visual measurement model from the well-known multistate constraint Kalman filter (MSCKF) is combined with the double-differenced GNSS measurement model to update the integration filter. A field vehicular experiment was carried out in GNSS-challenged environments to evaluate the performance of the proposed algorithm. Results indicate that both multi-GNSS and vision contribute significantly to the centimeter-level positioning availability in GNSS-challenged environments. Meanwhile, the velocity and attitude accuracy can be greatly improved by using the tightly-coupled multi-GNSS RTK/INS/Vision integration, especially for the yaw angle.

scholarly journals Spoofing Attack Detection Method for UAV Navigation System

2021 ◽  
Vol 20 (6) ◽  
pp. 1368-1394
Author(s):  
Elena Basan ◽  
Eugene Abramov ◽  
Anatoly Basyuk ◽  
Nikita Sushkin
Keyword(s):  
Low Cost ◽  
Satellite System ◽  
Attack Detection ◽  
Physical Parameters ◽  
Safe Navigation ◽  
Experimental Stand ◽  
Internal Parameters ◽  
Diagnosis Method ◽  
Global Navigation Satellite ◽  
Gps System

An implementation of methods for protecting unmanned aerial vehicles (UAVs) from spoofing attacks of the global positioning system (GPS) to ensure safe navigation is discussed in this paper. The Global Navigation Satellite System (GNSS) is widely used to locate UAVs and is by far the most popular navigation solution. This is due to the simplicity and relatively low cost of this technology, as well as the accuracy of the transmitted coordinates. However, there are many security threats to GPS navigation. Primarily this is due to the nature of the GPS signal, the signal is transmitted in the clear, so an attacker can block or tamper with it. This study analyzes the existing GPS protection methods. As part of the study, an experimental stand and scenarios of attacks on the UAV GPS system were developed. Data from the UAV flight logbook was collected and an analysis of cyber-physical parameters was carried out to see an effect of the attack on the on-board sensors readings. Based on this, a new method for detecting UAV anomalies was proposed, based on an analysis of changes in UAV internal parameters. This self-diagnosis method allows the UAV to independently assess the presence of changes in its subsystems and identify signs of a cyberattack. To detect an attack, the UAV collects data on changes in cyber-physical parameters over a certain period of time, then updates this data. As a result it is necessary for the UAV to determine the degree of difference between the two time series of the collected data. The greater the degree of difference between the updated data and the previous ones, the more likely the UAV is under attack.

scholarly journals Research on Time-Correlated Errors Using Allan Variance in a Kalman Filter Applicable to Vector-Tracking-Based GNSS Software-Defined Receiver for Autonomous Ground Vehicle Navigation

2019 ◽  
Vol 11 (9) ◽  
pp. 1026 ◽  
Author(s):  
Luo ◽  
Li ◽  
Yu ◽  
Xu ◽  
Li ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Precision Agriculture ◽  
Low Cost ◽  
Field Tests ◽  
Satellite System ◽  
Allan Variance ◽  
Correlated Errors ◽  
Ground Vehicle ◽  
Correlated Error ◽  
Vector Tracking

The global navigation satellite system (GNSS) has been applied to many areas, e.g.,the autonomous ground vehicle, unmanned aerial vehicle (UAV), precision agriculture, smart city,and the GNSS-reflectometry (GNSS-R), being of considerable significance over the past few decades.Unfortunately, the GNSS signal performance has the high risk of being reduced by the environmentalinterference. The vector tracking (VT) technique is promising to enhance the robustness in highdynamics as well as improve the sensitivity against the weak environment of the GNSS receiver.However, the time-correlated error coupled in the receiver clock estimations in terms of the VT loopcan decrease the accuracy of the navigation solution. There are few works present dealing with thisissue. In this work, the Allan variance is accordingly exploited to specify a model which is expectedto account for this type of error based on the 1st-order Gauss-Markov (GM) process. Then, it is usedfor proposing an enhanced Kalman filter (KF) by which this error can be suppressed. Furthermore,the proposed system model makes use of the innovation sequence so that the process covariancematrix can be adaptively adjusted and updated. The field tests demonstrate the performance of theproposed adaptive vector-tracking time-correlated error suppressed Kalman filter (A-VTTCES-KF).When compared with the results produced by the ordinary adaptive KF algorithm in terms of the VTloop, the real-time kinematic (RTK) positioning and code-based differential global positioning system(DGPS) positioning accuracies have been improved by 14.17% and 9.73%, respectively. On the otherhand, the RTK positioning performance has been increased by maximum 21.40% when comparedwith the results obtained from the commercial low-cost U-Blox receiver.

scholarly journals Attack Categorisation for IoT Applications in Critical Infrastructures, a Survey

2021 ◽  
Vol 11 (16) ◽  
pp. 7228
Author(s):  
Edward Staddon ◽  
Valeria Loscri ◽  
Nathalie Mitton
Keyword(s):  
Critical Infrastructure ◽  
Attack Detection ◽  
Critical Infrastructures ◽  
Cyber Attack ◽  
Specific System ◽  
Iot Applications ◽  
War Zone ◽  
Clear Vision ◽  
The Internet Of Things ◽  
Do So

With the ever advancing expansion of the Internet of Things (IoT) into our everyday lives, the number of attack possibilities increases. Furthermore, with the incorporation of the IoT into Critical Infrastructure (CI) hardware and applications, the protection of not only the systems but the citizens themselves has become paramount. To do so, specialists must be able to gain a foothold in the ongoing cyber attack war-zone. By organising the various attacks against their systems, these specialists can not only gain a quick overview of what they might expect but also gain knowledge into the specifications of the attacks based on the categorisation method used. This paper presents a glimpse into the area of IoT Critical Infrastructure security as well as an overview and analysis of attack categorisation methodologies in the context of wireless IoT-based Critical Infrastructure applications. We believe this can be a guide to aid further researchers in their choice of adapted categorisation approaches. Indeed, adapting appropriated categorisation leads to a quicker attack detection, identification, and recovery. It is, thus, paramount to have a clear vision of the threat landscapes of a specific system.

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