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

Fluxurile ISD in contextul crizei globale

Over time, foreign investments were the decisive factor contributing to the development of nations, but were the hardest hit during economic recessions. Under the circumstances of the world economy globalization and of the quasi-liberalization of international capital movements, leading to a high degree of financial markets interdependence, the impact of the crisis triggered in 2008 on FDI has been severe: decreased investment ability of companies, both on account of restricting access to financial resources and declining corporate profits; increased costs of financing on volatile international capital markets; reduced risk appetite of investors; deteriorated business climate; re-location of investments portfolio and the pressure of sovereign debts

Evaluating User Equipment Based Regional Area Augmentation

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