OVERVIEW

Enterprise strategic choices are heavily influenced by “changes” modifying the operating context. One of the most important is the “digitalization”: currently every business is a digital one. Some digital technologies, listed in the aim, will have a special impact on future industrial strategies, allowing the development of smart cities, manufacturing, factory and agriculture. The knowledge, use and application of these technologies will be essential to make decisions in strategic environments.

AIMS AND CONTENT

LEARNING OUTCOMES

The lectures are aimed at providing theoretical and practical knowledge about advanced Information and Communication Technologies which will influence strategic choices in the next future allowing the development of new paradigms and services such as smart cities, manufacturing, factory and agriculture. The lectures will provide a basic know-how about networking technologies such as IP and TCP/UDP architectures and will develop this information to explain concepts such as Cloud Computing and Internet of Things; 5G and Satellite Technology, Automated and Connected Mobility; Big Data Analytics, Artificial Intelligence and Machine Learning, and Cybersecurity.

AIMS AND LEARNING OUTCOMES

The lectures are aimed at providing theoretical and practical knowledge about advanced Information and Communication Technologies which will influence strategic choices in the next future allowing the development of new paradigms and services such as smart city, home, factory, and agriculture.

At the end of the Course the students will know main ICT technologies and to make decisions about their application to different operational contexts.

The lectures will provide a basic know-how about networking technologies, such as IP and TCP/UDP architectures, and will develop this information to explain concepts such as Internet of Things (IoT), Industrial Control Systems (ICS), Cloud and Edge Computing, 5G, Vehicular and Aerial/Satellites communications, and Cybersecurity.

TEACHING METHODS

Lectures integrated by tutorials.

SYLLABUS/CONTENT

• Basic of Telecommunications
  • General structure of telecommunication systems
  • Telecommunication network topologies
  • Telecommunication network taxonomy
  • Protocol stack
  • Interconnection nodes
  • Circuit and packet switching
  • Multiplexing and Demultiplexing
  • Principles of IP and TCP protocols
• Cybersecurity
  • Basic concepts
  • Introduction of Machine Learning for Cybersecurity
  • Intrusion Prevention Systems
  • Intrusion Detection Systems
• Advanced ICT technologies and application scenarios
  • Internet of Thing (IoT) – Smart Home, Smart Cities, Smart Factory, Smart Agriculture
  • Industrial Control Systems (ICS) – Smart Grid
  • Vehicular communications – Smart Transportation
  • Aerial/Satellite communications – Smart Logistics

RECOMMENDED READING/BIBLIOGRAPHY

- Notes on specific topics issued by the lecturer.

- Extracts of international regulatory and scientific documentation provided by the lecturer.

TEACHERS AND EXAM BOARD

Exam Board

FABIO PATRONE (President)

ENRICO CAMBIASO

MAURIZIO MONGELLI

MARIO MARCHESE (President Substitute)

LESSONS

LESSONS START

https://corsi.unige.it/10728/p/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The exam is structured into the following 3 parts:

Assignment for the networking part of the course

The candidate presents a 5-page (including figures) dissertation concerning the following topic.  

Choose a specific technology related to the ones investigated in the course, describe it briefly and state:

-              Perspective – Vision and direction

-              Position 

-              Strategic Planning

The last two issues should be imagined within a market derived from the personal experience or from its own future perspective.

Build a power point presentation and, in 15 minutes, present your report. 15 minutes are for questions and discussion.

The evaluation will be based on: Relevance to the themes presented in the course, Originality, Execution modalities, Feasibility, Clear presentation/exposition.

Assignment for the machine learning part of the course

The candidate presents a 3-page (including figures) dissertation concerning the following topic.  

Apply the matlab code of Bayes Decision Theory to a dataset and discuss the results according to the discussion presented during the lessons. The final vote is as follows.

>From 18 to 24 if the dataset is the same DNS tunneling database used for the lessons.

>From 24 to 28 if the candidate uses another open source dataset, e.g., taken from the UCI repository (https://archive.ics.uci.edu/ml/index.php). The candidate can work with different couples of features available from the dataset (as done during the lessons) and compare the results.

>From 28 to 30 laudae if the discussion includes at least ONE of the issues: how does the model generalize to new data? Which is the impact of the features to the classification performance? What is the feature extraction process? Is the Gaussian probability distribution assumption about data applicable? Apply 3D visualization of different features. Compare quadratic vs linear bayes. Apply the neural network Matlab code and discuss a comparison with Bayes Decision Theory.

Assignment for the cyber-security part of the course

The candidate presents a three pages (figures included) dissertation concerning the following topic.

The candidate selects a specific category of cyber-attacks (may be one of the presented ones, or other ones) for investigation.

Evaluation of the work:

 - Description of the attack: [18 to 26)

   if the functioning of the attack, the targeted components and protocols, impact and countermeasures are presented

 - Proof-of-concept of the exploitation: [26 to 30)

   if a proof-of-concept of the exploitation is provided (e.g. information on exploitation through Metasploit, link to open-source tools available on public repository services like GitHub, etc.)

 - Personal considerations: [30,30L]

   if an original dissertation on exploitation is provided (e.g. by extending state-of-the-art attacks, or by proposing the use of different approaches)

The final document, to be produced in English language, will need to be composed of three separated sections, according to the evaluation information reported above.

ASSESSMENT METHODS

Networking part of the course

The evaluation will be based on: Relevance to the themes presented in the course, Originality, Execution modalities, Feasibility, Clear presentation/exposition.

Machine learning part of the course

>From 18 to 24 if the dataset is the same DNS tunneling database used for the lessons.

>From 24 to 28 if the candidate uses another open source dataset, e.g., taken from the UCI repository (https://archive.ics.uci.edu/ml/index.php). The candidate can work with different couples of features available from the dataset (as done during the lessons) and compare the results.

>From 28 to 30 laudae if the discussion includes at least ONE of the issues: how does the model generalize to new data? Which is the impact of the features to the classification performance? What is the feature extraction process? Is the Gaussian probability distribution assumption about data applicable? Apply 3D visualization of different features. Compare quadratic vs linear bayes. Apply the neural network Matlab code and discuss a comparison with Bayes Decision Theory.

Cyber-security part of the course

 - Description of the attack: [18 to 26)

   if the functioning of the attack, the targeted components and protocols, impact and countermeasures are presented

 - Proof-of-concept of the exploitation: [26 to 30)

   if a proof-of-concept of the exploitation is provided (e.g. information on exploitation through Metasploit, link to open-source tools available on public repository services like GitHub, etc.)

 - Personal considerations: [30,30L]

   if an original dissertation on exploitation is provided (e.g. by extending state-of-the-art attacks, or by proposing the use of different approaches)

Exam schedule