12.4.8 : Cybersécurité

  • Monday, (5:00 PM - 5:50 PM CET) Cybersecurity in the Age of AI: Navigating Threats and Innovations for VCs [SE63316]
    • Sean Xiang : Co-Founder and CEO, Bloombase
    • Ash Bhalgat : Senior Director of Cybersecurity Market Development, NVIDIA
    • Denis Mandich : Co-Founder and CTO, Qrypt
    • Ben Colman : Co-Founder and CEO, Reality Defender
    • no record
  • Monday, (5:30 PM - 5:55 PM CET) Accelerating NetworkX: The Future of Easy Graph Analytics [S61674]
    • Mridul Seth : Core Developer, NetworkX
    • Rick Ratzel : Senior Software Engineer, NVIDIA
    • Slides
    • NetworkX : open source project
    • About Graphs
    • Connectivity, shrtest path, route planning
    • Everything can be a graph
    • Almost 20 year old library
    • Pure python dictionnary of dictionnary does not scale
    • nx.betweeness_centrality() on 3.7 M Nodes, 16.5 M Edges, k=500, => 80 min on Intel Xeon Gold 6128 CPU, 45 GB RAM
    • Backend : GOU nx-cugraph, CPU + OpenMP graphblas-algorithms, nx-parallel in pure python (in progress), scipy.sparse (not started but a good idea)
    • Rapids.cugraph : no code change for GPU
    • NETWORK_BACKEND_PRIORITY=cugraph ipython bc_demo.ipy
      • From 7min44 to 20s
    • nx-cugraph supports a lot a algorithms
    • Better performancew with cache enable
    • https://github.com/networkx/networkx
    • https://github.com/rapidsai/cugraph
    • cugraph scales to 1 B Edge, but nx-cugraph uses only one GPU for Now
    • No link to pytock geometric, because networkx is an old school graph Analytics library an not a Maniche Learning graph library as PyTorch.geometric
  • Tuesday, (4:00 PM - 4:50 PM CET) Build Secured AI Cloud Infrastructure at Scale [S62794]
    • no record
  • Tuesday, (5:00 PM - 5:50 PM CET) live Rethinking Cybersecurity in the Age of Generative AI: Emerging Generative AI Applications for Cybersecurity [S62696]
    • 5B Internet user, several ZB generated
    • Using GenAI to defend against GenAI
    • Using GenAI to predict when GenAI is used for mallicious purposes
    • You cannot have an hallucination which takes down a compagny
    • Use of AI agent can reduce mean time to responce from 100 minutes to tens of seconds in some cases
    • Even if 99% are false positive
    • Generate documentation
    • Takes 6 months to train a classifier, now it tool 2 or 3 days with Generative AI and Reenforcement AI
  • Tuesday, (6:00 PM - 6:50 PM CET) How to Apply Generative AI to Improve Cybersecurity [S62173]
    • no record
  • Tuesday, (7:00 PM - 7:50 PM CET) Multi-Modal Integration of Deep Graph and Metric Learning for Optimal Malicious Behavior Detection [S61863]
    • Abdul Rahman : Associate VP in the Artificial Intelligence (AI) Center of Excellence (CoE) in Advisory, Deloitte
    • Adversarial model
    • intention and attribution
    • Deep graph and metric learning : increase the visibility of mallicious Behavior
    • Single mode AI is a single tool
    • Rule based tool => defined by human and need human to kept up to date
    • PyG : Pytorch Graph Library
    • Multi modal Behavior detection has very good result (qui ne veut rien dire)
    • They also use Morpheus : Execution time per job goes from minutes to seconds (graph lo based method and learning method)
    • Defining a deviation impiles a norm definition
    • In 10000000 events there are 50 day threats, so traning data are unbalanced
    • Representation of data can be done by a graph embeding
  • Tuesday, (10:00 PM - 10:50 PM CET) GPU Power Play: Role of AI Democratization in Cyber Defense [S62820]
    • no record
  • Wednesday, (7:00 PM - 7:50 PM CET) The Intersection of AI and Security: What Cybersecurity Leaders Need to Know [S62433]
    • David Reber : Chief Security Officer, NVIDIA
    • Common strategies to help keep organisation safe
    • More usage and more users => to protect
    • Cybersecurity is a data problem
    • 6x use of generative AI in Cybersecurity from 2022 to 2023
    • Chat GPT : IPhone moment for AI
    • Attacker : Right Once => Defender : Right Every Time
    • Generative AI : Accelerate both Defender and Attacker
    • Attack a digital twin of your network to see what will hapended
    • What is vulnerable on this peace of worfware ?
    • Is my network vulnerable to this new vulnerability ?
    • Use AI on built fondamentals, not to replace everything
    • How do I version PB of data ? (needed to train models)
    • Still up in discussion
    • Need standards
    • How to respond to a malicious dataset ?
    • Start with some AI use cases
    • Granted access for only user with right permisions is not guaratee yet
    • The only way to do that for now is to use several models trained on several part with diffrent critical data
    • Learn the gaps before the date if too late
    • The scale is huge (amount of data to be protected, to be learned, and the scale of attacks)