An Updated Overview of the Satellite Networked Open Ground Stations (SatNOGS) Project

An overview of the SatNOGS project, a network of satellite ground stations around the world, optimized for modularity, built from readily available and affordable tools and resources. The rate of Low Earth Orbit (LEO) satellite launches increases with the participation of old and new entities. In this growing environment, SatNOGS provides a scalable and modular solution to track, identify, receive telemetry from, monitor, and assist operators in command/control of satellites. The SatNOGS global community, dedicated to its free and open-source values, develops hardware ground station designs (antennas, rotators, electronics), software for SDR-based communications, satellite scheduling and mission monitoring platforms. SatNOGS continuously develops and improves its infrastructure to allow observers to use this networked ground segment and remotely operate SatNOGS ground stations around the world. It also provides an easy way to store, access and view increasingly received satellites data, by supporting VHF, UHF, L and S bands

Artificial Intelligence-Driven Composition and Security Validation of an Internet of Things Ecosystem

Key challenges in Internet-of-Things (IoT) system design and management include the secure system composition and the calculation of the security and dependability level of the final system. This paper presents an event-based model-checking framework for IoT systems’ design and management, called CompoSecReasoner. It invokes two main functionalities: (i) system composition verification, and (ii) derivation and validation of security, privacy, and dependability (SPD) metrics. To measure the SPD values of a system, we disassemble two well-known types of security metrics—the attack surface methodologies and the medieval castle approach. The first method determines the attackable points of the system, while the second one defines the protection level that is provided by the currently composed system-of-systems. We extend these techniques and apply the Event Calculus method for modelling the dynamic behavior of a system with progress in time. At first, the protection level of the currently composed system is calculated. When composition events occur, the current system status is derived. Thereafter, we can deploy reactive strategies and administrate the system automatically at runtime, implementing a novel setting for Moving Target Defenses. We demonstrate the overall solution on a real ambient intelligence application for managing the embedded devices of two emulated smart buildings

Smart Home: Deep Learning as a Method for Machine Learning in Recognition of Face, Silhouette and Human Activity in the Service of a Safe Home

Despite the general improvement of living conditions and the ways of building buildings, the sense of security in or around them is often not satisfactory for their users, resulting in the search and implementation of increasingly effective protection measures. The insecurity that modern people face every day, especially in urban centers regarding their home security, led computer science to the development of intelligent systems, aiming to mitigate the risks and ultimately lead to the consolidation of the feeling of security. In order to establish security, smart applications were created that turned a house into a Smart and Safe Home. We first present and analyze the deep learning method and emphasize its important contribution to the development of the process for machine learning, both in terms of the development of methods for safety at home, but also in terms of its contribution to other sciences and especially medicine where the results are spectacular. We then analyze in detail the back propagation algorithm in neural networks in both linear and non-linear networks as well as the X-OR problem simulation. Machine learning has a direct and effective application with impressive results in the recognition of human activity and especially in face recognition, which is the most basic condition for choosing the most appropriate method in order to design a smart home. Due to the large amount of data and the large computing capabilities that a system must have in order to meet the needs of a safe, smart home, technologies such as fog and cloud computing are used for both face recognition and recognition of human silhouettes and figures. These smart applications compose the systems that are created mainly through “Deep Learning” methods based on machine learning techniques. Based on the study we have done and present in this work, we believe that with the use of DL technology, the creation of a completely safe house has been achieved to a large extent today, covering an urgent need these days due to the increase in crime

Artificial intelligence-driven composition and security validation of an Internet of Things ecosystem

Summarization: Key challenges in Internet-of-Things (IoT) system design and management include the secure system composition and the calculation of the security and dependability level of the final system. This paper presents an event-based model-checking framework for IoT systems’ design and management, called CompoSecReasoner. It invokes two main functionalities: (i) system composition verification, and (ii) derivation and validation of security, privacy, and dependability (SPD) metrics. To measure the SPD values of a system, we disassemble two well-known types of security metrics—the attack surface methodologies and the medieval castle approach. The first method determines the attackable points of the system, while the second one defines the protection level that is provided by the currently composed system-of-systems. We extend these techniques and apply the Event Calculus method for modelling the dynamic behavior of a system with progress in time. At first, the protection level of the currently composed system is calculated. When composition events occur, the current system status is derived. Thereafter, we can deploy reactive strategies and administrate the system automatically at runtime, implementing a novel setting for Moving Target Defenses. We demonstrate the overall solution on a real ambient intelligence application for managing the embedded devices of two emulated smart buildings.Παρουσιάστηκε στο: Applied Science

SPD-Safe: secure administration of railway intelligent transportation systems

Summarization: The railway transport system is critical infrastructure that is exposed to numerous man-made and natural threats, thus protecting this physical asset is imperative. Cyber security, privacy, and dependability (SPD) are also important, as the railway operation relies on cyber-physical systems (CPS) systems. This work presents SPD-Safe—an administration framework for railway CPS, leveraging artificial intelligence for monitoring and managing the system in real-time. The network layer protections integrated provide the core security properties of confidentiality, integrity, and authentication, along with energy-aware secure routing and authorization. The effectiveness in mitigating attacks and the efficiency under normal operation are assessed through simulations with the average delay in real equipment being 0.2–0.6 s. SPD metrics are incorporated together with safety semantics for the application environment. Considering an intelligent transportation scenario, SPD-Safe is deployed on railway critical infrastructure, safeguarding one outdoor setting on the railway’s tracks and one in-carriage setting on a freight train that contains dangerous cargo. As demonstrated, SPD-Safe provides higher security and scalability, while enhancing safety response procedures. Nonetheless, emergence response operations require a seamless interoperation of the railway system with emergency authorities’ equipment (e.g., drones). Therefore, a secure integration with external systems is considered as future work.Παρουσιάστηκε στο: Electronic

Generic Clopidogrel Besylate in the Secondary Prevention of Atherothrombotic Events: A 6-month Follow-up of a Randomised Clinical Trial

Background: The aim of the present interim analysis was to compare the clinical efficacy and safety of the generic clopidogrel besylate (CB) with the innovator clopidogrel hydrogen sulphate (CHS) salt in patient groups eligible to receive clopidogrel. Methods: A 2-arm, multicenter, open-label, phase IV clinical trial. Consecutive patients (n=1,864) were screened and 1,800 were enrolled in the trial and randomized to CHS (n=759) or CB (n=798). Primary efficacy end point was the composite of myocardial infarction, stroke or death from vascular causes. Primary safety end point was the rate of bleeding events as defined by Bleeding Academic Research Consortium (BARC) criteria. Results: At 6-months follow-up no differences were observed between CB and CHS in primary efficacy end point (OR, 0.80; 95% CI, 0.37 to 1.71; p=0.57). Rates of BARC-1,-2,-3a and -5b bleeding were similar between the two study groups whereas no bleeding events according to BARC-3b, -3c, -4 and -5a were observed in either CHS or CB group. Conclusion: The clinical efficacy and safety of the generic CB were similar to those of the innovator CHS salt, thus this generic clopidogrel formulation can be routinely used in the secondary prevention of atherothrombotic events for a period of at least 6 months. (Salts of Clopidogrel: Investigation to ENsure Clinical Equivalence, SCIENCE study Clinical Trials.gov Identifier: NCT02126982)