Tesi di Dottorato
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Item Semantic control for the Cybersecurity domain: investigation on the representativeness of a domain-specific terminology referring to lexical variation(Università della Calabria, 2021-05-12) Lanza, Claudia; Guarasci, Roberto; Crupi, FeliceThe underlying idea of this PhD research project is to develop a model meant to guarantee the terminological coverage of a semantic resource, such as a thesaurus, and its representativeness threshold with reference to semantic variation over time within a highly specialized domain, such as the Cybersecurity. By building an Italian thesaurus related to the Cybersecurity domain, this project wants to offer organizations a knowledge representation of the field of study in Information and Communications Technology (ICT) security as complete as possible. The development of an Italian thesaurus for the Cybersecurity knowledge domain is part of the activities included in the main project “Cybersecurity Observatory” held by the Institution of Informatics and Telematics (IIT) at the National Research Council (CNR) sited in Pisa (Italy). The thesis describes the steps followed for the construction of the Italian Cybersecurity thesaurus and for the assessment of a multi-domain methodology to fix a semantic representativeness threshold with reference to qualitative terms richness within a specialized domain and the variation in information related to the latter over time. The main phases henceforth described are related to (1) a presentation of the principal reasons for building a semantic tool, such as a thesaurus, as a means of semantic control for a specific domain; (2) a description of the steps which characterize the corpus creation and the terminological extraction through the use of specific Natural Language Processing (NLP) tasks and linguistic pattern configuration within the employed software; (3) the way a bilingual thesaurus and a bilingual ontology have been realized by creating parallel and comparable corpora; (4) a presentation of a model of mapping existing standards on Cybersecurity in English to all the head terms contained in the source corpus in Italian through Python scripts in order to evaluate which candidate terms should be chosen for inclusion in the thesaurus; (5) a descriptive section on the work done in migrating the terms and their relationships from the Italian thesaurus on Cybersecurity to an ontology system; (6) the phase related to keyphrases extraction, with the help of document oriented algorithms, i.e., Multipartite Rank or TopicRank, from the source documents. This was carried out to obtain a targeted clustering of the domain and as an aide in the process of semantic abstraction, needed to better systematize the structure of thesaurus’ main entry categories; (7) the exploration of new methodologies, i.e., distributional semantics, term variation, pattern-based detection schemes or inference from the Web Ontology Language (OWL) properties, to deduce the technical information included in the source corpus with the goal of automatically generating the semantic network of connections between the representative terms of the Cybersecurity domain in a thesaurus system; (8) a future perspective, accompanied by evolving examples in practice, of creating an additional database to populate the Cybersecurity source corpus through the use of the social media world. Twitter is one of the preferred web portals from which to retrieve information about the domain: this new information flow should give to the semantic resources, set up for Cybersecurity knowledge organization, an increased level of terminological density to be analyzed in order to improve the semantic coverage.Item Design of physically unclonable functions in cmos and emerging technologies for hardware security applications(Università della Calabria, 2023-02-23) Vatalaro, Massimo; Fortino, Giancarlo; Crupi, FeliceThe advent of the IoT scenario heavily pushed the demand of preserving the information down to the chip level due to the increasing demand of interconnected devices. Novel algorithms and hardware architectures are developed every year with the aim of making these systems more and more secure. However, IoT devices operate with constrained area, energy and budget thus making the hardware implementation of these architectures not always feasible. Moreover, these algorithms require truly random key for guarantying a certain security degree. Typically, these secret keys are generated off chip and stored in a non-volatile manner. Unfortunately, this approach requires additional costs and suffers from reverse engineering attacks. Physically unclonable functions (PUFs) are emerging cryptographic primitives which exploit random phenomena, such as random process variations in CMOS manufacturing processes, for generating a unique, repeatable, random, and secure keys in a volatile manner, like a digital fingerprint. PUFs represent a secure and low-cost solution for implementing lightweight cryptographic algorithms. Ideally PUF data should be unique and repeatable even under noisy or different environmental conditions. Unfortunately, guarantying a proper stability is still challenging, especially under PVT variations, thus requiring stability enhancement techniques which overtake the PUF itself in terms of required area and energy. Nowadays, different PUF solutions have been proposed with the aim of achieving ever more stable responses while keeping the area overhead low. This thesis presents a novel class of static monostable PUFs based on a voltage divider between two nominally identical sub-circuits. The fully static behavior along with the use of nominally identical sub-circuits ensure that the correct output is always delivered even when on-chip noise occasionally flips the bit, and that randomness is always guaranteed regardless of the PVT conditions. Measurement results in 180-nm CMOS technology demonstrates the effectiveness of the proposed solution with a native instability (BER) of only 0.61% (0.13%) along with a low sensitivity to both temperature and voltage variations. However, these results were achieved at the cost of more area-hungry design (i.e., 7,222𝐹 ) compared to other relevant works. The proposed solution was also implemented with emerging paper based MoS2 nFETs by exploiting a LUT-based Verilog-A model, calibrated with experimental 𝐼 vs 𝑉 at different 𝑉 curves, whose variability was extracted from different 𝐼 vs 𝑉 curves of 27 devices from the same manufacturing lot. Simulations results demonstrate that these devices can potentially used as building block for next generation electronics targeting hardware security applications. Finally, this thesis also provides an application scenario, in which the proposed PUF solution is employed as TRNG module for implementing a smart tag targeting anti-counterfeiting applications.Item Global Optimization and Fractal Curves(Università della Calabria, 2022-07-13) Nasso, Maria Chiara; Crupi, Felice; Sergeev, YaroslavIl presente lavoro di tesi è principalmente dedicato all'ottimizzazione globale e in particolare a metodi numerici di ottimizzazione globale basati su frattali. Viene affrontato lo studio teorico di alcune curve frattali, vengono proposti nuovi algoritmi che si basano su approcci frattali per ridurre la dimensione del problema e vengono introdotti nuovi metodi di ottimizzazione globale basati sulla tecnica del Local Tuning. Ciascuno dei nuovi metodi proposti è stato implementato e studiato dal punto di vista teorico. Inoltre, gli esperimenti numerici, condotti su diverse centinaia di funzioni test, tratte dalla letteratura e generate in maniera random confermano i vantaggi degli algoritmi presentati.Item Global Optimization and Fractal Curves(Università della Calabria, 2022-07-13) Nasso, Maria Chiara; Crupi, Felice; Sergeev, YaroslavIl presente lavoro di tesi è principalmente dedicato all'ottimizzazione globale e in particolare a metodi numerici di ottimizzazione globale basati su frattali. Viene affrontato lo studio teorico di alcune curve frattali, vengono proposti nuovi algoritmi che si basano su approcci frattali per ridurre la dimensione del problema e vengono introdotti nuovi metodi di ottimizzazione globale basati sulla tecnica del Local Tuning. Ciascuno dei nuovi metodi proposti è stato implementato e studiato dal punto di vista teorico. Inoltre, gli esperimenti numerici, condotti su diverse centinaia di funzioni test, tratte dalla letteratura e generate in maniera random confermano i vantaggi degli algoritmi presentati.Item Heterogeneous FPGA-based Embedded Systems for Vision IoT Applications(Università della Calabria, 2020-04-23) Spagnolo, Fanny; Crupi, Felice; Perri, Stefania; Corsonello, PasqualeEmbedded sensor devices provided by processing capabilities are opening novel and exciting opportunities in the era of edge-computing Internet-of-Things (IoT). The workload decentralization leads to a plenty of benefits, including better reactivity and reliability and reduced data transfer costs. These advantages have a strong impact especially in the visual IoT field, for which the large bandwidth required by visual data is one of the most critical challenges. However, bringing vision technologies into smart nodes is not a trivial task, because of the stringent energy and performance requirements, in addition to the need of cost-effective and compact processing units. Heterogeneous architectures may represent the key to address these necessities. Among possible heterogeneous platforms, those based on reconfigurable devices such as Field Programmable Gate Arrays (FPGAs) show a high adaptability to a variety of workloads, which is an important goal for edge-computing. Therefore, their deployment in disparate IoT applications, ranging from video surveillance to autonomous driving, is emerging as a promising solution. This dissertation proposes a study on the suitability of modern heterogeneous FPGA System-on-Chips (SoCs) to implement embedded smart vision sensor nodes. To this purpose, several computer vision algorithms aimed to extract synthetic data from raw input frames have been analysed, and novel hardware-oriented solutions have been proposed to deploy them on heterogeneous SoCs. In all the presented cases, ranging from stereo vision to connected component analysis and deep learning, speed performances and/or energy efficiency are considerably improved with respect to state-of-the-art solutions. As an example, the proposed heterogeneous architecture for convolutional neural networks achieves a power efficiency up to 89.5% higher than competitive prior works, demonstrating its suitability in the scenario of energy-constrained and real-time IoT.Item An Analog/Mixed-Signal SoC-Package Co-Design Methodology for Early Stage Signal Integrity Assessment Exploiting the Potential of Machine Learning Models(Università della Calabria, 2020-04-30) Settino, Francesco; Crupi, Felice; Palestri, Pierpaolo; Brandtner, Thomas; Koffler, HaraldThe development of new generation System-on-Chip (SoC) is mainly driven by the demand of an ever increasing number of functionalities at reduced cost and time-to-market. This is enabled by re-using specialized functional blocks, generally referred as intellectual property (IP) blocks. However, each block (analog, digital, analog mixed-signal) is typically designed and optimized independently either inhouse or by a third-party vendor. This leads to an increased design complexity, making the integration of the analog mixed-signal (AMS) blocks very challenging. As the switching behavior (di/dt and dv/dt) of the chip signals increases due to higher clock frequency, the package and board interconnects start to contribute significantly to the overall system-level performance. Signal integrity is a main issue in package designs due to the parasitic effects of capacitive/inductive coupling between potential aggressor and victim signals. In general, fast switching signals can induce unwanted disturbances into sensitive signals due to crosstalk effects even via off-chip interconnects, which may degrade significantly the overall system-level performance. A SoC for automotive applications typically requires several high accuracy analog-to-digital converters (ADCs), which are key blocks to sense and process the external inputs in order to quickly react at system-level (especially for safety requirements). However, those ADCs need to be integrated in a complex environment that comprises many different IP blocks (e.g. power converter or high-speed interfaces) at high switching frequency that can act as potential aggressors. Hence, next generation of SoC will face a significantly higher number of aggressor-victim couples. On the other hand, more accurate mixed-signal circuitries such as voltage monitoring will be required especially for advanced driver assistance systems (ADAS) application due to safety requirements. Reliable and accurate prediction of the system-level behavior by chip-packageboard co-design is essential to achieve “right first time” solutions. A machine learning approach can save significant time considering the main challenges in performing system-level simulations, mainly related to circuit complexity and convergence issue due to the integration of the package model (typically S-parameter data). This research work focuses on the development of a methodology exploiting machine learning algorithm to enable optimized SoC-Package co-design right from the early stage of the development cycle. The main target is to detect potential specification violation issues at system-level that may occur due to signal integrity challenge at package-level, providing guidelines for package design, and a quick feedback for the chip design development towards the optimization of the overall chip-package-board system, optimizing development cycles and time-to-market for competitive products.Item A multi-scale theoretical paradigm to model the complex interactions between macromolecules and polymeric membranes membranes(Università della Calabria, 2020-03-29) Petrosino, Francesco; Crupi, Felice; Curcio, Stefano; De Luca, GiorgioThe overall aim of the work was to provide a complete Multiscale Model of macromolecules interactions to simulate different processes and bioprocesses where such interactions, among different macromolecules and between macromolecules and polymeric surface, strongly determine the system behaviour. The adsorption of proteins on material surfaces is an essential biological phenomenon in nature, which shows a wide application prospect in many fields, such as membrane based processes, biosensors, biofuel cells, biocatalysis, biomaterials, and protein chromatography. Therefore, it is of great theoretical and practical significance to study the interfacial adsorption behaviour of proteins and their structuration and aggregation in order to describe concentration polarization phenomena in separation processes. It is worthwhile remarking that ab-initio simulations allow the estimation of parameters without exploiting any empirical or experimental methodology. In the present work, an improved multiscale model aimed at describing membrane fouling in the UltraFiltration (UF) process was proposed. The proteins-surface interactions were accurately computed by first-principle-based calculations. Both the effective surface of polysulfone (PSU) and the first layer of proteins adsorbed on the membrane surface were accurately modelled. At macroscopic scale, an unsteady-state mass transfer model was formulated to describe the behaviour of a typical dead-end UF process. The adsorption of an enzyme, i.e. the phosphotriesterase (PTE), on polysulfone (PSU) membrane surface was investigated as well through a double-scale computational approach. The results of such a formulated model were useful to obtain a detailed knowledge about enzyme adhesion and to give precise indications about the orientations of its binding site. One of the most important challenges is to use the stochastic approach adding an improved nano- and micro-scale step to the well-established multiscale procedure. The implementation of a Monte Carlo algorithm was performed with the aim of investigating the fouling structure during membrane operation like different micro-equilibrium states. The final aim of the work was to carry out the calculation of both Osmotic Pressure and Diffusion Coefficient in the fouling cake by the already-performed Monte Carlo simulations. Furthermore, the so-obtained parameters were exploited in macroscopic CFD simulations so as to calculate the overall resistance of the deposit accumulated on membrane surface during filtration.Item Enhanced electromagnetic models for the accurate design of non-invasive microwave biosensors(Università della Calabria, 2020-04-27) Cioffi, Vincenzo; Crupi, Felice; Costanzo, SandraItem Methodologies and Applications for Big Data Analytics(Università della Calabria, 2020-05-02) Cassavia, Nunziato; Crupi, Felice; Flesca, Sergio; Masciari, ElioDue to the emerging Big Data paradigm, driven by the increase availability of users generated data, traditional data management techniques are inadequate in many real life scenarios. The availability of huge amounts of data pertaining to user social interactions calls for advanced analysis strategies in order to extract meaningful information. Furthermore, heterogeneity and high speed of user generated data require suitable data storage and management and a huge amount of computing power. This dissertation presents a Big Data framework able to enhances user quest for information by exploiting previous knowledge about their social environment. Moreover an introduction to Big Data and NoSQL systems is provided and two basic architecture for Big Data analysis are presented. The framework that enhances user quest, leverages the extent of influence that the users are potentially subject to and the influence they may exert on other users. User influence spread, across the network, is dynamically computed as well to improve user search strategy by providing specific suggestions, represented as tailored faceted features. The approach is tested in an important application scenario such as tourist recommendation where several experiment have been performed to assess system scalability and data read/write efficiency. The study of this system and of advanced analysis on Big Data has shown the need for a huge computing power. To this end an high performance computing system named CoremunitiTM is presented. This system represents a P2P solution for solving complex works by using the idling computational resources of users connected to this network. Users help each other by asking the network computational resources when they face high computing demanding tasks. Differently from many proposals available for volunteer computing, users providing their resources are rewarded with tangible credits. This approach is tested in an interesting scenario as 3D rendering where its efficiency has been compared with "traditional" commercial solutions like cloud platforms and render farms showing shorter task completion times at low cost.Item Methodologies and Applications for Big Data Analytics(Università della Calabria, 2020-05-02) Cassavia, Nunziato; Crupi, Felice; Flesca, Sergio; Masciari, Elio;Due to the emerging Big Data paradigm, driven by the increase availability of users generated data, traditional data management techniques are inadequate in many real life scenarios. The availability of huge amounts of data pertaining to user social interactions calls for advanced analysis strategies in order to extract meaningful information. Furthermore, heterogeneity and high speed of user generated data require suitable data storage and management and a huge amount of computing power. This dissertation presents a Big Data framework able to enhances user quest for information by exploiting previous knowledge about their social environment. Moreover an introduction to Big Data and NoSQL systems is provided and two basic architecture for Big Data analysis are presented. The framework that enhances user quest, leverages the extent of influence that the users are potentially subject to and the influence they may exert on other users. User influence spread, across the network, is dynamically computed as well to improve user search strategy by providing specific suggestions, represented as tailored faceted features. The approach is tested in an important application scenario such as tourist recommendation where several experiment have been performed to assess system scalability and data read/write efficiency. The study of this system and of advanced analysis on Big Data has shown the need for a huge computing power. To this end an high performance computing system named CoremunitiTM is presented. This system represents a P2P solution for solving complex works by using the idling computational resources of users connected to this network. Users help each other by asking the network computational resources when they face high computing demanding tasks. Differently from many proposals available for volunteer computing, users providing their resources are rewarded with tangible credits. This approach is tested in an interesting scenario as 3D rendering where its efficiency has been compared with "traditional" commercial solutions like cloud platforms and render farms showing shorter task completion times at low cost.