UE Network applications

Informations aux utilisateurs

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Diplômes intégrant cet élément pédagogique :

Descriptif

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 
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  
Dependability - security - risk analysis 
 Concepts, application to networks and information systems, simple application examples. 
 TECHNOLOGY FOR SECURITY  
Attack strategies 
 The phases of an attack, types of attacks. 
Technologies for security:
 Network infrastructure, filtering, security protocols, VPN. 
 METHODOLOGIES  
Cryptography 
 Theories on symmetric and asymmetric cryptography, DES, RSA, application to encryption, hash calculation, signature, certificates.
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.

 

 

Informations complémentaires

Lieu(x) : Grenoble
Langue(s) : Anglais