Unit INTELLIGENT AND SECURE NETWORKS
- Course
- Computer engineering and robotics
- Study-unit Code
- A003167
- Curriculum
- Robotics
- Teacher
- Mauro Femminella
- Teachers
-
- Mauro Femminella
- Hours
- 72 ore - Mauro Femminella
- CFU
- 9
- Course Regulation
- Coorte 2022
- Offered
- 2022/23
- Learning activities
- Affine/integrativa
- Area
- Attività formative affini o integrative
- Academic discipline
- ING-INF/03
- Type of study-unit
- Opzionale (Optional)
- Type of learning activities
- Attività formativa monodisciplinare
- Language of instruction
- Italian
- Contents
- Introduction to network management (9 hours)
Network monitoring and capture of traffic statistics (24 hours)
Network security (21 hours)
Machine learning techniques applied to network management (18 hours) - Reference texts
- J. Kurose, K. Ross, Computer networking, Pearson.
- Clarence Chio & David Freeman, Machine Learning & Security, O'Reilly
- William Stallings, CRYPTOGRAPHY AND NETWORK SECURITY, Pearson
- Additional material provided by the instructor - Educational objectives
- The course represents the first telecommunications networks course in the course of study, and examines the various aspects of network management, with particular emphasis on security issues and the use of artificial intelligence tools.
The main objective of the course is to provide students with the basics to deal with the analysis, management, and configuration of networks, through the study and experimentation of modern network monitoring techniques and tools for the detection of anomalies, malfunctions, etc. The main project constraints that the student will be required to consider will be functional and performance-oriented, related to the amount of data (stored off-line or captured in real time) to be processed.
The main knowledge acquired at the end of the course will be:
- Basic elements of network traffic capture
- Basic elements of protocols and solutions for network management: SNMP and related evolutions.
- Knowledge of service architectures and protocols used for network monitoring: NetFlow and sFlow, In-band Network Telemetry
- Basic concepts of network security: firewalls and intrusion detection systems;
- Applications of artificial intelligence tools for the detection of network problems due to dimensioning, configuration, and security issues.
The main skills, i.e. the ability to apply the knowledge acquired during the course, will be:
- Evaluate the most suitable technological solution of network management for the service scenario (SNMP, NetFlow, sFlow);
- Identify the design constraints that determine the dimensioning of a solution for monitoring and analyzing the traffic of a network and the choice of the most appropriate software;
- Mastering open-source software tools in a virtualized environment for traffic capture and analysis. - Prerequisites
- In order to be able to understand and apply most of techniques and concepts described in the Course, you must have successfully passed the Internet Basics exam. In addition, the Course requires the knowledge of basic security in networking and computer science. These topics are a mandatory prerequisite for students to follow this course with profit, and you acquire them in the Internet Basics course.
Basic knowledge of machine learning is recommended. - Teaching methods
- The course is organized as follows:
- lectures in the classroom on all the topics of the course. Students will be provided in advance with a copy of the slides used by the teacher in class through the reserved area of ¿¿the course (UNISTUDIUM portal);
- laboratory exercises at the Software Engineering Laboratory (Biennium building, ground floor). The laboratory consists of 16 workstations, which students can access in groups of 2 or 3 people. The maximum number of students who can access a lesson is 48. In case the number of students exceeds this value or in case the lessons are particularly complex with continuous assistance from the teacher, the lesson will be repeated. Each exercise has a duration of 2 or 3 hours. Each exercise consists in the guided realization of traffic capture and monitoring experiments, or in the analysis of traffic traces using machine learning tools. During the exercises the students use open-source software tools in virtualized form (Linux virtual machine). Students will have free access to the laboratory at the end of the lesson for further individual exercises. - Other information
- The lessons can be integrated with seminars. Lab experiences will be carried out at the Software Engineering Laboratory.
- Learning verification modality
- The exam includes an oral test to be taken at the end of the course.
The final exam consists of an oral exam lasting about 30-45 minutes, aimed at ascertaining the level of knowledge and understanding reached by the student on the theoretical and methodological contents of the course, as well as the presentation of a theoretical / practical project work assigned by the teacher during the course.
This evaluation method allows ascertaining both the knowledge and the ability to know and understand the topics of the course, and the ability to apply the skills acquired, to present them, and the ability to learn and develop solutions.
For information on support services for students with disabilities and / or SLD, visit the page http://www.unipg.it/disabilita-e-dsa. - Extended program
- Introduction to network management (9 hours)
- What is meant by network management
- SNMP and its evolutions
Network monitoring and capture of traffic statistics (24 hours)
- Traffic capture methodologies and protocol analyzers
- NetFilter and iptables
- Flow-based network monitoring architectures and protocols: NetFlow, sFlow, and In-band Network Telemetry
Network security (21 hours)
- Firewall: theory and practical realization
- IDS: theory and practical realization
Machine learning techniques applied to network management (18 hours)
- Artificial intelligence applied to network management
- Experiments with open source machine learning tools on public datasets or real-time captures