Security enhancements and flaws of emerging communication technologies

The multi-faced evolution of network technologies ranges from big data centers to specialized network infrastructures and protocols for mission-critical operations. For instance, technologies such as Software Defined Networking (SDN) revolutionized the world of static configuration of the network - i.e., by removing the distributed and proprietary configuration of the switched networks - centralizing the control plane. While this disruptive approach is interesting from different points of view, it can introduce new unforeseen vulnerabilities classes. One topic of particular interest in the last years is industrial network security, an interest which started to rise in 2016 with the introduction of the Industry 4.0 (I4.0) movement. Networks that were basically isolated by design are now connected to the internet to collect, archive, and analyze data. While this approach got a lot of momentum due to the predictive maintenance capabilities, these network technologies can be exploited in various ways from a cybersecurity perspective. Some of these technologies lack security measures and can introduce new families of vulnerabilities. On the other side, these networks can be used to enable accurate monitoring, formal verification, or defenses that were not practical before. This thesis explores these two fields: by introducing monitoring, protections, and detection mechanisms where the new network technologies make it feasible; and by demonstrating attacks on practical scenarios related to emerging network infrastructures not protected sufficiently. The goal of this thesis is to highlight this lack of protection in terms of attacks on and possible defenses enabled by emerging technologies. We will pursue this goal by analyzing the aforementioned technologies and by presenting three years of contribution to this field. In conclusion, we will recapitulate the research questions and give answers to them.

Diffusive models and chaos indicators for non-linear betatron motion in circular hadron accelerators

Understanding the complex dynamics of beam-halo formation and evolution in circular particle accelerators is crucial for the design of current and future rings, particularly those utilizing superconducting magnets such as the CERN Large Hadron Collider (LHC), its luminosity upgrade HL-LHC, and the proposed Future Circular Hadron Collider (FCC-hh). A recent diffusive framework, which describes the evolution of the beam distribution by means of a Fokker-Planck equation, with diffusion coefficient derived from the Nekhoroshev theorem, has been proposed to describe the long-term behaviour of beam dynamics and particle losses. In this thesis, we discuss the theoretical foundations of this framework, and propose the implementation of an original measurement protocol based on collimator scans in view of measuring the Nekhoroshev-like diffusive coefficient by means of beam loss data. The available LHC collimator scan data, unfortunately collected without the proposed measurement protocol, have been successfully analysed using the proposed framework. This approach is also applied to datasets from detailed measurements of the impact on the beam losses of so-called long-range beam-beam compensators also at the LHC. Furthermore, dynamic indicators have been studied as a tool for exploring the phase-space properties of realistic accelerator lattices in single-particle tracking simulations. By first examining the classification performance of known and new indicators in detecting the chaotic character of initial conditions for a modulated Hénon map and then applying this knowledge to study the properties of realistic accelerator lattices, we tried to identify a connection between the presence of chaotic regions in the phase space and Nekhoroshev-like diffusive behaviour, providing new tools to the accelerator physics community.

Deep learning and embeddings for problems of computational biology

The development of Next Generation Sequencing promotes Biology in the Big Data era. The ever-increasing gap between proteins with known sequences and those with a complete functional annotation requires computational methods for automatic structure and functional annotation. My research has been focusing on proteins and led so far to the development of three novel tools, DeepREx, E-SNPs&GO and ISPRED-SEQ, based on Machine and Deep Learning approaches. DeepREx computes the solvent exposure of residues in a protein chain. This problem is relevant for the definition of structural constraints regarding the possible folding of the protein. DeepREx exploits Long Short-Term Memory layers to capture residue-level interactions between positions distant in the sequence, achieving state-of-the-art performances. With DeepRex, I conducted a large-scale analysis investigating the relationship between solvent exposure of a residue and its probability to be pathogenic upon mutation. E-SNPs&GO predicts the pathogenicity of a Single Residue Variation. Variations occurring on a protein sequence can have different effects, possibly leading to the onset of diseases. E-SNPs&GO exploits protein embeddings generated by two novel Protein Language Models (PLMs), as well as a new way of representing functional information coming from the Gene Ontology. The method achieves state-of-the-art performances and is extremely time-efficient when compared to traditional approaches. ISPRED-SEQ predicts the presence of Protein-Protein Interaction sites in a protein sequence. Knowing how a protein interacts with other molecules is crucial for accurate functional characterization. ISPRED-SEQ exploits a convolutional layer to parse local context after embedding the protein sequence with two novel PLMs, greatly surpassing the current state-of-the-art. All methods are published in international journals and are available as user-friendly web servers. They have been developed keeping in mind standard guidelines for FAIRness (FAIR: Findable, Accessible, Interoperable, Reusable) and are integrated into the public collection of tools provided by ELIXIR, the European infrastructure for Bioinformatics.

Anticancer drug discovery using artificial intelligence: an application in pharmacological activity prediction

Hematological cancers are a heterogeneous family of diseases that can be divided into leukemias, lymphomas, and myelomas, often called “liquid tumors”. Since they cannot be surgically removable, chemotherapy represents the mainstay of their treatment. However, it still faces several challenges like drug resistance and low response rate, and the need for new anticancer agents is compelling. The drug discovery process is long-term, costly, and prone to high failure rates. With the rapid expansion of biological and chemical "big data", some computational techniques such as machine learning tools have been increasingly employed to speed up and economize the whole process. Machine learning algorithms can create complex models with the aim to determine the biological activity of compounds against several targets, based on their chemical properties. These models are defined as multi-target Quantitative Structure-Activity Relationship (mt-QSAR) and can be used to virtually screen small and large chemical libraries for the identification of new molecules with anticancer activity. The aim of my Ph.D. project was to employ machine learning techniques to build an mt-QSAR classification model for the prediction of cytotoxic drugs simultaneously active against 43 hematological cancer cell lines. For this purpose, first, I constructed a large and diversified dataset of molecules extracted from the ChEMBL database. Then, I compared the performance of different ML classification algorithms, until Random Forest was identified as the one returning the best predictions. Finally, I used different approaches to maximize the performance of the model, which achieved an accuracy of 88% by correctly classifying 93% of inactive molecules and 72% of active molecules in a validation set. This model was further applied to the virtual screening of a small dataset of molecules tested in our laboratory, where it showed 100% accuracy in correctly classifying all molecules. This result is confirmed by our previous in vitro experiments.

Anatomical biomarkers of body composition in oncological population: application and development of artificial intelligences

In the Era of precision medicine and big medical data sharing, it is necessary to solve the work-flow of digital radiological big data in a productive and effective way. In particular, nowadays, it is possible to extract information “hidden” in digital images, in order to create diagnostic algorithms helping clinicians to set up more personalized therapies, which are in particular targets of modern oncological medicine. Digital images generated by the patient have a “texture” structure that is not visible but encrypted; it is “hidden” because it cannot be recognized by sight alone. Thanks to artificial intelligence, pre- and post-processing software and generation of mathematical calculation algorithms, we could perform a classification based on non-visible data contained in radiological images. Being able to calculate the volume of tissue body composition could lead to creating clasterized classes of patients inserted in standard morphological reference tables, based on human anatomy distinguished by gender and age, and maybe in future also by race. Furthermore, the branch of “morpho-radiology" is a useful modality to solve problems regarding personalized therapies, which is particularly needed in the oncological field. Actually oncological therapies are no longer based on generic drugs but on target personalized therapy. The lack of gender and age therapies table could be filled thanks to morpho-radiology data analysis application.

Evaluation of a multi variated analysis for the selection of t-tbar multijet events in the current CMS implemented environment at LHC

In the present study we are using multi variate analysis techniques to discriminate signal from background in the fully hadronic decay channel of ttbar events. We give a brief introduction to the role of the Top quark in the standard model and a general description of the CMS Experiment at LHC. We have used the CMS experiment computing and software infrastructure to generate and prepare the data samples used in this analysis. We tested the performance of three different classifiers applied to our data samples and used the selection obtained with the Multi Layer Perceptron classifier to give an estimation of the statistical and systematical uncertainty on the cross section measurement.

Drift Tubes Trigger System of the CMS Experiment at LHC : Commissioning and Performances

In this thesis the performances of the CMS Drift Tubes Local Trigger System of the CMS detector are studied. CMS is one of the general purpose experiments that will operate at the Large Hadron Collider at CERN. Results from data collected during the Cosmic Run At Four Tesla (CRAFT) commissioning exercise, a globally coordinated run period where the full experiment was involved and configured to detect cosmic rays crossing the CMS cavern, are presented. These include analyses on the precision and accuracy of the trigger reconstruction mechanism and measurement of the trigger efficiency. The description of a method to perform system synchronization is also reported, together with a comparison of the outcomes of trigger electronics and its software emulator code.

Study of the X(3872) state with the CMS experiment at LHC

The surprising discovery of the X(3872) resonance by the Belle experiment in 2003, and subsequent confirmation by BaBar, CDF and D0, opened up a new chapter of QCD studies and puzzles. Since then, detailed experimental and theoretical studies have been performed in attempt to determine and explain the proprieties of this state. Since the end of 2009 the world’s largest and highest-energy particle accelerator, the Large Hadron Collider (LHC), started its operations at the CERN laboratories in Geneva. One of the main experiments at LHC is CMS (Compact Muon Solenoid), a general purpose detector projected to address a wide range of physical phenomena, in particular the search of the Higgs boson, the only still unconfirmed element of the Standard Model (SM) of particle interactions and, new physics beyond the SM itself. Even if CMS has been designed to study high energy events, it’s high resolution central tracker and superior muon spectrometer made it an optimal tool to study the X(3872) state. In this thesis are presented the results of a series of study on the X(3872) state performed with the CMS experiment. Already with the first year worth of data, a clear peak for the X(3872) has been identified, and the measurement of the cross section ratio with respect to the Psi(2S) has been performed. With the increased statistic collected during 2011 it has been possible to study, in bins of transverse momentum, the cross section ratio between X(3872) and Psi(2S) and separate their prompt and non-prompt component.

Search for the MSSM Neutral Higgs Boson in the mu+mu- final state with the CMS experiment at LHC

In this thesis, my work in the Compact Muon Solenoid (CMS) experiment on the search for the neutral Minimal Supersymmetric Standard Model (MSSM) Higgs decaying into two muons is presented. The search is performed on the full data collected during the years 2011 and 2012 by CMS in proton-proton collisions at CERN Large Hadron Collider (LHC). The MSSM is explored within the most conservative benchmark scenario, m_h^{max}, and within its modified versions, m_h^{mod +} and m_h^{mod -}. The search is sensitive to MSSM Higgs boson production in association with a b\bar{b} quark pair and to the gluon-gluon fusion process. In the m_h^{max} scenario, the results exclude values of tanB larger than 15 in the m_A range 115-200 GeV, and values of tanB greater than 30 in the m_A range up to 300 GeV. There are no significant differences in the results obtained within the three different scenarios considered. Comparisons with other neutral MSSM Higgs searches are shown.

Search for heavy neutrinos in $D_s$ decays with the CMS detector at LHC

This thesis presents a search for a sterile right-handed neutrino $N$ produced in $D_s$ meson decays, using proton-proton collisions collected by the CMS experiment at the LHC. The data set used for the analysis, the B-Parking data set, corresponds to an integrated luminosity of $41.7\,\textrm{fb}^{-1}$ and was collected during the 2018 data-taking period. The analysis is targeting the $D_s^+\rightarrow N(\rightarrow\mu^{\pm}\pi^{\mp})\mu^{+}$ decays, where the final state muons can have the same electric charge allowing for a lepton flavor violating decay. To separate signal from background, a cut-based analysis is optimized using requirements on the sterile neutrino vertex displacement, muon and pion impact parameter, and impact parameter significance. The expected limit on the active-sterile neutrino mixing matrix parameter $|V_{\mu}|^2$ is extracted by performing a fit of the $\mu\pi$ invariant mass spectrum for two sterile neutrino mass hypotheses, 1.0 and 1.5 GeV. The analysis is currently blinded, following the internal CMS review process. The expected limit ranges between approximately $10^{-4}$ for a 1.0 GeV neutrino to $7\times10^{-5}$ for a 1.5 GeV neutrino. This is competitive with the best existing results from collider experiments over the same mass range.

Sensitivity of the Top quark mass measurement with the CMS experiment at LHC using t-tbar multijet simulated events

This thesis comes after a strong contribution on the realization of the CMS computing system, which can be seen as a relevant part of the experiment itself. A physics analysis completes the road from Monte Carlo production and analysis tools realization to the final physics study which is the actual goal of the experiment. The topic of physics work of this thesis is the study of tt events fully hadronic decay in the CMS experiment. A multi-jet trigger has been provided to fix a reasonable starting point, reducing the multi-jet sample to the nominal trigger rate. An offline selection has been provided to reduce the S/B ratio. The b-tag is applied to provide a further S/B improvement. The selection is applied to the background sample and to the samples generated at different top quark masses. The top quark mass candidate is reconstructed for all those samples using a kinematic fitter. The resulting distributions are used to build p.d.f.’s, interpolating them with a continuous arbitrary curve. These curves are used to perform the top mass measurement through a likelihood comparison

The new Beam Halo Monitor for the CMS experiment at the LHC

In the context of increasing beam energy and luminosity of the LHC accelerator at CERN, it will be important to accurately measure the Machine Induced Background. A new monitoring system will be installed in the CMS cavern for measuring the beam background at high radius. This detector, called the Beam Halo Monitor, will provide an online, bunch-by-bunch measurement of background induced by beam halo interactions, separately for each beam. The detector is composed of synthetic quartz Cherenkov radiators, coupled to fast UV sensitive photomultiplier tubes. The directional and fast response of the system allows the discrimination of the background particles from the dominant flux in the cavern induced by pp collision debris, produced within the 25 ns bunch spacing. The readout electronics of this detector will make use of many components developed for the upgrade of the CMS Hadron Calorimeter electronics, with a dedicated firmware and readout adapted to the beam monitoring requirements. The PMT signal will be digitized by a charge integrating ASIC, providing both the signal rise time and the charge integrated over one bunch crossing. The backend electronics will record bunch-by-bunch histograms, which will be published to CMS and the LHC using the newly designed CMS beam instrumentation specific DAQ. A calibration and monitoring system has been designed to generate triggered pulses of UV light to monitor the efficiency of the system. The experimental results validating the design of the detector, the calibration system and the electronics will be presented.

Application of Deep Learning techniques in the search for BSM Higgs bosons in the $\mu\mu$ final state in CMS

The Standard Model (SM) of particle physics predicts the existence of a Higgs field responsible for the generation of particles' mass. However, some aspects of this theory remain unsolved, supposing the presence of new physics Beyond the Standard Model (BSM) with the production of new particles at a higher energy scale compared to the current experimental limits. The search for additional Higgs bosons is, in fact, predicted by theoretical extensions of the SM including the Minimal Supersymmetry Standard Model (MSSM). In the MSSM, the Higgs sector consists of two Higgs doublets, resulting in five physical Higgs particles: two charged bosons $H^{\pm}$, two neutral scalars $h$ and $H$, and one pseudoscalar $A$. The work presented in this thesis is dedicated to the search of neutral non-Standard Model Higgs bosons decaying to two muons in the model independent MSSM scenario. Proton-proton collision data recorded by the CMS experiment at the CERN LHC at a center-of-mass energy of 13 TeV are used, corresponding to an integrated luminosity of $35.9\ \text{fb}^{-1}$. Such search is sensitive to neutral Higgs bosons produced either via gluon fusion process or in association with a $\text{b}\bar{\text{b}}$ quark pair. The extensive usage of Machine and Deep Learning techniques is a fundamental element in the discrimination between signal and background simulated events. A new network structure called parameterised Neural Network (pNN) has been implemented, replacing a whole set of single neural networks trained at a specific mass hypothesis value with a single neural network able to generalise well and interpolate in the entire mass range considered. The results of the pNN signal/background discrimination are used to set a model independent 95\% confidence level expected upper limit on the production cross section times branching ratio, for a generic $\phi$ boson decaying into a muon pair in the 130 to 1000 GeV range.

The Time-Of-Flight detector of ALICE at LHC: construction, test and commissioning with cosmic rays

The Time-Of-Flight (TOF) detector of ALICE is designed to identify charged particles produced in Pb--Pb collisions at the LHC to address the physics of strongly-interacting matter and the Quark-Gluon Plasma (QGP). The detector is based on the Multigap Resistive Plate Chamber (MRPC) technology which guarantees the excellent performance required for a large time-of-flight array. The construction and installation of the apparatus in the experimental site have been completed and the detector is presently fully operative. All the steps which led to the construction of the TOF detector were strictly followed by a set of quality assurance procedures to enable high and uniform performance and eventually the detector has been commissioned with cosmic rays. This work aims at giving a detailed overview of the ALICE TOF detector, also focusing on the tests performed during the construction phase. The first data-taking experience and the first results obtained with cosmic rays during the commissioning phase are presented as well and allow to confirm the readiness state of the TOF detector for LHC collisions.

Hardware and software development of a multichannel readout board named CARLOSrx for the ALICE experiment

ALICE, that is an experiment held at CERN using the LHC, is specialized in analyzing lead-ion collisions. ALICE will study the properties of quarkgluon plasma, a state of matter where quarks and gluons, under conditions of very high temperatures and densities, are no longer confined inside hadrons. Such a state of matter probably existed just after the Big Bang, before particles such as protons and neutrons were formed. The SDD detector, one of the ALICE subdetectors, is part of the ITS that is composed by 6 cylindrical layers with the innermost one attached to the beam pipe. The ITS tracks and identifies particles near the interaction point, it also aligns the tracks of the articles detected by more external detectors. The two ITS middle layers contain the whole 260 SDD detectors. A multichannel readout board, called CARLOSrx, receives at the same time the data coming from 12 SDD detectors. In total there are 24 CARLOSrx boards needed to read data coming from all the SDD modules (detector plus front end electronics). CARLOSrx packs data coming from the front end electronics through optical link connections, it stores them in a large data FIFO and then it sends them to the DAQ system. Each CARLOSrx is composed by two boards. One is called CARLOSrx data, that reads data coming from the SDD detectors and configures the FEE; the other one is called CARLOSrx clock, that sends the clock signal to all the FEE. This thesis contains a description of the hardware design and firmware features of both CARLOSrx data and CARLOSrx clock boards, which deal with all the SDD readout chain. A description of the software tools necessary to test and configure the front end electronics will be presented at the end of the thesis.