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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 

  1. Introduction: network systems 
  2. Graphs: fundamentals of algebraic graph theory
  3. Markov chains: convergence to invariant measure, Perron-Frobenius theorem 
  4. Consensus (time-invariant graph) 
  5. Consensus (gossip: randomly varying graph) 
  6. 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) 
  7. Labs (3): implementation of distributed algorithms on real sensor network, remotely using FIT/IoT LAB. Programming language: C.