Machine-learning-based device-independent certification of quantum networks

Witnessing nonclassical behavior is a crucial ingredient in quantum information processing. For that, one has to optimize the quantum features a given physical setup can give rise to, which is a hard computational task currently tackled with semidefinite programming, a method limited to linear objective functions and that becomes prohibitive as the complexity of the system grows. Here, we propose an alternative strategy, which exploits a feedforward artificial neural network to optimize the correlations compatible with arbitrary quantum networks. A remarkable step forward with respect to existing methods is that it deals with nonlinear optimization constraints and objective functions, being applicable to scenarios featuring independent sources and nonlinear entanglement witnesses. Furthermore, it offers a significant speedup in comparison with other approaches, thus allowing to explore previously inaccessible regimes. We also extend the use of the neural network to the experimental realm, a situation in which the statistics are unavoidably affected by imperfections, retrieving device-independent uncertainty estimates on Bell-like violations obtained with independent sources of entangled photon states. In this way, this work paves the way for the certification of quantum resources in networks of growing size and complexity

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

Il Futuro della Cybersecurity in Italia: Ambiti Progettuali Strategici

Il presente volume nasce come continuazione del precedente, con l’obiettivo di delineare un insieme di ambiti progettuali e di azioni che la comunità nazionale della ricerca ritiene essenziali a complemento e a supporto di quelli previsti nel DPCM Gentiloni in materia di sicurezza cibernetica, pubblicato nel febbraio del 2017. La lettura non richiede particolari conoscenze tecniche; il testo è fruibile da chiunque utilizzi strumenti informatici o navighi in rete. Nel volume vengono considerati molteplici aspetti della cybersecurity, che vanno dalla definizione di infrastrutture e centri necessari a organizzare la difesa alle azioni e alle tecnologie da sviluppare per essere protetti al meglio, dall’individuazione delle principali tecnologie da difendere alla proposta di un insieme di azioni orizzontali per la formazione, la sensibilizzazione e la gestione dei rischi. Gli ambiti progettuali e le azioni, che noi speriamo possano svilupparsi nei prossimi anni in Italia, sono poi accompagnate da una serie di raccomandazioni agli organi preposti per affrontare al meglio, e da Paese consapevole, la sfida della trasformazione digitale. Le raccomandazioni non intendono essere esaustive, ma vanno a toccare dei punti che riteniamo essenziali per una corretta implementazione di una politica di sicurezza cibernetica a livello nazionale. Politica che, per sua natura, dovrà necessariamente essere dinamica e in continua evoluzione in base ai cambiamenti tecnologici, normativi, sociali e geopolitici. All’interno del volume, sono riportati dei riquadri con sfondo violetto o grigio; i primi sono usati nel capitolo introduttivo e nelle conclusioni per mettere in evidenza alcuni concetti ritenuti importanti, i secondi sono usati negli altri capitoli per spiegare il significato di alcuni termini tecnici comunemente utilizzati dagli addetti ai lavori. In conclusione, ringraziamo tutti i colleghi che hanno contribuito a questo volume: un gruppo di oltre 120 ricercatori, provenienti da circa 40 tra Enti di Ricerca e Università, unico per numerosità ed eccellenza, che rappresenta il meglio della ricerca in Italia nel settore della cybersecurity. Un grazie speciale va a Gabriella Caramagno e ad Angela Miola che hanno contribuito a tutte le fasi di produzione del libro. Tra i ringraziamenti ci fa piacere aggiungere il supporto ottenuto dai partecipanti al progetto FILIERASICURA

Threshold Switching by Bipolar Avalanche Multiplication in Ovonic Chalcogenide Glasses

Abstract An ovonic threshold switch (OTS) based on chalcogenide glasses finds application as a selecting device in storage class memory (SCM) arrays. The OTS operation relies on the threshold switching, where the device switches from the off to the on state without phase transition, then the device turns off almost immediately as the voltage is reduced below a certain holding value. The physics behind the switching phenomenon has attracted wide interest due to the complicated interplay between electronic transport, joule heating and phase transition. In this work, it is shown that the current‐voltage characteristic close to the switching point carries the fingerprint of carrier multiplication. The physical mechanism of threshold switching is then explained by bipolar impact ionization leading to avalanche multiplication, which in turn gives rise to the typical S‐shaped characteristic. Numerical simulations by this physics‐based model account for the measured switching properties, namely threshold voltage and current, at various chalcogenide thicknesses and compositions. These results provide the theoretical framework for future design and optimization of OTS in memory and computing applications

SyML: Guiding Symbolic Execution Toward Vulnerable States Through Pattern Learning

Exploring many execution paths in a binary program is essential to discover new vulnerabilities. Dynamic Symbolic Execution (DSE) is useful to trigger complex input conditions and enables an accurate exploration of a program while providing extensive crash replayability and semantic insights. However, scaling this type of analysis to complex binaries is difficult. Current methods suffer from the path explosion problem, despite many attempts to mitigate this challenge (e.g., by merging paths when appropriate). Still, in general, this challenge is not yet surmounted, and most bugs discovered through such techniques are shallow. We propose a novel approach to address the path explosion problem: A smart triaging system that leverages supervised machine learning techniques to replicate human expertise, leading to vulnerable path discovery. Our approach monitors the execution traces in vulnerable programs and extracts relevant features - register and memory accesses, function complexity, system calls - to guide the symbolic exploration. We train models to learn the patterns of vulnerable paths from the extracted features, and we leverage their predictions to discover interesting execution paths in new programs. We implement our approach in a tool called SyML, and we evaluate it on the Cyber Grand Challenge (CGC) dataset - a well-known dataset of vulnerable programs - and on 3 real-world Linux binaries. We show that the knowledge collected from the analysis of vulnerable paths, without any explicit prior knowledge about vulnerability patterns, is transferrable to unseen binaries, and leads to outperforming prior work in path prioritization by triggering more, and different, unique vulnerabilities