Bot Manager + Cloudlet Strengthen Mitigation Capability
As bot traffic is getting smarter and mutating faster, prompt mitigation is now more necessary than ever. After successfully detecting bot traffic via Akamai Bot Manager's pattern- and behavior-based intelligence, the challenge quickly shifts to mitigation. Consider the case of traffic that appears normal -- but suddenly shifts to bot-like behavior, pulling data and crawling your entire site. Instant mitigation is needed to deny or slow down the bot traffic in order to protect your business and minimize the impact on real customers. In this session, we'll explore how Akamai Bot Manager can be paired with Phased Release Cloudlet to achieve dynamic and nearly instant bot-traffic mitigation. We'll demo and share how Bot Manager and Cloudlet work together, seamlessly leveraging Cloudlet’s fast propagation mechanism and API-friendly characteristics on top of Bot Manager's intelligent detection capability.
Related slideshows
Bot Manager + Cloudlet Strengthen Mitigation Capability
- 1. © AKAMAI - EDGE 2017 Bot Manager + Cloudlet strengthen mitigation capability Quentin Leung & Feybian Yip
- 2. © AKAMAI - EDGE 2017 Speaker Introduction Quentin Leung Senior Technical Project Manager Feybian Yip Senior Engagement Manager
- 3. © AKAMAI - EDGE 2017 Session’s Agenda 1/ Walk through typical use case of bot manager 2/ Share what tomorrow’s bot looks like on Attack side 3/ Explain evolution approach on bot detection and mitigation on Defense side 4/ Demo
- 4. © AKAMAI - EDGE 2017 1/ Walk through typical use case of bot manager Good Good Bad Bad Unknown Unknown Known Known
- 5. © AKAMAI - EDGE 2017 1/ Walk through typical use case of bot manager Price Scraping Marketing Effectiveness Credential Abuse
- 6. © AKAMAI - EDGE 2017 2/ Share what tomorrow’s bot looks like on Attack side Good Good Bad Bad Unknown Unknown Known Known
- 7. © AKAMAI - EDGE 2017 2/ Share what tomorrow’s bot looks like on Attack side More Easy: 60s to build a botnet
- 8. © AKAMAI - EDGE 2017 2/ Share what tomorrow’s bot looks like on Attack side More Target Oriented Comply with best rate guarantee Influence competitor business
- 9. © AKAMAI - EDGE 2017 2/ Share what tomorrow’s bot looks like on Attack side (Cont.) Impact - Fraud Orders from Bot Market Data: <provide reference> 2.9% to 7.6% lost from online revenue •1.3% direct lost •2 to 5% real sales impact Ecommerce lost $7billion to chargeback in 2016 Estimate this number reaches $31billion by 2020 Fraud rate for physical goods was 0.38% Fraud rate for digital goods was 0.42% Industry like E-ticketing reported 2x to fraud rate from online vs box office
- 10. © AKAMAI - EDGE 2017 2/ Share what tomorrow’s bot looks like on Attack side (Cont.) Impact - Fraud Orders from Bot
- 11. © AKAMAI - EDGE 2017 3/ Explain evolution approach on bot detection & mitigation on Defense side Origin CDN Data Analysis@Origin Decision Automation via API Bad Bots Good Bots
- 12. © AKAMAI - EDGE 2017 3/ Explain evolution approach on bot detection & mitigation on Defense side
- 13. © AKAMAI - EDGE 2017 3/ Explain evolution approach on bot detection & mitigation on Defense side +Akamai Cloudlet <1m Propagation Time Dev. Ops. Friendly
- 14. © AKAMAI - EDGE 2017 3/ Explain evolution approach on bot detection & mitigation on Defense side Identify Delay: 1-3s Slow: 8-10s Customized Content Block
- 15. © AKAMAI - EDGE 2017 4/ Demo
- 16. © AKAMAI - EDGE 2017