Dottorato di Ricerca in 
Information Technology and Electrical Engineering (ITEE)

09-11-16-17-18/01/2024

The course covers topics regarding the design, realization, and evaluation of Network Intrusion

Detection Systems (NIDSs) used for protecting networks against attacks. Specifically, the course details how Machine Learning (ML) and Deep Learning (DL) approaches can be properly exploited to develop these detection systems. The course briefly provides the basics on the most common attacks against networks and on ML and DL models used to counteract them. The students will learn methodological guidelines to determine which are the most suitable models and input data based on, for instance, the problem to face (e.g., anomaly detection, attack classification), the information available (e.g., supervised vs. unsupervised), and the attacks to deal with (e.g., DDoS, BotNet). The course follows a “hands-on” approach that will guide the students toward the actual design, implementation, and performance evaluation of NIDSs, exploiting the tools provided by state-of-the-art (Python) frameworks (e.g., Scikit-learn, Keras, PyTorch). The course will make extensive use of actual case studies based on data from real network attacks (e.g., attacks against IoT or Android devices) to provide the operational scenarios for the detection systems. There will be a final assessment concerning the realization of a basic prototype and a report describing its design and evaluation.

  Hands-on Network

dr.  Antonio Montieri - DIETI-Unina

07-15/12/2023 – 12-19-TBD-TBD/01/2024

In a globally competitive environment where the value of research, and indeed one’s professional/academic success, is measured in terms of how impactfully research findings are communicated/disseminated (publication) and how many times they are cited (citation impact), researchers and academics - especially in STEM domains - are increasingly held to the motto “publish or perish!” Yet too many remain myopically focused on technical skills, failing to prioritise the strategic and communication skills needed to effectively showcase technical merit… until faced with imminent rejection of their submitted papers! Participants will learn to avoid such myopia with the value-driven orientation necessary to thrive amidst competition. Along with capacity-building attributes and core competencies including creative thinking and cross-disciplinary team work, participants will work to develop effective professional communication and writing skills, with a view to getting published. Designed to be interactive, this course emphasises active participation/discussion, and as with comparable offerings, has a normal course load incl: readings, in-class exercises/activities and at-home work (individual/team). Overall assessments are based on various learning components including collaborative research project, manuscript preparation and submission as applicable.

 Strategic Orientation STEM Research Writing

Sig.ra Adriana D'Auria – DIETI- Unina

The course introduces the new vision of Natural Language Processing (NLP) considering the recent revolutions in the field. The advent of Large Language Models like ChatGPT has brought huge changes in the way we all see AI but it has also increased expectations and “noise” in people’s minds. At the same time, we observed different reactions from experts in the field, who were forced to specify the pros and cons of this revolution, and from non-experts who suddenly became proficient in their own view. In this perspective, we decided to offer to the doctoral school, and anyone else interested, a series of lectures on the foundations of modern NLP and aspects related to AI.

There will be a final assessment.

 Artificial Intelligence and Natural Language Processing (syllabus 2023)

prof. Francesco Cutugno  - DIETI - Unina

DIETI

ITEE - ICTH - CQB PhD programs

dr. Francesco Tortorelli

Dr. Domenico Amalfitano, prof. Anna Rita Fasolino

DIETI

ITEE - ICTH PhD programs

 How to boost your PhD (syllabus 2023)

 DIETI

ITEE - ICTH - CQB PhD programs

This course will discuss the applications of Electronic Scanning Antennas in the Radar field and will provide the students with a brief introduction and a review of the main techniques/algorithms that can be applied for target filtering, beamforming, and detection. After a theoretical part, the attention will be shifted to some specific examples with emphasis on solutions proposed in practice. At the end of each lecture, students are encouraged to start a discussion on possible alternative techniques or solutions.

 Electronic Scan Antennas (syllabus 2023)

Designing and implementing a Deep Learning system is not an easy task.
The process requires several choices regarding model design, data engineering, parameter modification and testing. This process is easily subject to errors that are not easily identifiable and, in some cases, may lead to overestimating the performance of the proposed solution. This course aims to provide a general pipeline for designing and validating a machine learning system, avoiding the most common errors that can easily be made. To this end, it will be shown how to implement the experimental evaluation of simple classification tasks, highlighting their peculiarities and points to pay attention to. The practical part of the course is based on PyTorch, one of the best-known packages for neural networks. An introductory view of it is given. There will be a final assessment.

 Using Deep Learning properly (syllabus 2023)

DIETI

ITEE PhD

This course will present advances on Data Analysis with emphasis on its adoption in the Internet of Things environments, where vast amounts of data are generated from multiple and heterogeneous data sources. The course will provide the students with the concepts and advanced techniques for analyzing field data (such as computer logs, event trace, system level metrics, IoT data) to understand the behavior of an IoT system from a dependability point of view. The course puts the basis for the development of analysis frameworks the students can leverage in their own research field. The final assessment will require students to prepare a good quality presentation about the potential application of the provided Data Analysis concepts and techniques to their research activities. Student’s presentations will take place in the last lesson. Details about the presentation format and schedule will be given during the course.

 IoT-DataAnalysis (syllabus 2023)

DIETI

ITEE PhD

Dr. Luigi De Simone, DIETI

DIETI

ITEE PhD

Feb 2024

Prof. Roberto Pietrantuono, DIETI

DIETI

ITEE PhD

Proff. Antonio De Maio, Augusto Aubry, Dr. Vincenzo Carotenuto, DIETI

DIETI

ITEE PhD

Proff. Antonio De Maio, Augusto Aubry, Dr. Vincenzo Carotenuto, DIETI

DIETI

ITEE PhD

DIETI

ITEE PhD

DIETI

ITEE PhD

Proff. A. Corazza, F. Isgrò, R. Prevete, C. Sansone, G. Pezzulo

DIETI

ITEE PhD

Winter 2023 

(38th cycle, second year)

DIETI

ITEE PhD

 Spring 2024

(38th cycle, second year)

DIETI

ITEE PhD 

M. Cesarelli, P. Bifulco