{"id":12182,"date":"2019-10-22T12:45:50","date_gmt":"2019-10-22T11:45:50","guid":{"rendered":"https:\/\/www.riskinsight-wavestone.com\/?p=12182"},"modified":"2019-12-31T09:10:23","modified_gmt":"2019-12-31T08:10:23","slug":"radar-startups-2019-33","status":"publish","type":"post","link":"https:\/\/www.riskinsight-wavestone.com\/en\/2019\/10\/radar-startups-2019-33\/","title":{"rendered":"L\u2019utilisation du Machine Learning par les startups fran\u00e7aises dans le domaine de la cybers\u00e9curit\u00e9"},"content":{"rendered":"

Cet article a pour but de pr\u00e9senter nos convictions sur l\u2019utilisation du Machine Learning par les startups fran\u00e7aises en cybers\u00e9curit\u00e9 du Radar Wavestone 2019<\/a>.<\/em><\/p>\n

L\u2019intelligence artificielle est un sujet \u00e0 la mode et la cybers\u00e9curit\u00e9 fait partie des cas d\u2019usages phare de d\u00e9veloppement. Est-ce aussi le cas pour les startups<\/em> fran\u00e7aises en cybers\u00e9curit\u00e9\u00a0? Qu\u2019en est-il de son utilisation ? Quelles sont les tendances du march\u00e9 concernant cette technologie\u00a0?<\/p>\n

 <\/p>\n

\u00ab\u00a0Intelligence Artificielle\u00a0\u00bb, \u00ab\u00a0Machine Learning\u00a0\u00bb, \u00ab\u00a0Deep Learning\u00a0\u00bb\u00a0: trois termes trop souvent confondus<\/h2>\n

Avant de rentrer dans le vif du sujet, commen\u00e7ons par clarifier le vocabulaire qui sera employ\u00e9 dans la suite de l\u2019article\u00a0:<\/p>\n

    \n
  • Intelligence Artificielle<\/strong><\/em>: ensemble des techniques mises en \u0153uvre pour que des machines simulent l\u2019intelligence ;<\/li>\n
  • Machine Learning<\/em><\/strong>: technique reposant sur des mod\u00e8les statistiques qui permettent \u00e0 l\u2019ordinateur \u00ab\u00a0d\u2019apprendre\u00a0\u00bb \u00e0 partir d\u2019un grand nombre de donn\u00e9es\u00a0;<\/li>\n
  • Deep Learning<\/em><\/strong>: m\u00e9thode de Machine Learning<\/em> bas\u00e9e sur un r\u00e9seau de neurones profonds. D\u2019autres m\u00e9thodes existent : Support Vector Machines, Random Forests, K-Nearest Neighbors<\/em>, \u2026<\/li>\n<\/ul>\n

    La confusion entre ces trois termes est fr\u00e9quente. Bien souvent l\u2019utilisation du terme \u00ab\u00a0Intelligence Artificielle\u00a0\u00bb en cybers\u00e9curit\u00e9 d\u00e9signe l\u2019utilisation du Machine Learning<\/em>, sous toutes ses formes.<\/p>\n

     <\/p>\n

    \"\"<\/figure>\n

     <\/p>\n

    La cybers\u00e9curit\u00e9, un terreau fertile pour les technologies de Machine Learning<\/h2>\n

    Sur les 134 startups<\/em><\/strong> recens\u00e9es dans notre radar 2019 des startups<\/em> fran\u00e7aises en cybers\u00e9curit\u00e9, 19% <\/strong>proposent des solutions bas\u00e9es sur du Machine Learning<\/em>. Interrog\u00e9es, 70%<\/strong> de ces startups<\/em> d\u00e9clarent que d\u00e9velopper ce type de technologie dans leurs solutions fait partie de leur strat\u00e9gie.<\/p>\n

    \"\"<\/p>\n

    De plus, l\u2019utilisation du Machine Learning<\/em> dans certains domaines de la cybers\u00e9curit\u00e9 devient quasi incontournable<\/strong> et la majorit\u00e9 des startups<\/em> de ces domaines envisagent de baser les futures \u00e9volutions de leur solution sur cette technologie.<\/p>\n

    Le Machine Learning<\/em> en cybers\u00e9curit\u00e9 est en pleine progression et son utilisation, d\u00e9j\u00e0 implant\u00e9 dans l\u2019\u00e9cosyst\u00e8me des startups<\/em> fran\u00e7aises, d\u00e9montre une forte volont\u00e9 d\u2019innovation du march\u00e9. On pressent que ce rythme d\u2019adoption va continuer \u00e0 s\u2019acc\u00e9l\u00e9rer dans les ann\u00e9es \u00e0 venir, la m\u00eame \u00ab\u00a0photo\u00a0\u00bb de l\u2019\u00e9tat des lieux dans un an devrait le prouver.<\/p>\n

     <\/p>\n

    Le Machine Learning utilis\u00e9 dans un but d\u2019am\u00e9lioration de performances<\/h2>\n

    Les startups<\/em> qui ont choisi d\u2019utiliser le Machine Learning<\/em> le font principalement afin de\u00a0:<\/p>\n

      \n
    • Obtenir des temps de r\u00e9ponses courts<\/strong>: r\u00e9duire le temps de r\u00e9ponse de la prise de d\u00e9cision en utilisation nominale. En effet, dans certains cas o\u00f9 le nombre de donn\u00e9es est particuli\u00e8rement important, il faudrait des mois \u00e0 un algorithme n\u2019utilisant pas de Machine Learning<\/em> pour fournir un r\u00e9sultat\u00a0;<\/li>\n
    • Am\u00e9liorer la fiabilit\u00e9 des d\u00e9tections<\/strong>: r\u00e9duire le nombre d\u2019erreurs, c\u2019est-\u00e0-dire diminuer le taux de faux positif et faux n\u00e9gatifs. Les solutions anti-phishing<\/em> sont une bonne illustration car celles reposant sur du Machine Learning<\/em> filtrent avec moins d\u2019erreur qu\u2019une solution dite \u00ab\u00a0classique\u00a0\u00bb.<\/li>\n<\/ul>\n

       <\/p>\n

      Une adoption h\u00e9t\u00e9rog\u00e8ne selon le th\u00e8me du radar\u2026<\/h2>\n

      L\u2019adoption du Machine Learning<\/em> varie de mani\u00e8re importante d\u2019une th\u00e9matique du radar \u00e0 l\u2019autre. Les th\u00e9matiques o\u00f9 l\u2019utilisation du Machine Learning<\/em> est la plus r\u00e9pandue\u00a0sont : \u00ab\u00a0Application Security<\/em>\u00a0\u00bb, \u00ab\u00a0Endpoint<\/em>\u00a0\u00bb, \u00ab\u00a0Industrial Security<\/em>\u00a0\u00bb et \u00ab\u00a0Web Security<\/em>\u00a0\u00bb. On note \u00e9galement quelques cas d\u2019usages particuliers dans d\u2019autres th\u00e8mes du radar tel DPO Consulting utilisant du Machine Learning<\/em> pour aider \u00e0 la prise de d\u00e9cisions dans le cas d\u2019une \u00e9valuation des risques.<\/p>\n

      \"\"<\/figure>\n

      \u2026expliqu\u00e9 par la nature m\u00eame du Machine Learning.<\/h2>\n

      Le Machine Learning n\u00e9cessite un certain nombre de pr\u00e9requis et de conditions pour fonctionner efficacement. Toute la performance des mod\u00e8les de Machine Learning repose sur la phase d\u2019entrainement<\/strong> o\u00f9 le mod\u00e8le \u00ab apprend \u00bb gr\u00e2ce aux donn\u00e9es qu\u2019on lui fournit. Ces donn\u00e9es, que nous allons illustrer par le cas d\u2019une solution anti-phishing pour boite mail, doivent \u00eatre :<\/p>\n

        \n
      • Pertinentes<\/strong>: c\u2019est-\u00e0-dire porteuses d\u2019informations utiles. Dans notre exemple de solution anti-phishing<\/em>, une information utile est par exemple la pr\u00e9sence de certains mots souvent utilis\u00e9s dans des mails de phishing<\/em>\u00a0; une image ou la taille du fichier mail sont moins utiles ;<\/li>\n
      • En nombre suffisant<\/strong>: ce nombre varie selon le cas d\u2019usage et le niveau de pr\u00e9cision souhait\u00e9. Dans notre exemple de solution anti-phishing<\/em>, il faudrait probablement entrainer l\u2019algorithme avec quelques dizaines de milliers de mails ;<\/li>\n
      • Vari\u00e9es<\/strong>: si possible de sources diff\u00e9rentes pour plus de r\u00e9silience de l\u2019algorithme. Dans notre exemple de solution anti-phishing<\/em>, il serait bon que la base de donn\u00e9es d\u2019entrainement contienne des mails issus de diff\u00e9rentes campagnes de phishing<\/em>, re\u00e7us par diff\u00e9rents entreprises\/particuliers, des mails cibl\u00e9s ou non\u2026, et qu\u2019elle puisse traiter \u00e0 la fois le contenu du mail mais \u00e9galement les headers<\/em>, etc. ;<\/li>\n
      • Repr\u00e9sentatives\u00a0<\/strong>: c\u2019est-\u00e0-dire ne pas<\/strong> introduire de biais <\/strong>et \u00eatre \u00e0 jour<\/strong>. Dans notre exemple de solution anti-phishing<\/em>, il convient par exemple de r\u00e9-entrainer r\u00e9guli\u00e8rement le mod\u00e8le afin de prendre en compte les derni\u00e8res tendances en mati\u00e8re de phishing<\/em>.<\/li>\n<\/ul>\n

        \"\"<\/p>\n

        Sch\u00e9ma de fonctionnement d\u2019une solution bas\u00e9e sur du Machine Learning<\/em><\/p>\n

        Dans les domaines o\u00f9 le Machine Learning est le plus utilis\u00e9, il s\u2019av\u00e8re que ces conditions sont plus facilement r\u00e9unies. En effet, les donn\u00e9es n\u00e9cessaires \u00e0 l\u2019entrainement sont souvent d\u00e9j\u00e0 disponibles dans les \u00e9quipements en place (log applicatifs, log syst\u00e8me, log r\u00e9seau, alerte anti-virus, \u2026), voire d\u00e9j\u00e0 consolid\u00e9es dans des \u00e9quipements de s\u00e9curit\u00e9 centraux (SIEM, Data Lake\u2026).<\/p>\n

         <\/p>\n

        \u00ab\u00a0Intelligence Artificielle\u00a0\u00bb\u00a0: Gare \u00e0 l\u2019effet de mode\u00a0!<\/h2>\n

        Si le Machine Learning<\/em> offre de nouvelles possibilit\u00e9s permettant de grandement am\u00e9liorer les capacit\u00e9s cybers\u00e9curit\u00e9 des entreprises, cette technologie n\u2019est pas en soi une solution miracle. Il est important de bien comprendre ces algorithmes et de garder certains points d\u2019attention en t\u00eate avant de se lancer dans l\u2019acquisition d\u2019une telle solution.<\/p>\n

        Tout d\u2019abord, comme la phase d\u2019entrainement est cl\u00e9 pour la performance du Machine Learning<\/em>, il faut s\u2019interroger sur sa capacit\u00e9 \u00e0 fournir \u00e0 la solution les donn\u00e9es n\u00e9cessaires et suffisantes pour l\u2019apprentissage. Le principal frein remont\u00e9 par les startups<\/em> proposant des solutions entrain\u00e9es avec des donn\u00e9es clients est d\u2019ailleurs la difficult\u00e9 d\u2019obtenir des donn\u00e9es en qualit\u00e9 et quantit\u00e9 suffisante <\/strong>pour faire tourner leur solution.<\/p>\n

        Il est \u00e9galement important de r\u00e9ussir \u00e0 lire au-del\u00e0 du discours commercial pour comprendre l\u2019apport r\u00e9el du Machine Learning<\/em> \u00e0 la solution, au risque de payer plus cher un outil qui ne serait pas forc\u00e9ment justifi\u00e9. Et surtout, il faut \u00eatre conscient que le Machine Learning<\/em> ne signifie pas ne plus avoir de risques. Ces solutions, comme toute solution de s\u00e9curit\u00e9, r\u00e9pondent \u00e0 un cas d\u2019usage pr\u00e9cis et viennent compl\u00e9ter un ensemble de mesures de s\u00e9curit\u00e9.<\/p>\n

        Cette mise en garde nous semble n\u00e9cessaire m\u00eame si nous avons constat\u00e9 une utilisation pertinente et justifi\u00e9e de ces technologies par les startups fran\u00e7aises en cybers\u00e9curit\u00e9 dans le cadre de notre analyse.<\/p>\n","protected":false},"excerpt":{"rendered":"

        Cet article a pour but de pr\u00e9senter nos convictions sur l\u2019utilisation du Machine Learning par les startups fran\u00e7aises en cybers\u00e9curit\u00e9 du Radar Wavestone 2019. L\u2019intelligence artificielle est un sujet \u00e0 la mode et la cybers\u00e9curit\u00e9 fait partie des cas d\u2019usages…<\/p>\n","protected":false},"author":1353,"featured_media":11736,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"page-templates\/tmpl-one.php","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[3223,36],"tags":[2733,3076,2505],"coauthors":[3194,3195,3196],"class_list":["post-12182","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-cloud-next-gen-it-security","category-cybersecurity-digital-trust","tag-machine-learning","tag-radar","tag-start-up"],"acf":[],"yoast_head":"\nL\u2019utilisation du Machine Learning par les startups fran\u00e7aises dans le domaine de la cybers\u00e9curit\u00e9 - RiskInsight<\/title>\n<meta name=\"description\" content=\"Cet article a pour but de pr\u00e9senter nos convictions sur l\u2019utilisation du Machine Learning par les startups fran\u00e7aises en cybers\u00e9curit\u00e9.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.riskinsight-wavestone.com\/2019\/10\/radar-startups-2019-33\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"L\u2019utilisation du Machine Learning par les startups fran\u00e7aises dans le domaine de la cybers\u00e9curit\u00e9 - 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