1,197 research outputs found
Use of supervised machine learning for GNSS signal spoofing detection with validation on real-world meaconing and spoofing data : part I
The vulnerability of the Global Navigation Satellite System (GNSS) open service signals to spoofing and meaconing poses a risk to the users of safety-of-life applications. This risk consists of using manipulated GNSS data for generating a position-velocity-timing solution without the user's system being aware, resulting in presented hazardous misleading information and signal integrity deterioration without an alarm being triggered. Among the number of proposed spoofing detection and mitigation techniques applied at different stages of the signal processing, we present a method for the cross-correlation monitoring of multiple and statistically significant GNSS observables and measurements that serve as an input for the supervised machine learning detection of potentially spoofed or meaconed GNSS signals. The results of two experiments are presented, in which laboratory-generated spoofing signals are used for training and verification within itself, while two different real-world spoofing and meaconing datasets were used for the validation of the supervised machine learning algorithms for the detection of the GNSS spoofing and meaconing
Signal processing techniques for GNSS anti-spoofing algorithms
The Global Navigation Satellite Systems (GNSS) usage is growing at a very high
rate, and more applications are relying on GNSS for correct functioning. With the
introduction of new GNSSs, like the European Galileo and the Chinese Beidou, in
addition to the existing ones, the United States Global Positioning System (GPS)
and the Russian GLONASS, the applications, accuracy of the position and usage of
the signals are increasing by the day.
Given that GNSS signals are received with very low power, they are prone to
interference events that may reduce the usage or decrease the accuracy. From these
interference, the spoofing attack is the one that has drawn major concerns in the
GNSS community. A spoofing attack consist on the transmission of GNSS-like
signals, with the goal of taking control of the receiver and make it compute an
erroneous position and time solution.
In the thesis, we focus on the design and validation of different signal processing
techniques, that aim at detection and mitigation of the spoofing attack effects. These
are standalone techniques, working at the receiver’s level and providing discrimination
of spoofing events without the need of external hardware or communication
links. Four different techniques are explored, each of them with its unique sets of
advantages and disadvantages, and a unique approach to spoofing detection. For
these techniques, a spoofing detection algorithm is designed and implemented, and
its capabilities are validated by means of a set of datasets containing spoofing signals.
The thesis focuses on two different aspects of the techniques, divided as per detection
and mitigation capabilities. Both detection techniques are complementary, their joint
use is explored and experimental results are shown that demonstrate the advantages.
In addition, each mitigation technique is analyzed separately as they require
specialized receiver architecture in order to achieve spoofing detection and mitigation.
These techniques are able to decrease the effects of the spoofing attacks, to the point
of removing the spoofing signal from the receiver and compute navigation solutions
that are not controlled by the spoofer and lead in more accurate end results.
The main contributions of this thesis are: the description of a multidimensional
ratio metric test for distinction between spoofing and multipath effects; the introduction
of a cross-check between automatic gain control measurements and the
carrier to noise density ratio, for distinction between spoofing attacks and other
interference events; the description of a novel signal processing method for detection
and mitigation of spoofing effects, based on the use of linear regression algorithms;
and the description of a spoofing detection algorithm based on a feedback tracking
architecture
Recommended from our members
A Testbed for Developing and Evaluating GNSS Signal Authentication Techniques
An experimental testbed has been created for developing
and evaluating Global Navigation Satellite System (GNSS)
signal authentication techniques. The testbed advances the state
of the art in GNSS signal authentication by subjecting candidate
techniques to the strongest publicly-acknowledged GNSS spoofing
attacks. The testbed consists of a real-time phase-coherent GNSS
signal simulator that acts as spoofer, a real-time softwaredefined
GNSS receiver that plays the role of defender, and
post-processing versions of both the spoofer and defender. Two
recently-proposed authentication techniques are analytically and
experimentally evaluated: (1) a defense based on anomalous
received power in a GNSS band, and (2) a cryptographic
defense against estimation-and-replay-type spoofing attacks. The
evaluation reveals weaknesses in both techniques; nonetheless,
both significantly complicate a successful GNSS spoofing attackAerospace Engineering and Engineering Mechanic
Recommended from our members
GNSS Signal Authentication via Power and Distortion Monitoring
We propose a simple low-cost technique that enables
civil Global Positioning System (GPS) receivers and other civil
global navigation satellite system (GNSS) receivers to reliably
detect carry-off spoofing and jamming. The technique, which
we call the Power-Distortion detector, classifies received signals
as interference-free, multipath-afflicted, spoofed, or jammed
according to observations of received power and correlatio
n
function distortion. It does not depend on external hardware or
a network connection and can be readily implemented on many
receivers via a firmware update. Crucially, the detector can with
high probability distinguish low-power spoofing from ordinary
multipath. In testing against over 25 high-quality empirical data
sets yielding over 900,000 separate detection tests, the detector
correctly alarms on all malicious spoofing or jamming attack
s
while maintaining a
<0.5% single-channel false alarm rate.Aerospace Engineering and Engineering Mechanic
An autonomous GNSS anti-spoofing technique
open3siIn recent years, the problem of Position, Navigation and Timing (PNT) resiliency has received significant attention due to an increasing awareness on threats and the vulnerability of the current GNSS signals. Several proposed solutions make uses of cryptography to protect against spoofing. A limitation of cryptographic techniques is that they introduce a communication and processing computation overhead and may impact the performance in terms of availability and continuity for GNSS users. This paper introduces autonomous non cryptographic antispoofing mechanisms, that exploit semi-codeless receiver techniques to detect spoofing for signals with a component making use of spreading code encryption.openCaparra, Gianluca; Wullems, Christian; Ioannides, Rigas T.Caparra, Gianluca; Wullems, Christian; Ioannides, Rigas T
Location Estimation and Recovery using 5G Positioning: Thwarting GNSS Spoofing Attacks
The availability of cheap GNSS spoofers can prevent safe navigation and
tracking of road users. It can lead to loss of assets, inaccurate fare
estimation, enforcing the wrong speed limit, miscalculated toll tax, passengers
reaching an incorrect location, etc. The techniques designed to prevent and
detect spoofing by using cryptographic solutions or receivers capable of
differentiating legitimate and attack signals are insufficient in detecting
GNSS spoofing of road users. Recent studies, testbeds, and 3GPP standards are
exploring the possibility of hybrid positioning, where GNSS data will be
combined with the 5G-NR positioning to increase the security and accuracy of
positioning. We design the Location Estimation and Recovery(LER) systems to
estimate the correct absolute position using the combination of GNSS and 5G
positioning with other road users, where a subset of road users can be
malicious and collude to prevent spoofing detection. Our Location Verification
Protocol extends the understanding of Message Time of Arrival Codes (MTAC) to
prevent attacks against malicious provers. The novel Recovery and Meta Protocol
uses road users' dynamic and unpredictable nature to detect GNSS spoofing. This
protocol provides fast detection of GNSS spoofing with a very low rate of false
positives and can be customized to a large family of settings. Even in a
(highly unrealistic) worst-case scenario where each user is malicious with a
probability of as large as 0.3, our protocol detects GNSS spoofing with high
probability after communication and ranging with at most 20 road users, with a
false positive rate close to 0. SUMO simulations for road traffic show that we
can detect GNSS spoofing in 2.6 minutes since its start under moderate traffic
conditions
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