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Portal > Offres > Offre UPR8001-PHIOWE-001 - Offre de thèse sur la Détection autonome du trafic malicieux dans les réseaux véhiculaires (H/F)

PhD thesis offer in Autonomous detection of malicious traffic in vehicular networks

This offer is available in the following languages:
Français - Anglais

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General information

Reference : UPR8001-PHIOWE-001
Workplace : TOULOUSE
Date of publication : Monday, March 23, 2020
Type of Contract : FTC Scientist
Contract Period : 4 months
Expected date of employment : 1 May 2020
Proportion of work : Full time
Remuneration : 2135€ per month before taxes, depending on background.
Desired level of education : 5-year university degree
Experience required : Indifferent

Missions

The continuous arising of new attacks creates a continuous challenge to cope with events that put the network integrity at risk. Most network attack detection systems proposed so far employ a supervised strategy to accomplish the task, using either signature-based detection methods or supervised-learning techniques. However, both approaches present major limitations: the former fails to detect and characterize unknown attacks (letting the network unprotected for long periods), the latter requires training and labelled traffic, which is difficult and expensive to produce. Such limitations impose a serious bottleneck to the previously presented problem. We argue that an unsupervised approach to detect and characterize network attacks, without relying on signatures, statistical training, or labelled traffic, represents a significant step towards the autonomy of networks. It is proposed for the unsupervised detection to accomplish it by means of robust machine learning and data-mining techniques leveraging a set of traffic and network features provided by a wireless network monitoring functionality. These parameters can be combination of any signal level features of low physical and MAC wireless layers, and digital features of high level layers (network and upper). Once detected, the objective is to autonomously characterize detected attacks, and derive signatures or filtering rules for configuring security or switching/routing devices.

Activities

- Experimental validation expérimentale
- Thesis manuscrit writing
- Articles writing

Skills

- Communication networks
- Ontologies
- Programming in C, Python
- Cybersecurity

Work Context

End of thesis to be done at LAAS, in the SARA team

Constraints and risks

none

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