Data breach - services for threat detection, analysis and response

Nel mondo della sicurezza informatica, le tecnologie si evolvono per far fronte alle minacce. Non è possibile prescindere dalla prevenzione, ma occorre accettare il fatto che nessuna barriera risulterà impenetrabile e che la rilevazione, unitamente ad una pronta risposta, rappresenta una linea estremamente critica di difesa, ma l’unica veramente attuabile per poter guadagnare più tempo possibile o per limitare i danni. Introdurremo quindi un nuovo modello operativo composto da procedure capaci di affrontare le nuove sfide che il malware costantemente offre e allo stesso tempo di sollevare i comparti IT da attività onerose e sempre più complesse, ottimizzandone il processo di comunicazione e di risposta.

Twitter data analysis for financial markets

Over the time, Twitter has become a fundamental source of information for news. As a one step forward, researchers have tried to analyse if the tweets contain predictive power. In the past, in financial field, a lot of research has been done to propose a function which takes as input all the tweets for a particular stock or index s, analyse them and predict the stock or index price of s. In this work, we take an alternative approach: using the stock price and tweet information, we investigate following questions. 1. Is there any relation between the amount of tweets being generated and the stocks being exchanged? 2. Is there any relation between the sentiment of the tweets and stock prices? 3. What is the structure of the graph that describes the relationships between users?

The atmospheric stable boundary layer: Data analysis and comparison with similarity theories

The objective of this dissertation is to study the structure and behavior of the Atmospheric Boundary Layer (ABL) in stable conditions. This type of boundary layer is not completely well understood yet, although it is very important for many practical uses, from forecast modeling to atmospheric dispersion of pollutants. We analyzed data from the SABLES98 experiment (Stable Atmospheric Boundary Layer Experiment in Spain, 1998), and compared the behaviour of this data using Monin-Obukhov's similarity functions for wind speed and potential temperature. Analyzing the vertical profiles of various variables, in particular the thermal and momentum fluxes, we identified two main contrasting structures describing two different states of the SBL, a traditional and an upside-down boundary layer. We were able to determine the main features of these two states of the boundary layer in terms of vertical profiles of potential temperature and wind speed, turbulent kinetic energy and fluxes, studying the time series and vertical structure of the atmosphere for two separate nights in the dataset, taken as case studies. We also developed an original classification of the SBL, in order to separate the influence of mesoscale phenomena from turbulent behavior, using as parameters the wind speed and the gradient Richardson number. We then compared these two formulations, using the SABLES98 dataset, verifying their validity for different variables (wind speed and potential temperature, and their difference, at different heights) and with different stability parameters (zita or Rg). Despite these two classifications having completely different physical origins, we were able to find some common behavior, in particular under weak stability conditions.

Evaluation of a cloud infrastructure for the CMS distributed data analysis in the top quark sector at the LHC

Nella fisica delle particelle, onde poter effettuare analisi dati, è necessario disporre di una grande capacità di calcolo e di storage. LHC Computing Grid è una infrastruttura di calcolo su scala globale e al tempo stesso un insieme di servizi, sviluppati da una grande comunità di fisici e informatici, distribuita in centri di calcolo sparsi in tutto il mondo. Questa infrastruttura ha dimostrato il suo valore per quanto riguarda l'analisi dei dati raccolti durante il Run-1 di LHC, svolgendo un ruolo fondamentale nella scoperta del bosone di Higgs. Oggi il Cloud computing sta emergendo come un nuovo paradigma di calcolo per accedere a grandi quantità di risorse condivise da numerose comunità scientifiche. Date le specifiche tecniche necessarie per il Run-2 (e successivi) di LHC, la comunità scientifica è interessata a contribuire allo sviluppo di tecnologie Cloud e verificare se queste possano fornire un approccio complementare, oppure anche costituire una valida alternativa, alle soluzioni tecnologiche esistenti. Lo scopo di questa tesi è di testare un'infrastruttura Cloud e confrontare le sue prestazioni alla LHC Computing Grid. Il Capitolo 1 contiene un resoconto generale del Modello Standard. Nel Capitolo 2 si descrive l'acceleratore LHC e gli esperimenti che operano a tale acceleratore, con particolare attenzione all’esperimento CMS. Nel Capitolo 3 viene trattato il Computing nella fisica delle alte energie e vengono esaminati i paradigmi Grid e Cloud. Il Capitolo 4, ultimo del presente elaborato, riporta i risultati del mio lavoro inerente l'analisi comparata delle prestazioni di Grid e Cloud.

Data analysis and simulations of VIRTIS venus spectra

VIRTIS, a bordo di Venus Express, è uno spettrometro in grado di operare da 0.25 a 5 µm. Nel periodo 2006-2011 ha ricavato un'enorme mole di dati ma a tutt'oggi le osservazioni al lembo sono poco utilizzate per lo studio delle nubi e delle hazes, specialmente di notte. Gli spettri al lembo a quote mesosferiche sono dominati dalla radianza proveniente dalle nubi e scatterata in direzione dello strumento dalle hazes. L'interpretazione degli spettri al lembo non può quindi prescindere dalla caratterizzazione dell'intera colonna atmosferica. L'obiettivo della tesi è di effettuare un’analisi statistica sulle osservazioni al nadir e proporre una metodologia per ricavare una caratterizzazione delle hazes combinando osservazioni al nadir e al lembo. La caratterizzazione delle nubi è avvenuta su un campione di oltre 3700 osservazioni al nadir. È stato creato un ampio dataset di spettri sintetici modificando, in un modello iniziale, vari parametri di nube quali composizione chimica, numero e dimensione delle particelle. Un processo di fit è stato applicato alle osservazioni per stabilire quale modello potesse descrivere gli spettri osservati. Si è poi effettuata una analisi statistica sui risultati del campione. Si è ricavata una concentrazione di acido solforico molto elevata nelle nubi basse, pari al 96% in massa, che si discosta dal valore generalmente utilizzato del 75%. Si sono poi integrati i risultati al nadir con uno studio mirato su poche osservazioni al lembo, selezionate in modo da intercettare nel punto di tangenza la colonna atmosferica osservata al nadir, per ricavare informazioni sulle hazes. I risultati di un modello Monte Carlo indicano che il numero e le dimensioni delle particelle previste dal modello base devono essere ridotte in maniera significativa. In particolare si osserva un abbassamento della quota massima delle hazes rispetto ad osservazioni diurne.

Data analysis of collapse mechanisms of a 3D printed groin vault in shaking table testing

The aim of this novel experimental study is to investigate the behaviour of a 2m x 2m model of a masonry groin vault, which is built by the assembly of blocks made of a 3D-printed plastic skin filled with mortar. The choice of the groin vault is due to the large presence of this vulnerable roofing system in the historical heritage. Experimental tests on the shaking table are carried out to explore the vault response on two support boundary conditions, involving four lateral confinement modes. The data processing of markers displacement has allowed to examine the collapse mechanisms of the vault, based on the arches deformed shapes. There then follows a numerical evaluation, to provide the orders of magnitude of the displacements associated to the previous mechanisms. Given that these displacements are related to the arches shortening and elongation, the last objective is the definition of a critical elongation between two diagonal bricks and consequently of a diagonal portion. This study aims to continue the previous work and to take another step forward in the research of ground motion effects on masonry structures.

Deployment of a data analysis workflow of the ATLAS experiment on HPC systems

LHC experiments produce an enormous amount of data, estimated of the order of a few PetaBytes per year. Data management takes place using the Worldwide LHC Computing Grid (WLCG) grid infrastructure, both for storage and processing operations. However, in recent years, many more resources are available on High Performance Computing (HPC) farms, which generally have many computing nodes with a high number of processors. Large collaborations are working to use these resources in the most efficient way, compatibly with the constraints imposed by computing models (data distributed on the Grid, authentication, software dependencies, etc.). The aim of this thesis project is to develop a software framework that allows users to process a typical data analysis workflow of the ATLAS experiment on HPC systems. The developed analysis framework shall be deployed on the computing resources of the Open Physics Hub project and on the CINECA Marconi100 cluster, in view of the switch-on of the Leonardo supercomputer, foreseen in 2023.

PLUME preliminary study and first Van der Meer scan data analysis

Il rilevatore Probe for LUminosity MEasurement (PLUME) è un luminometro per l’esperimento LHCb al CERN. Fornirà misurazioni istantanee della luminosità per LHCb durante la Run 3 a LHC. L’obiettivo di questa tesi è di valutare, con dati simulati, le prestazioni attese di PLUME, come l’occupanza dei PMT che compongono il rivelatore, e riportare l’analisi dei primi dati ottenuti da PLUME durante uno scan di Van der Meer. In particolare, sono state ottenuti tre misure del valore della sezione d’urto, necessarie per tarare il rivelatore, ovvero σ1Da = (1.14 ± 0.11) mb, σ1Db = (1.13 ± 0.10) mb, σ2D = (1.20 ± 0.02) mb, dove i pedici 1D e 2D corrispondono a uno scan di Van der Meer unidimensionale e bidimensionale. Tutti i risultati sono in accordo tra loro.

Smart Data Analysis on Lamborghini Connected Vehicles

The thesis is the result of work conducted during a period of six months at the Strategy department of Automobili Lamborghini S.p.A. in Sant'Agata Bolognese (BO) and concerns the study and analysis of Big Data relating to Lamborghini's connected cars. The Big Data is a project of Connected Car Project House, that is an inter-departmental team which works toward the definition of the Lamborghini corporate connectivity strategy and its implementation in the product portfolio. The Data of the connected cars is one of the hottest topics right now in the automotive industry; in fact, all the largest automotive companies are investi,ng a lot in this direction, in order to derive the greatest advantages both from a purely economic point of view, because from these data you can understand a lot the behaviors and habits of each driver, and from a technological point of view because it will increasingly promote the development of 5G that will be an important enabler for the future of connectivity. The main purpose of the work by Lamborghini prospective is to analyze the data of the connected cars, in particular a data-set referred to connected Huracans that had been already placed on the market, and, starting from that point, derive valuable Key Performance Indicators (KPIs) on which the company could partly base the decisions to be made in the near future. The key result that we have obtained at the end of this period was the creation of a Dashboard, in which is possible to visualize many parameters and indicators both related to driving habits and the use of the vehicle itself, which has brought great insights on the huge potential and value that is present behind the study of these data. The final Demo of the project has received great interest, not only from the whole strategy department but also from all the other business areas of Lamborghini, making mostly a great awareness that this will be the road to follow in the coming years.

Applicazione della Metodologia Agile e di tecniche di Data Analysis nello sviluppo di un sistema di e-grocery

I principi Agile, pubblicati nell’omonimo Manifesto più di 20 anni fa, al giorno d’oggi sono declinati in una moltitudine di framework: Scrum, XP, Kanban, Lean, Adaptive, Crystal, etc. Nella prima parte della tesi (Capitoli 1 e 2) sono stati descritti alcuni di questi framework e si è analizzato come un approccio Agile è utilizzato nella pratica in uno specifico caso d’uso: lo sviluppo di una piattaforma software a supporto di un sistema di e-grocery da parte di un team di lab51. Si sono verificate le differenze e le similitudini rispetto alcuni metodi Agile formalizzati in letteratura spiegando le motivazioni che hanno portato a differenziarsi da questi framework illustrando i vantaggi per il team. Nella seconda parte della tesi (Capitoli 3 e 4) è stata effettuata un’analisi dei dati raccolti dal supermercato online negli ultimi anni con l’obiettivo di migliorare l’algoritmo di riordino. In particolare, per prevedere le vendite dei singoli prodotti al fine di avere degli ordini più adeguati in quantità e frequenza, sono stati studiati vari approcci: dai modelli statistici di time series forecasting, alle reti neurali, fino ad una metodologia sviluppata ad hoc.

Data analysis and hydrological modelling of rainfall and discharge extremes along the Tione River: implications for the design of water structures

There are many natural events that can negatively affect the urban ecosystem, but weather-climate variations are certainly among the most significant. The history of settlements has been characterized by extreme events like earthquakes and floods, which repeat themselves at different times, causing extensive damage to the built heritage on a structural and urban scale. Changes in climate also alter various climatic subsystems, changing rainfall regimes and hydrological cycles, increasing the frequency and intensity of extreme precipitation events (heavy rainfall).  From an hydrological risk perspective, it is crucial to understand future events that could occur and their magnitude in order to design safer infrastructures. Unfortunately, it is not easy to understand future scenarios as the complexity of climate is enormous.  For this thesis, precipitation and discharge extremes were primarily used as data sources. It is important to underline that the two data sets are not separated: changes in rainfall regime, due to climate change, could significantly affect overflows into receiving water bodies. It is imperative that we understand and model climate change effects on water structures to support the development of adaptation strategies.   The main purpose of this thesis is to search for suitable water structures for a road located along the Tione River. Therefore, through the analysis of the area from a hydrological point of view, we aim to guarantee the safety of the infrastructure over time.   The observations made have the purpose to underline how models such as a stochastic one can improve the quality of an analysis for design purposes, and influence choices.

Synthetic data generation for the assessment of antimicrobial resistance through machine learning

As a consequence of the diffusion of next generation sequencing techniques, metagenomics databases have become one of the most promising repositories of information about features and behavior of microorganisms. One of the subjects that can be studied from those data are bacteria populations. Next generation sequencing techniques allow to study the bacteria population within an environment by sampling genetic material directly from it, without the needing of culturing a similar population in vitro and observing its behavior. As a drawback, it is quite complex to extract information from those data and usually there is more than one way to do that; AMR is no exception. In this study we will discuss how the quantified AMR, which regards the genotype of the bacteria, can be related to the bacteria phenotype and its actual level of resistance against the specific substance. In order to have a quantitative information about bacteria genotype, we will evaluate the resistome from the read libraries, aligning them against CARD database. With those data, we will test various machine learning algorithms for predicting the bacteria phenotype. The samples that we exploit should resemble those that could be obtained from a natural context, but are actually produced by a read libraries simulation tool. In this way we are able to design the populations with bacteria of known genotype, so that we can relay on a secure ground truth for training and testing our algorithms.