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.

1. © AKAMAI - EDGE 2017
Bot Manager + Cloudlet strengthen mitigation capability
Quentin Leung & Feybian Yip

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Speaker Introduction
Quentin Leung
Senior Technical Project
Manager
Feybian Yip
Senior Engagement
Manager

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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

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1/ Walk through typical use case of bot manager
Good
Good
Bad
Bad
Unknown Unknown
Known Known

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1/ Walk through typical use case of bot manager
Price Scraping Marketing Effectiveness
Credential Abuse

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2/ Share what tomorrow’s bot looks like on Attack side
Good
Good
Bad
Bad
Unknown Unknown
Known Known

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2/ Share what tomorrow’s bot looks like on Attack side
More Easy: 60s to build a botnet

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2/ Share what tomorrow’s bot looks like on Attack side
More Target Oriented Comply with best rate guarantee
Influence competitor business

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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

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2/ Share what tomorrow’s bot looks like on Attack side (Cont.)
Impact - Fraud Orders from Bot

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3/ Explain evolution approach on bot detection & mitigation on Defense side
Origin
CDN
Data Analysis@Origin
Decision
Automation via API
Bad Bots
Good Bots

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3/ Explain evolution approach on bot detection & mitigation on Defense side

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3/ Explain evolution approach on bot detection & mitigation on Defense side
+Akamai Cloudlet
<1m Propagation Time
Dev. Ops. Friendly

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3/ Explain evolution approach on bot detection & mitigation on Defense side
Identify
Delay: 1-3s
Slow: 8-10s
Customized Content
Block

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4/ Demo

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