Design and Performance Evaluation of GPS Spoofing Signal Detection Algorithm at RF Spoofing Simulation Environment

“…In general, wireless positioning techniques, including the time of signal arrival (TOA) ( Lim et al., 2021 ), time difference of signal arrival (TDOA) ( Bottigliero et al, 2021 ; Sadhukhan & Das, 2009 ), angle of arrival (AOA) ( Al-Sadoon et al, 2020 ), and RSSI fingerprint-based techniques, affect the efficiency of the positioning systems. The aforementioned techniques are based on different measurements, namely the received signal strength indicator (RSSI), and time of flight (TOF).…”

Section: Introductionmentioning

confidence: 99%

Novel integrated matching algorithm using a deep learning algorithm for Wi-Fi fingerprint-positioning technique in the indoors-IoT era

Asaad

Maghdid

2023

PeerJ Computer Science

View full text Add to dashboard Cite

The Internet-of-Things (IoT) has been used with greater frequency to track peoples’ daily activities, particularly those conducted indoors. Wi-Fi technology has been also been used as an alternative to global navigation satellite system (GNSS) technologies to track indoor activities. The received signal strength indicator (RSSI) is widely used to assist in the positioning of Wi-Fi signals. However, the RSSI-based technique suffers from multipath, non-line-of-sight (NLOS) problems and the fluctuation of RSSI measurements via Wi-Fi chipsets. One of the most well-known RSSI-based approaches is to apply the fingerprinting method to do the positioning. However, the fingerprinting-based form has an additional problem due to the lack of RSSI data samples, specifically in harsh area with a huge number of classes or reference points (RPs) and an unstable matching process algorithm. To mitigate the problems of the RSSI-based fingerprinting approach, this research proposes a novel matching process algorithm called Norm_MSATE_LSTM. We first performed the augmentation process to increase the RSSI data records via the Mean Stander deviation Augmentation TEchnique (MSATE). The RSSI records were normalized (norm), and the long short-term memory (LSTM) technique was applied to estimate the correct positions. Finally, the proposed matching algorithm was compared with the stand-alone matching algorithms, including the weighted k-nearest neighbors (WkNN) and LSTM. The results obtained from the experiments and the simulated experiments using OMNeT++ show that the proposed matching algorithm may improve positioning accuracy by 33.1% and 57.5% when only augmentation and augmentation with normalization are applied, respectively.

show abstract

“…Others have suggested a spoofing detection method that takes advantage of spatial information using a single difference in the carrier phase between two antennas (Montgomery et al 2009, Psiaki et al 2014. Additionally, various detection methods are researched (Konovaltsev et al 2013, JafarniaJahromi et al 2015, Lim et al 2015, Radin et al 2015. However, detection and mitigation are passive methods to deal with spoofing attacks.…”

Section: Introductionmentioning

confidence: 99%

Ranging Performance for Spoofer Localization using Receiver Clock Offset

Lee¹,

Seo²,

Jee³

et al. 2016

Journal of Positioning, Navigation, and Timing

View full text Add to dashboard Cite

In this paper, the performance of ranging measurement, which is generated using two receiver clock offsets in one receiver, was analyzed. A spoofer transmits a counterfeited spoofing signal which is similar to the GPS signal with hostile purposes, so the same tracking technique can be applied to the spoofing signal. The multi-correlator can generate two receiver clock offsets in one receiver. The difference between these two clock offsets consists of the path length from the spoofer to the receiver and the delay of spoofer system. Thus, in this paper, the ranging measurement was evaluated by the spoofer localization performance based on the time-of-arrival (TOA) technique. The results of simulation and real-world experiments show that the position and the system clock offset of the spoofer could be estimated successfully.

show abstract

Design and Performance Evaluation of GPS Spoofing Signal Detection Algorithm at RF Spoofing Simulation Environment

Cited by 5 publications

References 1 publication

Research status and prospect of GNSS anti-spoofing technology}{Research status and prospect of GNSS anti-spoofing technology

Research status and prospect of GNSS anti-spoofing technology}{Research status and prospect of GNSS anti-spoofing technology

Novel integrated matching algorithm using a deep learning algorithm for Wi-Fi fingerprint-positioning technique in the indoors-IoT era

Ranging Performance for Spoofer Localization using Receiver Clock Offset

Contact Info

Product

Resources

About