Intelligenza artificiale e sicurezza: opportunità, rischi e raccomandazioni

L'IA (o intelligenza artificiale) è una disciplina in forte espansione negli ultimi anni e lo sarà sempre più nel prossimo futuro: tuttavia è dal 1956 che l’IA studia l’emulazione dell’intelligenza da parte delle macchine, intese come software e in certi casi hardware. L’IA è nata dall’idea di costruire macchine che - ispirandosi ai processi legati all’intelligenza umana - siano in grado di risolvere problemi complessi, per i quali solitamente si ritiene che sia necessario un qualche tipo di ragionamento intelligente. La principale area di ricerca e applicazione attuale dell’IA è il machine learning (algoritmi che imparano e si adattano in base ai dati che ricevono), che negli ultimi anni ha trovato ampie applicazioni grazie alle reti neurali (modelli matematici composti da neuroni artificiali) che a loro volta hanno consentito la nascita del deep learning (reti neurali di maggiore complessità). Appartengono al mondo dell’IA anche i sistemi esperti, la visione artificiale, il riconoscimento vocale, l’elaborazione del linguaggio naturale, la robotica avanzata e alcune soluzioni di cybersecurity. Quando si parla di IA c'è chi ne è entusiasta pensando alle opportunità, altri sono preoccupati poiché temono tecnologie futuristiche di un mondo in cui i robot sostituiranno l'uomo, gli toglieranno il lavoro e decideranno al suo posto. In realtà l'IA è ampiamente utilizzata già oggi in molti campi, ad esempio nei cellulari, negli oggetti smart (IoT), nelle industry 4.0, per le smart city, nei sistemi di sicurezza informatica, nei sistemi di guida autonoma (drive o parking assistant), nei chat bot di vari siti web; questi sono solo alcuni esempi basati tutti su algoritmi tipici dell’intelligenza artificiale. Grazie all'IA le aziende possono avere svariati vantaggi nel fornire servizi avanzati, personalizzati, prevedere trend, anticipare le scelte degli utenti, ecc. Ma non è tutto oro quel che luccica: ci sono talvolta problemi tecnici, interrogativi etici, rischi di sicurezza, norme e legislazioni non del tutto chiare. Le organizzazioni che già adottano soluzioni basate sull’IA, o quelle che intendono farlo, potrebbero beneficiare di questa pubblicazione per approfondirne le opportunità, i rischi e le relative contromisure. La Community for Security del Clusit si augura che questa pubblicazione possa fornire ai lettori un utile quadro d’insieme di una realtà, come l’intelligenza artificiale, che ci accompagnerà sempre più nella vita personale, sociale e lavorativa.AI (or artificial intelligence) is a booming discipline in recent years and will be increasingly so in the near future.However, it is since 1956 that AI has been studying the emulation of intelligence by machines, understood as software and in some cases hardware. AI arose from the idea of building machines that-inspired by processes related to human intelligence-are able to solve complex problems, for which it is usually believed that some kind of intelligent reasoning is required. The main current area of AI research and application is machine learning (algorithms that learn and adapt based on the data they receive), which has found wide applications in recent years thanks to neural networks (mathematical models composed of artificial neurons), which in turn have enabled the emergence of deep learning (neural networks of greater complexity). Also belonging to the AI world are expert systems, computer vision, speech recognition, natural language processing, advanced robotics and some cybersecurity solutions. When it comes to AI there are those who are enthusiastic about it thinking of the opportunities, others are concerned as they fear futuristic technologies of a world where robots will replace humans, take away their jobs and make decisions for them. In reality, AI is already widely used in many fields, for example, in cell phones, smart objects (IoT), industries 4.0, for smart cities, cybersecurity systems, autonomous driving systems (drive or parking assistant), chat bots on various websites; these are just a few examples all based on typical artificial intelligence algorithms. Thanks to AI, companies can have a variety of advantages in providing advanced, personalized services, predicting trends, anticipating user choices, etc. But not all that glitters is gold: there are sometimes technical problems, ethical questions, security risks, and standards and legislation that are not entirely clear. Organizations already adopting AI-based solutions, or those planning to do so, could benefit from this publication to learn more about the opportunities, risks, and related countermeasures. Clusit's Community for Security hopes that this publication will provide readers with a useful overview of a reality, such as artificial intelligence, that will increasingly accompany us in our personal, social and working lives

Inspection of Structures by Passive Extraction of Acoustic Transfer Functions and Ultrasonic Imaging

Inspection of structures is a critical task that needs to be performed in order to guarantee the safety of structural components during their service life. Different Nondestructive Evaluation (NDE) techniques can be used to inspect aerospace, civil, and biological systems to ensure their structural integrity and to identify the presence of damages and defects, which could impair the correct functioning of the overall structure.The focus of this dissertation is the inspection of structures through the passive extraction of the acoustic transfer function of the medium under consideration, and the 2D and 3D characterization of defects by means of ultrasonic imaging. The first part of the dissertation addresses the issue of defect detection in railroad tracks by extracting the acoustic transfer function of rails through a normalized cross-correlation operator, which exploits the random acoustic vibrations generated by the train wheels. A technique to remove uncorrelated noise from the recorded signals is also introduced to make the transfer function reconstruction more robust. A statistical outlier analysis is used to detect any variation in the transfer function of the rail as the train moves along the track, in order to identify locations where discontinuities (joints, welds, defects) might be present. A prototype with multiple pairs of capacitive sensors was developed to perform the inspection in a non-contact, passive-only, high-speed manner. Results from fields tests performed at the Transportation Technology Center (TTC) in Pueblo, CO, will demonstrate the feasibility of the system for the reliable inspection of railroad tracks at speeds up to 80mph.The second part of the dissertation is focused on the characterization of defects using ultrasonic imaging to create 2D and 3D images of the inspected medium. Imaging in bulk solids and plates is performed using sensor arrays and an improved beamforming algorithm that uses information about the structure of the propagating acoustic wave modes to improve the defect characterization process. Furthermore, the experimental application to railroad tracks and the implementation on a Graphics Processing Unit (GPU) shows the potential for the accurate real-time imaging of rail flaws

On the identification of bridge decks aeroelastic coefficients: the covariance block hankel matrix method

Il seguente elaborato si concentra sull'identifi�cazione strutturale di sistemi soggetti a sollecitazioni aeroelastiche e nello speci�fico l'attenzione viene rivolta ad impalcati da ponte. Si analizzano i concetti principali caratterizzanti il campo dell'aeroelasticità indagando i fattori dominanti che entrano in gioco sul piano teorico. In seguito, si considera il metodo di identifi�cazione strutturale chiamato Covariance Block Hankel Matrix (CBHM) utilizzato come strumento di derivazione dei coeffi�cienti aeroelastici propri della struttura. Infi�ne, si indaga il comportamento di questo metodo di identi�ficazione al variare di una serie di parametri chiave e all'interno di diversi scenari, visionando risultati ottenuti tramite una serie di test eff�ettuati per provare l'a�dattabilità del metodo stesso al variare delle condizioni che caratterizzano il sistema

Inspection of Structures by Passive Extraction of Acoustic Transfer Functions and Ultrasonic Imaging

Inspection of structures is a critical task that needs to be performed in order to guarantee the safety of structural components during their service life. Different Nondestructive Evaluation (NDE) techniques can be used to inspect aerospace, civil, and biological systems to ensure their structural integrity and to identify the presence of damages and defects, which could impair the correct functioning of the overall structure.The focus of this dissertation is the inspection of structures through the passive extraction of the acoustic transfer function of the medium under consideration, and the 2D and 3D characterization of defects by means of ultrasonic imaging. The first part of the dissertation addresses the issue of defect detection in railroad tracks by extracting the acoustic transfer function of rails through a normalized cross-correlation operator, which exploits the random acoustic vibrations generated by the train wheels. A technique to remove uncorrelated noise from the recorded signals is also introduced to make the transfer function reconstruction more robust. A statistical outlier analysis is used to detect any variation in the transfer function of the rail as the train moves along the track, in order to identify locations where discontinuities (joints, welds, defects) might be present. A prototype with multiple pairs of capacitive sensors was developed to perform the inspection in a non-contact, passive-only, high-speed manner. Results from fields tests performed at the Transportation Technology Center (TTC) in Pueblo, CO, will demonstrate the feasibility of the system for the reliable inspection of railroad tracks at speeds up to 80mph.The second part of the dissertation is focused on the characterization of defects using ultrasonic imaging to create 2D and 3D images of the inspected medium. Imaging in bulk solids and plates is performed using sensor arrays and an improved beamforming algorithm that uses information about the structure of the propagating acoustic wave modes to improve the defect characterization process. Furthermore, the experimental application to railroad tracks and the implementation on a Graphics Processing Unit (GPU) shows the potential for the accurate real-time imaging of rail flaws

Bistatic scattering of an elastic object using the structural admittance and noise-based holographic measurements

International audienc

Air-Coupled Ultrasonic Testing of Rails

Under sponsorship by the Federal Railroad Administration (FRA), the University of California at San Diego is developing a system for non-contact rail integrity evaluation. The system uses ultrasonic air-coupled guided wave signal generation and air-coupled signal detection to detect transverse and longitudinal defects in the rail. The fully air-coupled ultrasound transduction presents an evolution over the previous-generation prototype that used pulsed laser ultrasound excitation and air-coupled detection. The system also features a real-time statistical data analysis that minimizes false positive rates and maximizes true detection rates, as well as a specialized filtering approach and impedance matching to overcome the inherently poor signal-to-noise ratio of air-coupled ultrasonic measurements in rail steel. Special targets of the inspection are Detail Fractures, Transverse Fissures and Vertical Split Head defects that were responsible for several accidents in recent years. The design of the prototype was guided by rigorous Finite Element Analysis simulations that revealed fundamental aspects of air-coupled wave propagation and interaction with internal defects in rails. The non-contact air-coupled prototype has been tested at the Transportation Technology Center in Pueblo, CO, in October 2014 (at walking speed) and in November 2015 (at speeds of 5 mph, 10 mph and 15 mph). Results from these tests are presented in terms of Receiver Operating Characteristic (ROC) curves that assess the performance of the system in terms of the unavoidable trade-off between true detection rates and false positive rates. The preliminary analysis of the ROC curves from the latest November 2015 tests is quite encouraging

Determining structural acoustic properties from noise-based holographic measurements

International audienc

Holographic array for determining structural acoustic properties

International audienc

Bistatic scattering of an elastic object using the structural admittance and noise-based holographic measurements

International audienc