Lutte Informatique d’Influence (L2I) / Fake News Detection, Generation, Prevention

In an era where information spreads at the speed of light and trust is constantly under siege, the proliferation of fake news poses a profound threat to our society’s fabric. 

The societal impact of our research direction on fake news cannot be overstated. By combating the erosion of trust and amplifying the voice of truth in an increasingly volatile digital landscape, we bolster the resilience of our critical networked infrastructures and uphold the fundamental principles of democracy, informed consent, and social cohesion. 

Together, let us rise to the challenge of defending truth in the digital age, ensuring that our interconnected world remains a bastion of integrity, reliability, and trustworthiness for generations to come.

Source: https://www.reddit.com/r/midjourney/comments/120vhdc/the_pope_drip/

Our research direction on fake news encompasses three pivotal pillars: generation, detection, and prevention.

  • Firstly, we delve into the intricacies of fake news generation, understanding the underlying mechanisms and technologies that enable its creation. By comprehensively analyzing the tactics employed by malicious actors, we aim to anticipate and counteract their efforts, thwarting the dissemination of misinformation before it takes root.
  • Secondly, our focus extends to the development of robust detection methodologies. Leveraging cutting-edge techniques from machine learning, natural language processing, and network analysis, we strive to identify and categorize fake news with unprecedented accuracy and efficiency. By empowering digital platforms and users with the tools to discern truth from falsehood, we fortify our collective defenses against manipulation and deception.
  • Lastly, we are dedicated to proactive prevention strategies aimed at inoculating our information ecosystem against the spread of fake news. Through interdisciplinary collaboration and engagement with stakeholders across academia, industry, and government, we seek to implement robust frameworks and protocols that promote transparency, accountability, and authenticity in online discourse.

News

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Publications

ANN Autonomous service management Certificates CoAP cyber physical system Cyber security deep learning event model firewall ind industrial systems Internet of Things (IoT) IoT L2I Machine learning metrics Microservices network traffic prediction Ransomware resilience SCADA Security Survivability Unattended nodes V2V Virtual reality Visual analytics Visualization Visualization application domains X.509

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2020

Moussaileb, Routa; Cuppens, Nora; Lanet, Jean Louis; Bouder, Hélène Le

Ransomware Network Traffic Analysis for Pre-encryption Alert Journal Article

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12056 LNCS, pp. 20–38, 2020, ISSN: 16113349.

Abstract | Links | BibTeX | Tags: Machine learning, Network traffic, Ransomware

0000

Moussaileb, Routa; Bouget, Benjamin; Cuppens, Nora

Ransomware ' s Early Mitigation Mechanisms Journal Article

In: 0000, ISBN: 9781450364485.

BibTeX | Tags: File Syste, file system tra-, Intrusion Detection System, Ransomware

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Alexander Loth
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