The Degree in Bioinformatics integrates knowledge of programming, algorithms, systems and architectures, and computer science fundamentals, with basic knowledge of chemistry, biochemistry and biology, such as cell biology. This combination provides the necessary training to work in a new cutting edge field at the forefront of computer science and biology which, for example, has made human genome sequencing possible.

The programme provides the skills needed to work in applied computer science for medicine and biology, sectors which are increasingly using information technology for tasks such as accessing genomic databases and analysing data from biotechnology laboratories. The degree programme lays the foundation to address specific topics in advanced bioinformatics that will have a growing impact on medicine and natural sciences in the years to come.

The Master degree in Computer Science and Engineering has been running at the University of Verona since the academic year 2009-2010. It aims to provide students with the methodological bases for addressing issues related to the design, analysis and development of complex computer systems.

Part of the programme is dedicated to deepening and expanding upon on fundamental knowledge acquired in the Bachelor degree (in Science or Engineering), providing students with a host of tools for looking at complex problems in the field. Another part of the programme provides state of the art knowledge in the main methods of modelling, design, analysis and evaluation of algorithms and systems (hardware and software) able to manipulate data from either discrete or continuous sources.

Erasmus+ in Murcia, Spain

I have been in Erasmus in Murcia, Spain, where I began the work for my master thesis under the supervision of prof. Gregorio Martínez P´rez.

Master thesis

Modeling Cyber-Threats: adopting Bayes' principles in the attack graph theory

This master thesis will analyze how the Bayesian Theory can be applied to the Intrusion Prevention and Response Strategy research area. I am going to present a brief summary on the graphical security modelling technique, with the objective ofdescribing a common point between the existing formalism and aiming to implement a Security Model Simulator that allows the expert to both run and compare different solutions and approaches to the same problem (or architecture).

The focus of this work is on the simulator, presented in chapter 3, in particular on the technical details of the implementation and on the innate difficulties related to the lack of standard basic structure.

I am currently researching the applicability of Artificial Intelligence solutions to cybersecurity challenges, especially related to anomaly detection and behavioural analysis.

Ph.d. Thesis - Provisional

Tackling cybersecurity network threats with artificial intelligence

Artificial Intelligence (AI) may achieve results otherwise precluded to human administrators. Specifically, Machine Learning (ML) models may drastically improve notable properties such as performances, precision and efficacy all through the security process, i.e. from the threat detection to the automated countermeasure deployment, providing alignment with the recently defined Moving Target Defence (MTD) paradigm

TELL-OP is a Strategic Partnership that seeks to promote the take-up of innovative practices in European language learning (Data Driven Learning, DDL) by supporting personalised learning approaches that rely on the use of ICT & OER by bringing together the knowledge & expertise of European stakeholders in the fields of language education, corpus & applied linguistics, e-learning & knowledge engineering in order to promote cooperation & contribute to unleash the potential behind already available web 2.0 services to promote the personalised e-learning of languages in the contexts of higher & adult education, in particular, through mobile devices.

Acknowledgement

TELL-OP -Transforming European Learner Language into Learning Opportunities A KA200 Higher Education Strategic Partnership 2014‐1‐ES01‐KA203-004782 licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The SELFNET project will design and implement an autonomic network management framework to achieve self-organizing capabilities in managing network infrastructures by automatically detecting and mitigating a range of common network problems that are currently still being manually addressed by network operators, thereby significantly reducing operational costs and improving user experience. SELFNET explores a smart integration of state-of-the-art technologies in Software-Defined Networks (SDN), Network Function Virtualization (NFV), Self-Organizing Networks (SON), Cloud computing, Artificial intelligence, Quality of Experience (QoE) and Nextgeneration networking to provide a novel intelligent network management framework that is capable of assisting network operators in key management tasks: automated network monitoring by the automatic deployment of NFV applications to facilitate system-wide awareness of Health of Network metrics to have more direct and precise knowledge about the real status of the network; autonomic network maintenance by defining high-level tactical measures and enabling autonomic corrective and preventive actions against existing or potential network problems.

Acknowledgement

SELFNET is supported by the European Commission Horizon 2020 Programme under grant agreement number H2020-ICT-2014-2/671672

The COSMOS project, funded by a Leonardo Grant awarded by the BBVA Foundation, intends to develop novel and innovative solutions aimed at providing sophisticated protection mechanisms within the context of the Internet of Things (IoT). In this regard, its overall and main goal lies in the development of the so-called collaborative, seamless and adaptive sentinels. Such sentinels would seamlessly sense their environment, automatically identifying all those devices in the nearby to be potentially protected. Once the appropriate devices have been selected, the sentinels would adapt themselves in order to become experts in the protection of such specific devices against cyber-attacks. Last but not least, in case a new device to be protected comes into play and the assigned sentinel does not know how to protect it, the sentinel would ask for such protection knowledge to other collaborative sentinels in the community.

Furthermore, the IoT sentinels would not only focus on detecting intrusions, but rather cover the four phases of the cyberdefense, namley: prevention, detection, reaction and forensics.

Materialized in two different forms (dedicated and virtual), the sentinels in the context of COSMOS would operate in a Smart Home scenario, where a number of heterogeneous devices can be found (PCs, laptops, smartphones, electrical appliances, wearables, etc.).

This project runs from 15/09/2017 until 14/03/2019 (18 months).

Acknowledgement

Official Italian Engineer Certificate number A-4783 issued by the "Ordine degli Ingegneri di Verona e Provincia".

Official Spanish Certification issued by the "Instituto Cercantes".

Social networks constitute one of the major venues for massive information sharing and their popularity renders them one of the most impactful means to form or shift public opinion. Recently, a great raise of ill-motivated Social Bots, has been witnessed aiming to manipulate community sentiment. Social bots nowadays constitute coordinated armies controlled by malefactors who aim to manipulate and deceive media users. While social media enable the reproducibility of fake news or misleading information over the web, Social Bots take care of amplifying their popularity and catch social community’s eye though crafted viral trends.

Social bots are leveraged in order to manipulate the market by forming trends in favour (or against) products/services and brands’ reputation, while it is believed that they have played an important role in election periods by spoiling (or benefiting) the political profile of candidates. To this end, the endeavour of finding, validating and sharing the truth in the 5G era implies the detection of those coordinated nodes, understanding their motivation and revealing the main influencer actor.