Detecting ADS-B Spoofing Attacks using Deep Neural Networks

The Automatic Dependent Surveillance-Broadcast (ADS-B) system is a key component of the Next Generation Air Transportation System (NextGen) that manages the increasingly congested airspace. It provides accurate aircraft localization and efficient air traffic management and also improves the safety of billions of current and future passengers. While the benefits of ADS-B are well known, the lack of basic security measures like encryption and authentication introduces various exploitable security vulnerabilities. One practical threat is the ADS-B spoofing attack that targets the ADS-B ground station, in which the ground-based or aircraft-based attacker manipulates the International Civil Aviation Organization (ICAO) address (a unique identifier for each aircraft) in the ADS-B messages to fake the appearance of non-existent aircraft or masquerade as a trusted aircraft. As a result, this attack can confuse the pilots or the air traffic control personnel and cause dangerous maneuvers. In this paper, we introduce SODA - a two-stage Deep Neural Network (DNN)-based spoofing detector for ADS-B that consists of a message classifier and an aircraft classifier. It allows a ground station to examine each incoming message based on the PHY-layer features (e.g., IQ samples and phases) and flag suspicious messages. Our experimental results show that SODA detects ground-based spoofing attacks with a probability of 99.34%, while having a very small false alarm rate (i.e., 0.43%). It outperforms other machine learning techniques such as XGBoost, Logistic Regression, and Support Vector Machine. It further identifies individual aircraft with an average F-score of 96.68% and an accuracy of 96.66%, with a significant improvement over the state-of-the-art detector.Comment: Accepted to IEEE CNS 201

Driver Distraction through Conversation Measured with Pupillometry

Assessing a driver´s mental workload during tasks that are not visualmanual is a challenging endeavor. Especially with the rapid development of speech systems, this is becoming increasingly important. Pupillometry promises to be a suitable physiological measurement method, sensitive to variations of cognitive workload. This driving simulator study shows that the pupillometry data indicate a significant increase in cognitive activity during conversation tasks regardless of the acoustic channel used

Reflecting on the Ordinary

My work is about valuing the ordinary moments in life that can be described as joyful, difficult, beautiful, painful, or even transcendent. My creativity arises from a need to tell the story of these moments, and to share my experience with others. These moments are often evidenced by the objects we leave behind. They are a testament to the life we have lived, or are now living. My attention to small, ordinary details includes honoring and remembering people who have died. They may be artists, writers, friends, or family members but it is important to me to record the unique contributions they have made to this world. To tell my stories I work with clay, paint, wood, wire, photography, jewelry, books, and found objects. I incorporate text to tell the story of the piece. I layer objects and imagery to focus the viewer\u27s attention on the mundane. I use found objects to serve as metaphorical evidence of the path we take. Many symbolic images also recur in my work, such as crows, gravestones, hands, and trees. Finally, I use grids to organize the structure of my pieces and help create a sense of order in the profusion of information. I want my art to be mindful, to elevate the ordinary, and to ask the viewer to join me in reflection on the human experience

The Coronavirus Crisis as Catalyst for EU Legitimacy? Italian Public Opinion and the EU during the Pandemic

Over the past few years, the EU has been challenged by multiple disintegration forces sustained by a growing number of Eurosceptic citizens. In this critical scenario, Italy has emerged as a relevant case because of its transformation from a leading pro-integration country to a country where EU integration is an increasingly divisive issue. We explore the relationship between Italian public opinion and the EU, with a specific interest in understanding how the coronavirus crisis may affect such a relationship, supposing that our case study may also be revealing as to how a crisis context can produce effects on the popular legitimacy of the EU. We show that in Italy there is demand from some majoritarian segments of society for stronger cooperation in the EU. To explain the apparent paradox of why Italians decreasingly feel that their country benefits from the EU but still want to increase EU cooperation in certain areas, we turn to the argument of the public's instrumental approach to the principle of burden-sharing: citizens support deeper integration to face the costs of the most pressing crises affecting the country and the EU at large

Application of multivariate data analysis for the classification of two dimensional gel images in Neuroproteomics

Two-dimensional gel electrophoresis (2DE) still plays a key role in proteomics for exploring the protein content of complex biological mixtures. However, the development of fully automatic strategies in extracting interpretable information from gel images is still a challenging task. In this work, we present a computational strategy aiming at an automatic classification of the discriminant patterns emerging from separation images intended as fingerprints of the correspondent biological conditions. The method was applied to gel images acquired in a study on motor neuron diseases: 33 2DE maps generated from samples of cerebrospinal fluid were processed (26 pathologic and 7 control subjects). Quantitative image descriptors were extracted and fitted to a partial least squares-discriminant analysis (PLSDA) assessing the chance to classify the samples. Moreover, the model was able to identify gel areas that most differ through the clinical categories. Combining multivariate statistical techniques with 2DEs may represent a valid tool to extract informative protein patterns. This kind of approach can contribute to the development of a system of screening to discriminate different clinical conditions on the basis of the overall patterns emerging from the maps, representing a useful complementary analysis in the routine of a proteomic laboratory. © 2011 Mazzara S, et al