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Recent advances in hardware and information technology have accelerated the proliferation of smart and interconnected devices facilitating the rapid development of the Internet of Things (IoT). IoT applications and services are widely adopted in environments such as smart cities, smart industry, autonomous vehicles, and eHealth. As such, IoT devices are ubiquitously connected, transferring sensitive and personal data without requiring human interaction. Consequently, it is crucial to preserve data privacy. This paper presents a comprehensive survey of recent Machine Learning (ML)- and Deep Learning (DL)-based solutions for privacy in IoT. First, we present an in depth analysis of current privacy threats and attacks. Then, for each ML architecture proposed, we present the implementations, details, and the published results. Finally, we identify the most effective solutions for the different threats and attacks.

Cyberattacks in the Internet of Things (IoT) are growing exponentially, especially zero-day attacks mostly driven by security weaknesses on IoT networks. Traditional intrusion detection systems (IDSs) adopted machine learning (ML), especially deep Learning (DL), to improve the detection of cyberattacks. DL-based IDSs require balanced datasets with large amounts of labeled data; however, there is a lack of such large collections in IoT networks. This paper proposes an efficient intrusion detection framework based on transfer learning (TL), knowledge transfer, and model refinement, for the effective detection of zero-day attacks. The framework is tailored to 5G IoT scenarios with unbalanced and scarce labeled datasets. The TL model is based on convolutional neural networks (CNNs). The framework was evaluated to detect a wide range of zero-day attacks. To this end, three specialized datasets were created. Experimental results show that the proposed TL-based framework achieves high accuracy and low false prediction rate (FPR). The proposed solution has better detection rates for the different families of known and zero-day attacks than any previous DL-based IDS. These results demonstrate that TL is effective in the detection of cyberattacks in IoT environments.

The brightness of type Ia supernovae, and their homogeneity as a class, makes them powerful tools in cosmology, yet little is known about the progenitor systems of these explosions. They are thought to arise when a white dwarf accretes matter from a companion star, is compressed and undergoes a thermonuclear explosion 1 , 2 , 3 . Unless the companion star is another white dwarf (in which case it should be destroyed by the mass-transfer process itself), it should survive and show distinguishing properties. Tycho's supernova 4 , 5 is one of only two type Ia supernovae observed in our Galaxy, and so provides an opportunity to address observationally the identification of the surviving companion. Here we report a survey of the central region of its remnant, around the position of the explosion, which excludes red giants as the mass donor of the exploding white dwarf. We found a type G0–G2 star, similar to our Sun in surface temperature and luminosity (but lower surface gravity), moving at more than three times the mean velocity of the stars at that distance, which appears to be the surviving companion of the supernova.

This article describes the case of the personal items found in common graves dated between 1939 and 1956 after the Spanish Civil War (1936–1939), located in Paterna’s cemetery (Spain). It was important in this study to know the state of the conservation of the objects and to obtain clues about their origin and use just as in a forensic study. This would allow the moral restitution of the historical memory of the victims of the war conflict. The multi-technique strategy has included light and electron microscopy, infrared spectroscopy and X-ray diffraction. Materials of the early 20th century used in pencil sharpeners, glasses, cutlery, lighters, rings, and buttons or medications contained in small bottles and boxes have been identified and have enabled the lives of their owners to be reconstructed during their imprisonment and execution. All these objects exhibited a thin layer of adipocere, a well-known compound in forensic science formed during the decomposition of human and animal corpses. Interestingly, rare corrosion processes have been identified in two of the objects analyzed, which are linked to their proximity to the decomposing corpses of the deceased. Copper sulfides and/or sulfates have been identified in the lighter, and scholzite, a zinc and calcium phosphate, has been identified in the glasses.

El artículo analiza y compara el pensamiento en torno a la participaciónciudadana de políticos, técnicos y activistas de la esfera municipal deMadrid, Barcelona, San Sebastián y Lleida. La investigación sefundamenta en la metodología Q, cuya combinación de elementoscuantitativos y cualitativos permite generar evidencias de modosistemático, riguroso y cuantificable, sin renunciar a la complejidad y lariqueza del lenguaje de los propios actores. Los resultados nos revelantres perspectivas sobre la participación (integral, regeneradora ydesconfiada), divergentes en cuanto a su apreciación de lasinstituciones políticas y las organizaciones sociales. Con todo, tambiénse constata la existencia de un núcleo de consenso sobre el que sepueden construir instituciones participativas más legítimas y eficaces.

Some high performance computing (HPC) applications exhibit increasing real-time requirements, which call for effective means to predict their high execution times distribution. This is a new challenge for HPC applications but a well-known problem for real-time embedded applications where solutions already exist, although they target low-performance systems running single-threaded applications. In this paper, we show how some performance validation and measurement-based practices for real-time execution time prediction can be leveraged in the context of HPC applications on high-performance platforms, thus enabling reliable means to obtain real-time guarantees for those applications. In particular, the proposed methodology uses coordinately techniques that randomly explore potential timing behavior of the application together with Extreme Value Theory (EVT) to predict rare (and high) execution times to, eventually, derive probabilistic Worst-Case Execution Time (pWCET) curves. We demonstrate the effectiveness of this approach for an acoustic wave inversion application used for geophysical exploration.

The widespread use of smart devices and the numerous security weaknesses of networks has dramatically increased the number of cyber-attacks in the internet of things (IoT). Detecting and classifying malicious traffic is key to ensure the security of those systems. This paper implements a distributed framework based on deep learning (DL) to prevent many different sources of vulnerability at once, all under the same protection system. Two different DL models are evaluated: feed forward neural network and long short-term memory. The models are evaluated with two different datasets (i.e.NSL-KDD and BoT-IoT) in terms of performance and identification of different kinds of attacks. The results demonstrate that the proposed distributed framework is effective in the detection of several types of cyber-attacks, achieving an accuracy up to 99.95% across the different setups.

The article analyses and compares the thinking on citizen participation of elected and non-elected officials, as well as social activists of the Spanish cities of Madrid, Barcelona, San Sebastián and Lleida. The research is based on Q methodology, whose combination of quantitative and qualitative elements can generate systematic, rigorous and quantifiable evidence, without sacrificing the complexity and richness of language. The results reveal three distinct perspectives on participation (integral, regenerative and distrustful), that differ notably in their appreciation of political institutions and social organizations. However, results also point to the existence of a core of consensus beliefs, which opens the door to building more legitimate and effective participatory institutions.

The article analyses and compares the thinking on citizen participation of elected and non-elected officials, as well as social activists of the Spanish cities of Madrid, Barcelona, San Sebastián and Lleida. The research is based on Q methodology, whose combination of quantitative and qualitative elements can generate systematic, rigorous and quantifiable evidence, without sacrificing the complexity and richness of language. The results reveal three distinct perspectives on participation (integral, regenerative and distrustful), that differ notably in their appreciation of political institutions and social organizations. However, results also point to the existence of a core of consensus beliefs, which opens the door to building more legitimate and effective participatory institutions.