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AI vs. AI: Can Predictive Models Stop the Tide of Hacker AI?

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Machine learning and artificial intelligence (AI) have become essential components of any effective cyber
security plan. They allow us to spot and take down fraud more quickly and accurately than any traditional
method, and provide a much higher level of efficiency. When we are fighting against humans with AI on
our side, we know that we will always have the advantage. We also know that every defensive action we
take will undoubtedly create a reaction from fraudsters. So, what would happen if fraudsters were to begin
using AI technology? Would they be able to defeat existing defenses? Our dedicated research team of data
scientists decided to find out.

Published in: Data & Analytics
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AI vs. AI: Can Predictive Models Stop the Tide of Hacker AI?

  1. 1. AIvs.AI Can Predictive Models Stop theTide of Hacker AI? Alejandro Correa Bahnsen, Chief Data Scientist
  2. 2.  Portfolio of cybersecurity software and services  Intelligent and adaptive  Cloud-native and hybrid-ready  Global colocation leader  57 data centers in 29 global markets  2.6M sq. feet of data center space  195 megawatts of power  3,500 customers  1,100 employees  Headquartered in Miami with offices globally  Experienced leadership in infrastructure and security CyxteraTechnologies 2
  3. 3. 3
  4. 4. 4 PossibleAdversaryUsesofAI More & Better Phishing Attacks Increasingly Powerful Self-Spreading Malware Weaken Authentication Controls Cheat Rule-based Transaction Monitoring
  5. 5. 5
  6. 6. 6 Phishing
  7. 7. Asthreatactorsimprovetheirattacks,isAIthe newtechnologytheywilluse?
  8. 8. TheExperimentProcess Identify individual threat actors Ran them through our own AI detection system Improved their attacks using AI
  9. 9. AIinAction 9 Initial URLs Synthetic URLs http://naylorantiques.com/secure/login/929874xxx94n/ http://www.naylorantiques.com/content/centrais/fone_facil http://kisanart.com/arendivento/menu-opcoes-fone-facil/ http://naylorantiques.com/atendimento/menu-opcoes-fone-facil/3 http://naylorantiques.com/arendimento/menu-opcoes-fone-facil/ http://naylorantiques.com/paonimone/ieticor_tent/olesco http://naylorantiques.com/docs/canais_atendimento/11/ Model
  10. 10. TraditionalAttacksvs.AI-DrivenAttacks 10 25% 20% 15% 10% 5% 0% 3000% more successful AITraditional Phishing Campaign Success Rate
  11. 11. TakeAction,TheTimeIsNow 11 AIenhancesattackersefficiencies ML and AI driven detection systems Deep Adversarial Learning Relentless monitoring Multi-layered approach to anti-fraud
  12. 12. 12 ThePowerofAdversaryAI More & Better Phishing Attacks Increasingly Powerful Self-Spreading Malware Weaken Authentication Controls Cheat Rule-based Transaction Monitoring
  13. 13. 13  1-Minute ResearchVideo Brief  2 Page Research Summary  Slides (Extended Version)  Academic paper AIvs.AI:CanPredictiveModelsStoptheTideofHackerAI? EasySolutionsBooth#3941 www.easysol.net/ai-project
  14. 14. www.cyxtera.comwww.easysol.net

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