Development of a framework for the analysis of document-based phishing campaigns

In questo elaborato di tesi si affronta lo sviluppo di un framework per l'analisi di URL di phishing estratte da documenti malevoli. Tramite il linguaggio python3 e browsers automatizzati si è sviluppata una pipeline per analizzare queste campagne malevole. La pipeline ha lo scopo di arrivare alla pagina finale, evitando di essere bloccata da tecniche anti-bot di cloaking, per catturare una schermata e salvare la pagina in locale. Durante l'analisi tutto il traffico è salvato per analisi future. Ad ogni URL visitato vengono salvate informazioni quali entry DNS, codice di Autonomous System e lo stato nella blocklist di Google. Un'analisi iniziale delle due campagne più estese è stata effettuata, rivelando il business model dietro ad esse e le tecniche usate per proteggere l'infrastruttura stessa.

Machine learning "as a service" for high energy physics (MLaaS4HEP): evolution of a framework for ML-based physics analyses

The scientific success of the LHC experiments at CERN highly depends on the availability of computing resources which efficiently store, process, and analyse the amount of data collected every year. This is ensured by the Worldwide LHC Computing Grid infrastructure that connect computing centres distributed all over the world with high performance network. LHC has an ambitious experimental program for the coming years, which includes large investments and improvements both for the hardware of the detectors and for the software and computing systems, in order to deal with the huge increase in the event rate expected from the High Luminosity LHC (HL-LHC) phase and consequently with the huge amount of data that will be produced. Since few years the role of Artificial Intelligence has become relevant in the High Energy Physics (HEP) world. Machine Learning (ML) and Deep Learning algorithms have been successfully used in many areas of HEP, like online and offline reconstruction programs, detector simulation, object reconstruction, identification, Monte Carlo generation, and surely they will be crucial in the HL-LHC phase. This thesis aims at contributing to a CMS R&D project, regarding a ML "as a Service" solution for HEP needs (MLaaS4HEP). It consists in a data-service able to perform an entire ML pipeline (in terms of reading data, processing data, training ML models, serving predictions) in a completely model-agnostic fashion, directly using ROOT files of arbitrary size from local or distributed data sources. This framework has been updated adding new features in the data preprocessing phase, allowing more flexibility to the user. Since the MLaaS4HEP framework is experiment agnostic, the ATLAS Higgs Boson ML challenge has been chosen as physics use case, with the aim to test MLaaS4HEP and the contribution done with this work.

Numerical investigation of turbulent/non-turbulent interface

The subject of this work is the diffusion of turbulence in a non-turbulent flow. Such phenomenon can be found in almost every practical case of turbulent flow: all types of shear flows (wakes, jet, boundary layers) present some boundary between turbulence and the non-turbulent surround; all transients from a laminar flow to turbulence must account for turbulent diffusion; mixing of flows often involve the injection of a turbulent solution in a non-turbulent fluid. The mechanism of what Phillips defined as “the erosion by turbulence of the underlying non-turbulent flow”, is called entrainment. It is usually considered to operate on two scales with different mechanics. The small scale nibbling, which is the entrainment of fluid by viscous diffusion of turbulence, and the large scale engulfment, which entraps large volume of flow to be “digested” subsequently by viscous diffusion. The exact role of each of them in the overall entrainment rate is still not well understood, as it is the interplay between these two mechanics of diffusion. It is anyway accepted that the entrainment rate scales with large properties of the flow, while is not understood how the large scale inertial behavior can affect an intrinsically viscous phenomenon as diffusion of vorticity. In the present work we will address then the problem of turbulent diffusion through pseudo-spectral DNS simulations of the interface between a volume of decaying turbulence and quiescent flow. Such simulations will give us first hand measures of velocity, vorticity and strains fields at the interface; moreover the framework of unforced decaying turbulence will permit to study both spatial and temporal evolution of such fields. The analysis will evidence that for this kind of flows the overall production of enstrophy , i.e. the square of vorticity omega^2 , is dominated near the interface by the local inertial transport of “fresh vorticity” coming from the turbulent flow. Viscous diffusion instead plays a major role in enstrophy production in the outbound of the interface, where the nibbling process is dominant. The data from our simulation seems to confirm the theory of an inertially stirred viscous phenomenon proposed by others authors before and provides new data about the inertial diffusion of turbulence across the interface.