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Degrees incorporating this pedagocial element :

### Description

#### Security of Network and Applications (18h + 8h labs)

The objective of this class is to introduce security principles, on the theoretical, organizational and technical aspects. The points which are more specifically developed are: detection errors, firewall technics, network architecture, cryptology and VPN, anti-virus strategy. Are also discussed how to implement a security strategy, and some elements for the definition of a security policy. Some elements about safe networks, or networks for safety or critical applications, are also studied.

Lesson | Topic | |
---|---|---|

1 | Introduction to networks, error detection and correction | |

Bases of network, theoretical elements of error correction and detection, application in the case of parity, CRC, checksum. | ||

DEPENDABILITY - SECURITY | ||

2 | Dependability - security - risk analysis | |

Concepts, application to networks and information systems, simple application examples. | ||

TECHNOLOGY FOR SECURITY | ||

3 | Attack strategies | |

The phases of an attack, types of attacks. | ||

4 | Technologies for security: | |

Network infrastructure, filtering, security protocols, VPN. | ||

METHODOLOGIES | ||

5 | Cryptography | |

Theories on symmetric and asymmetric cryptography, DES, RSA, application to encryption, hash calculation, signature, certificates. | ||

6 | Virology | |

Bases of virology. application to encryption, hash calculation, signature, certificates. | ||

LABS on NETWORK AND SECURITY | ||

Lab 1 | Firewalls and wireless networks | |

Lab 2 | Communication security and encryption |

#### Field buses and Zigbee (10.5 h + 15h labs)

#### Distributed Algorithms and Network Systems (13.5 h + 6h labs)

Objectives Distributed algorithms aim at obtaining a global goal by exploiting a large number of simple devices (``agents''), and their local interactions. These algorithms can be for the purposes of estimation in a wireless sensor network, or control e.g. of a self-organized robotic fleet. This introductory class will first review the necessary tools from graph theory and Markov chains, and then present consensus: a prototypical example of distributed algorithm, as well as a building block for more complex algorithms. Theory will be accompanied by implementation on a real-world sensor network: FIT/IoT LAB.Class schedule

- Introduction: network systems
- Graphs: fundamentals of algebraic graph theory
- Markov chains: convergence to invariant measure, Perron-Frobenius theorem
- Consensus (time-invariant graph)
- Consensus (gossip: randomly varying graph)
- Consensus-based algorithms: using consensus as a building block of other algorithms (e.g., localisation from relative measurements, least-squares regression, gradient descent minimization, distributed Kalman filter, counting nodes in an anonymous network)
- Labs (3): implementation of distributed algorithms on real sensor network, remotely using FIT/IoT LAB. Programming language: C.