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Evolution of Cybersecurity and the Role of Machine Learning

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Cristian Garcia G.
Cristian Garcia G.

This document discusses the evolution of cybersecurity threats and defenses over time. It describes how threats have evolved from early viruses distributed via email to today's targeted attacks and advanced persistent threats. It also outlines how security defenses have evolved from early antivirus software and firewalls to today's next-generation firewalls, endpoint protection, cloud security, and use of machine learning. The document argues that isolated security tools are ineffective, and an integrated platform is needed to effectively defend organizations across networks, endpoints, and clouds in today's digital era.

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EVOLUCIÓN DE “CIBERSEGURIDAD” Fernando Gamero Systems Engineer, Perú y Ecuador
TECNOLOGIA ES PARTE DE NUESTRAS VIDAS 2 | © 2018 Palo Alto Networks. All Rights Reserved.
EL DÍA a DÍA DE UN OFICIAL DE SEGURIDAD 3 | © 2018 Palo Alto Networks. All Rights Reserved. RISK SPEED
4 | © 2018 Palo Alto Networks. All Rights Reserved.
5 LAS AMENAZAS
6 6 | © 2015, Palo Alto Networks. Confidential and Proprietary. Evolución de las amenazas DOS viruses Eg: Brain (1986), Jerusalem (1987), Morris (1988) y Michelangelo (1991) 1986-1995 1995-1999 Distribución por internet Propagación a través de correo electrónico y el impacto llega a usuarios en hogares 2000-2005 Malware masivo (1999-2005) Eg: Loveleter (2000), Anna Kournikova (2001), MyDoom (2004), Sony rootkit (2005) 2005-2010 Malware for profit (2005-2010) Botnets para injección de codigo, robar credenciales de FTP, primeros malware para controlar maquinaria industrial Eg: Stuxnet (2010) 2010+ Targeted Breaches & APTs Ciber-crimen Malware para Macs, Android e IOT Ransomware
7 LOS SUPER HEROES
8 8 | © 2015, Palo Alto Networks. Confidential and Proprietary. Evolución de la Seguridad IDS 1985 1994 Stateful Firewall 2007 NGFW 2004 Sandbox 1987 Anti Virus Año 2018- qué sigue? - Nube pública y privada - IoT - Machine learning - Automatización Defence in Depth 1991
INCREMENTO DE INVERSION EN HERRAMIENTAS Y PERSONAS 9 | © 2018 Palo Alto Networks. All Rights Reserved.
10 | © 2017, Palo Alto Networks. All Rights Reserved. 3 EVOLUCIONES
Al Principio… 11 | © 2015, Palo Alto Networks. Confidential and Proprietary. IMDLPIPS ProxyURLAV Internet/WANOrganizational Network UTM
PALO ALTO NETWORKS SECURITY OPERATING PLATFORM 12 | © 2018 Palo Alto Networks, Inc. All Rights Reserved. EVOLUTION I NETWORK SECURITY CLOUD-DELIVERED SECURITY SERVICES Threat Prevention URL Filtering Malware Analysis THREAT DATA
Wildfire Bare Metal Analysis Static Analysis Dynamic Analysis Dynamic Unpacking Detect known exploits, malware, and variants Find new zero-day exploits & malware through execution Heuristic Engine Steer evasive malware to bare metal Identify VM-aware threats using hardware systems Memory analysis Machine learning File anomalies Malicious patterns Known malicious code Custom hypervisor Behavioral scoring Multi-version analysis Full dynamic analysis Real desktop hardware No virtual environment No hypervisor © 2018, Palo Alto Networks. All Rights Reserved.
14 | © 2018, Palo Alto Networks, Inc. All Rights Reserved. LO DESCONOCIDO
15 | © 2018, Palo Alto Networks, Inc. All Rights Reserved. LO CONOCIDO
Malware por aplicación 16 | © 2018, Palo Alto Networks. All Rights Reserved. Stats de Jul 2017 a Jul 2018
HERRAMIENTAS DESCONECTADAS NO PROVEEN SEGURIDAD DE FORMA EFECTIVA EN EL PERIMETRO TRADICIONAL URL/IP Sandbox AV IPS Forensics UBA Threat Intel HIPS EDR Endpoint AV MFA Orchestration 17 | © 2018 Palo Alto Networks. All Rights Reserved.
NO EXISTE EL PERIMETRO HERRAMIENTAS ISLADAS SON TOTALMENTE INEFECTIVAS PARA NUESTRA ERA DIGITAL URL/IP Sandbox Forensics UBA Threat Intel HIPS EDR Endpoint AV MFA Orchestration BRANCH MOBILE SaaS PUBLIC CLOUD SaaS Security Cloud SecuritySaaS Security Cloud Security Branch Security Mobile Security Branch Security Mobile Security SaaS Security Cloud Security Branch Security Mobile Security SaaS Security Cloud Security Branch Security Mobile Security Proxy AV IPS 18 | © 2018 Palo Alto Networks. All Rights Reserved.
PALO ALTO NETWORKS SECURITY OPERATING PLATFORM 19 | © 2018 Palo Alto Networks, Inc. All Rights Reserved. EVOLUTION IEVOLUTION II NETWORK SECURITY ADVANCED ENDPOINT PROTECTION CLOUD SECURITY CLOUD-DELIVERED SECURITY SERVICES Threat Prevention URL Filtering Malware Analysis THREAT DATA
20 | © 2018, Palo Alto Networks. All Rights Reserved.
LA ANATOMIA DE UN ATAQUE EXITOSO Connectivity rate change Vulnerability Exploit Malware Installation Command and Control Lateral Movement Data Exfiltration Repeated access to an unusual site Unusually large upload • Ocurre en segundos o minutos • Involucra un numero menor de acciones en la red • Puede ser identificado por IoCs • Ocurre durante días, semanas o meses • Involucra un gran numero de acciones en la red • Dificilmente identificado por IoCs
23 | © 2018, Palo Alto Networks, Inc. All Rights Reserved. INTELIGENCIA ARTIFICIAL
EVOLUTION II EVOLUTION III PALO ALTO NETWORKS APPS 3rd PARTY PARTNER APPS CUSTOMER APPS CLOUD-DELIVERED SECURITY SERVICES Threat Prevention URL Filtering Malware Analysis 24 | © 2018 Palo Alto Networks, Inc. All Rights Reserved. APPLICATION FRAMEWORK & LOGGING SERVICE NETWORK SECURITY ADVANCED ENDPOINT PROTECTION CLOUD SECURITY
STOP ADVANCED THREATS WITH MAGNIFIER NETWORK MAGNIFIER Behavioral Analytics ENDPOINT CLOUD 25 | © 2018 Palo Alto Networks, Inc. All Rights Reserved. MACHINE LEARNING • Automatically detect attacks using rich data • Accelerate investigations with endpoint analysis • Achieve scalability and agility as a cloud-delivered application THREAT DATA & LOGS
JOIN THE GROWING ECOSYSTEM 26 | © 2018, Palo Alto Networks. All Rights Reserved.
GRACIAS Fernando Gamero, Sales Engineer, Perú y Ecuador fgamero@paloaltonetworks.com Kenneth Tovar, Regional Sales Manager, Perú y Ecuador ktovar@paloaltonetworks.com

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Evolution of Cybersecurity and the Role of Machine Learning

  • 2. TECNOLOGIA ES PARTE DE NUESTRAS VIDAS 2 | © 2018 Palo Alto Networks. All Rights Reserved.
  • 3. EL DÍA a DÍA DE UN OFICIAL DE SEGURIDAD 3 | © 2018 Palo Alto Networks. All Rights Reserved. RISK SPEED
  • 4. 4 | © 2018 Palo Alto Networks. All Rights Reserved.
  • 6. 6 6 | © 2015, Palo Alto Networks. Confidential and Proprietary. Evolución de las amenazas DOS viruses Eg: Brain (1986), Jerusalem (1987), Morris (1988) y Michelangelo (1991) 1986-1995 1995-1999 Distribución por internet Propagación a través de correo electrónico y el impacto llega a usuarios en hogares 2000-2005 Malware masivo (1999-2005) Eg: Loveleter (2000), Anna Kournikova (2001), MyDoom (2004), Sony rootkit (2005) 2005-2010 Malware for profit (2005-2010) Botnets para injección de codigo, robar credenciales de FTP, primeros malware para controlar maquinaria industrial Eg: Stuxnet (2010) 2010+ Targeted Breaches & APTs Ciber-crimen Malware para Macs, Android e IOT Ransomware
  • 8. 8 8 | © 2015, Palo Alto Networks. Confidential and Proprietary. Evolución de la Seguridad IDS 1985 1994 Stateful Firewall 2007 NGFW 2004 Sandbox 1987 Anti Virus Año 2018- qué sigue? - Nube pública y privada - IoT - Machine learning - Automatización Defence in Depth 1991
  • 9. INCREMENTO DE INVERSION EN HERRAMIENTAS Y PERSONAS 9 | © 2018 Palo Alto Networks. All Rights Reserved.
  • 10. 10 | © 2017, Palo Alto Networks. All Rights Reserved. 3 EVOLUCIONES
  • 11. Al Principio… 11 | © 2015, Palo Alto Networks. Confidential and Proprietary. IMDLPIPS ProxyURLAV Internet/WANOrganizational Network UTM
  • 12. PALO ALTO NETWORKS SECURITY OPERATING PLATFORM 12 | © 2018 Palo Alto Networks, Inc. All Rights Reserved. EVOLUTION I NETWORK SECURITY CLOUD-DELIVERED SECURITY SERVICES Threat Prevention URL Filtering Malware Analysis THREAT DATA
  • 13. Wildfire Bare Metal Analysis Static Analysis Dynamic Analysis Dynamic Unpacking Detect known exploits, malware, and variants Find new zero-day exploits & malware through execution Heuristic Engine Steer evasive malware to bare metal Identify VM-aware threats using hardware systems Memory analysis Machine learning File anomalies Malicious patterns Known malicious code Custom hypervisor Behavioral scoring Multi-version analysis Full dynamic analysis Real desktop hardware No virtual environment No hypervisor © 2018, Palo Alto Networks. All Rights Reserved.
  • 14. 14 | © 2018, Palo Alto Networks, Inc. All Rights Reserved. LO DESCONOCIDO
  • 15. 15 | © 2018, Palo Alto Networks, Inc. All Rights Reserved. LO CONOCIDO
  • 16. Malware por aplicación 16 | © 2018, Palo Alto Networks. All Rights Reserved. Stats de Jul 2017 a Jul 2018
  • 17. HERRAMIENTAS DESCONECTADAS NO PROVEEN SEGURIDAD DE FORMA EFECTIVA EN EL PERIMETRO TRADICIONAL URL/IP Sandbox AV IPS Forensics UBA Threat Intel HIPS EDR Endpoint AV MFA Orchestration 17 | © 2018 Palo Alto Networks. All Rights Reserved.
  • 18. NO EXISTE EL PERIMETRO HERRAMIENTAS ISLADAS SON TOTALMENTE INEFECTIVAS PARA NUESTRA ERA DIGITAL URL/IP Sandbox Forensics UBA Threat Intel HIPS EDR Endpoint AV MFA Orchestration BRANCH MOBILE SaaS PUBLIC CLOUD SaaS Security Cloud SecuritySaaS Security Cloud Security Branch Security Mobile Security Branch Security Mobile Security SaaS Security Cloud Security Branch Security Mobile Security SaaS Security Cloud Security Branch Security Mobile Security Proxy AV IPS 18 | © 2018 Palo Alto Networks. All Rights Reserved.
  • 19. PALO ALTO NETWORKS SECURITY OPERATING PLATFORM 19 | © 2018 Palo Alto Networks, Inc. All Rights Reserved. EVOLUTION IEVOLUTION II NETWORK SECURITY ADVANCED ENDPOINT PROTECTION CLOUD SECURITY CLOUD-DELIVERED SECURITY SERVICES Threat Prevention URL Filtering Malware Analysis THREAT DATA
  • 20. 20 | © 2018, Palo Alto Networks. All Rights Reserved.
  • 21. LA ANATOMIA DE UN ATAQUE EXITOSO Connectivity rate change Vulnerability Exploit Malware Installation Command and Control Lateral Movement Data Exfiltration Repeated access to an unusual site Unusually large upload • Ocurre en segundos o minutos • Involucra un numero menor de acciones en la red • Puede ser identificado por IoCs • Ocurre durante días, semanas o meses • Involucra un gran numero de acciones en la red • Dificilmente identificado por IoCs
  • 22. 23 | © 2018, Palo Alto Networks, Inc. All Rights Reserved. INTELIGENCIA ARTIFICIAL
  • 23. EVOLUTION II EVOLUTION III PALO ALTO NETWORKS APPS 3rd PARTY PARTNER APPS CUSTOMER APPS CLOUD-DELIVERED SECURITY SERVICES Threat Prevention URL Filtering Malware Analysis 24 | © 2018 Palo Alto Networks, Inc. All Rights Reserved. APPLICATION FRAMEWORK & LOGGING SERVICE NETWORK SECURITY ADVANCED ENDPOINT PROTECTION CLOUD SECURITY
  • 24. STOP ADVANCED THREATS WITH MAGNIFIER NETWORK MAGNIFIER Behavioral Analytics ENDPOINT CLOUD 25 | © 2018 Palo Alto Networks, Inc. All Rights Reserved. MACHINE LEARNING • Automatically detect attacks using rich data • Accelerate investigations with endpoint analysis • Achieve scalability and agility as a cloud-delivered application THREAT DATA & LOGS
  • 25. JOIN THE GROWING ECOSYSTEM 26 | © 2018, Palo Alto Networks. All Rights Reserved.
  • 26. GRACIAS Fernando Gamero, Sales Engineer, Perú y Ecuador fgamero@paloaltonetworks.com Kenneth Tovar, Regional Sales Manager, Perú y Ecuador ktovar@paloaltonetworks.com