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In this work, we present Model Acquisition and Maintenance Tool (MAMAT) for modeling and risk analysis of large-scale ICT systems. It was developed as part of ADAX research project and applied to electronic payment infrastructure for case studies. We also describe the R&D results and lessons learned during this research effort.

Approximately a thousand artificial satellites operate in orbit around their parent's bodies. These man-made celestial objects are employed for observation, communication, navigation, weather forecast and research purposes. Sending high accuracy ephemeris and clock offset data to Earth; navigation satellites help receivers estimate their location and precise time. The accuracy of receivers' location information and transmitting time data depends on compensable and noncompensable errors. Compensating the perturbations, Augmented GNSS propagate error correction signals based upon carefully surveyed locations. Notwithstanding Augmented GNSS ensures receivers' collecting reliable, accurate and available navigation signals and correction even in globally spoofed medium, local spoofers and jammers still are sources of threat. At this multi-GNSS age, users want to know whether the announced or calculated data are valid or not. To confirm the received signals, there are techniques that necessitate additional devices, antenna arrays and high computational cost. In this paper, we want to get researchers attention to trajectory outlier detection methods as an auxiliary to validate the signals the navigation satellite or Augmented GNSS send. Considering trajectory outlier detection methods in verification of navigation satellite messages is relatively low cost and not requiring additional device. In this approach, although a simple GNSS receiver is adequate, receiving the Augmented GNSS signal is preferable.

Cognitive radio networks (CRNs) are envisaged to alleviate the spectrum and capacity shortage aggravated by the static spectrum allocation. They are crucial for meeting the requirements of wireless traffic explosion emerging with new wireless applications and services. However, the security of CRNs is critical for their practical usage and widespread proliferation. Therefore, the fundamental mechanism of spectrum sensing in these systems has to be investigated from the security perspective. In this work, we define and analyze coordinated attack models for Cooperative Spectrum Sensing (CSS) in CRNs. We devise a simple yet effective countermeasure scheme based on trust management for cooperative sensing. We also provide experimental results to illustrate the performance of these systems under coordinated attacks.