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Predicting future threats

Predicting attacks can help to prevent them or at least reduce their impact. Nowadays, existing attack prediction methods make accurate predictions only hours in advance or cannot predict geo-politically motivated attacks. ThreatPredict aims to predict different attack types days in advance. It develops machine-learning algorithms that capture spatio-temporal dynamics of cyber-attacks and global social, geo-political and technical events. Various sources of information are collected, enriched and correlated such as honeypot data, darknet, GDELT, Twitter, and vulnerability databases. In addition to warning about attacks, this project will improve our understanding of the effect of global events on cyber-security.

Predicting future threats

Predicting attacks can help to prevent them or at least reduce their impact. Nowadays, existing attack prediction methods make accurate predictions only hours in advance or cannot predict geo-politically motivated attacks. ThreatPredict aims to predict different attack types days in advance. It develops machine-learning algorithms that capture spatio-temporal dynamics of cyber-attacks and global social, geo-political and technical events. Various sources of information are collected, enriched and correlated such as honeypot data, darknet, GDELT, Twitter, and vulnerability databases. In addition to warning about attacks, this project will improve our understanding of the effect of global events on cyber-security.

International Project

This project involved three countries with three academic partners to deliver high-quality research: Inria in France, International University of Rabat in Morocco, and Carnegie Mellon University in USA.

Partners

Funding

News

ThreatPredict on TV

France 3 Lorraine brodcasted a report on the project with the aim to explain how we can help in rpedicting future threats on Internet https://youtu.be/CGfaVKYgBQk

Paper acceptation at ISI 2018 conference

We are happy to announce our papers acceptation at ISI 2018 conference, the IEEE Intelligence and Security Informatics (ISI) 2018. The name of the papers are :  Exploratory Data Analysis of a Network Telescope Traffic and Prediction of Port Probing Rates. Analysis of Hacking Related Trade in the Darkweb. Have a look at participating authors: Mehdi … Continuer la lecture de « Paper acceptation at ISI 2018 conference »

ThreatPredict at NetSoft18 conference

ThreatPredict @ IEEE NetSoft18

ThreatPredict was represented at IEEE Netsoft 2018, in the ETSN workshop: https://project.inria.fr/etsn/. Jérôme François was presenting results about methods to mine known relations between attack and vulnerability descriptions to enhance their classification and predict unknown relations.

Publications

2018

  • Quang-Vinh Dang, Jérôme François. Utilizing attack enumerations to study SDN/NFV vulnerabilities. IEEE ETSN – Emerging Trends in Softwarized Networks, Jun 2018, Montreal, Canada
  • Kathleen M. Carley, Guido Cervone, Nitin Agarwal, Huan Liu, 2018, Social Cyber-Security, In Proceedings of the International Conference SBP-BRiMS 2018, Halil Bisgin, Ayaz Hyder, Chris Dancy, and Robert Thomson (Eds.) July 10-13, 2018 Washington DC, Springer.
  • Geoffrey Dobson and Kathleen M. Carley, 2018, A Computational Model of Cyber Situational Awareness, In Proceedings of the International Conference SBP-BRiMS 2018, Halil Bisgin, Ayaz Hyder, Chris Dancy, and Robert Thomson (Eds.) July 10-13, 2018 Washington DC, Springer.
  • Ghita Mezzour, Kathleen M. Carley, L. Richard Carley. Remote Assessment of Countries’ Cyber Weapon Capabilities. Social Network Analysis and Mining (R&R)
  • T. Tang, S.A.R. Zaidi, D. McLernon, L. Mhamdi, M. Ghogho, “Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks, IEEE International Conference on Network Softwarization (NetSoft 2018), Montreal, Canada, June 2018.

Follow us on Twitter

And now the demo… try yourself at https://t.co/1k18O4KClF daily updated #Resist @Inria_Nancy @LHS_GrandEst @ThreatPredict @SecureIoTproj @im2019 #threatintelligence

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Tired to do intensive manual analysis…. attackers will do for you! or when attackers are knowledge creators for security analytics. @im2019 @Inria_Nancy @SecureIoTproj @ThreatPredict @LHS_GrandEst #CyberSecurity #ThreatIntelligence

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A. Lahmadi at DISSECT – IFIP/IEEE IM 2019 « AI cannot only drive cars but also networks…. if your network is not a 2CV » https://t.co/RwvZS6kOfp #resist @ThreatPredict @SecureIoTproj @Inria_Nancy @lahmadia

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