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ORGANIZER;CN=Martina Vigorito:mailto:martina.vigorito@epita.fr
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Martina Vi
 gorito:mailto:martina.vigorito@epita.fr
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Current:ma
 ilto:current@ml.lre.epita.fr
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=secu-sys@m
 l.lre.epita.fr:mailto:secu-sys@ml.lre.epita.fr
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Hela Marou
 ane:mailto:hela.marouane@esme.fr
ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Nidà Medd
 ouri:mailto:nida.meddouri@epita.fr
ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Nicolas Bo
 utry:mailto:nicolas.boutry@epita.fr
ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Loïc Rouq
 uette:mailto:loic.rouquette@epita.fr
DESCRIPTION:Bonjour à toutes et à tous\,\n\nNous reprenons notre cycle de
  séminaires de l'équipe Sécurité/Systèmes\, nous avons le plaisir de 
 vous inviter au premier séminaire de cette nouvelle année\, qui aura lie
 u :\n\n📅 Quand ? Mardi 16 septembre 2025\, de 10h à 11h30\n\n\n\n📍 
 Où ? LRE\, Open Space / Réunion Microsoft Teams\n\n\n\n🎤 Titre du sé
 minaire: Anomaly Detection in Vehicular Networks: A Generative AI-Based Ap
 proach\n\nNous aurons le plaisir d’accueillir Hela Marouane\, qui prése
 ntera son travail de recherche dont les détails suivent.\nRésumé: Vehic
 ular Ad Hoc Networks (VANETs) play a crucial role in road safety and the d
 eployment of Intelligent Transportation Systems (ITS). However\, their hig
 hly dynamic nature and the continuous growth of data exchanges make them p
 articularly vulnerable to intrusions and abnormal behaviors\, which can co
 mpromise the reliability of communications. Traditional anomaly detection 
 approaches\, often relying on neural network algorithms or machine learnin
 g techniques\, show limitations when faced with the diversity and rapid ev
 olution of attacks. In this context\, generative artificial intelligence o
 ffers a new perspective. By learning the normal distribution of network be
 haviors and generating realistic representations of traffic flows or patte
 rns\, generative models\, such as Variational Autoencoders (VAEs) and Gene
 rative Adversarial Networks (GANs)\, can identify subtle deviations that m
 ay indicate potential attacks or anomalies.\nTo enhance the trustworthines
 s of these systems\, the integration of explainability mechanisms (XAI) en
 ables the interpretation of model decisions and provides insights into why
  certain behaviors are classified as anomalous\, thereby facilitating vali
 dation by cybersecurity experts and supporting real-time decision-making.\
 nThe objective of this research is to explore the integration of generativ
 e AI into proactive anomaly detection mechanisms within VANETs\, with the 
 aim of improving the resilience\, reliability\, and security of vehicular 
 communications.\n\nBio: Hela Marouane is currently an Associate Professor 
 at the engineering school ESME-Sudria. She obtained a Master’s degree in
  Informatics and Multimedia from the University of Sfax in 2010\, and a Ph
 D in Information Technology from the University of Le Havre and the Univer
 sity of Sfax in 2015. Before joining ESME\, she worked as an Associate Pro
 fessor at the Institute of Informatics and Multimedia.\n\nVous pouvez part
 iciper soit en ligne\, soit en présentiel à l'Open Space LRE. Pour les p
 articipants en présentiel\, merci de vous inscrire via https://forms.offi
 ce.com/e/5bLjTkQtyf avant le 14/09/2025\, pour des raisons d'organisation.
 \n\n\n\nÀ bientôt et merci!\n\nMartina\n\n\n____________________________
 ____________________________________________________\nMicrosoft Teams Serv
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 _________________________________________\n
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SUMMARY:Séminaire Sécurité/Systèmes - Mardi 16/09 à 10h00\, Hela Marou
 ane\, "Anomaly Detection in Vehicular Networks: A Generative AI-Based Appr
 oach"    
DTSTART:20250916T080000Z
DTEND:20250916T093000Z
CLASS:PUBLIC
PRIORITY:5
DTSTAMP:20250830T091925Z
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