What is Akamai and how to bypass it when web scraping?

Akamai stands as a prominent anti-bot protection service extensively utilized by numerous websites to prevent unauthorized automated access.

Although automated access blocking is typically centered security risks, platforms like Akamai, which operate as Web Application Firewalls (WAF) also block harmless web scraping bots.

In this guide, we will delve into the workings of Akamai's anti-bot technology and examine approved methods to navigate around it for effective web scraping.

What is Akamai WAF?

Akamai delivers a Web Application Firewall (WAF) service serving as a robust middleware barrier between a website and its visitors.

This essential middleware layer actively filters incoming traffic, differentiating between bot-generated activities and genuine human interactions.

While most web scrapers operate harmlessly, they are still automated entities and are commonly intercepted by Akamai's advanced antibot technology.

How to bypass Akamai?

The Akamai bypass when web scraping involves many different techniques and resources that are already implemented by most web scraping APIs.

Here are the best web scraping APIs for Akamai anti-bot protected targets:

• stockx.com

	Service	Success %	Speed	Cost $/1000	🔗
1	Scrapfly	100% =	1.2s +0.2	$0.15 =	code
2	Zenrows	100% =	8.6s -2.0	$6.9 =	code
3	Scrapingant	100% =	34.2s +9.9	$1.9 =	code
4	Scraperapi	99% =	8.1s +1.1	$9.8 =	code
5	Scrapingdog	98% -1	9.1s -1.4	$2.2 =	code
6	WebScrapingAPI	86% +48	14.9s -0.8	$2.45 =	code
7	Scrapingbee	62% +1	2.2s -0.6	$3.27 =	code

Data range Jun 20 - Jun 27

Stockx.com product and other public pages

more on stockx.com

However, to bypass Akamai WAF manually the web scraper needs to be fortified with several key features that resist Akamai identification methods. To implement that we need to take a deep look at how Akamai works.

How does the Akamai anti-bot works?

Akamai inspects each incoming connection and generates a trust score which is used as a filter to determine the likelihood of the connection coming from a real human being.

To generate this score Akamai is using various fingerprinting and data point metrics. Let's take a quick look at some.

IP Address

The first metric is the IP address of the connecting client and each client has one.

There's a limited amount of IPs on the internet and they each have distinct features that let Akamai to assign probabilities to them.

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For example, a user connecting from a home internet connection is significantly less likely to be a robot than a user connecting from a data center.

With this IP's are separated into several categories:

• Datacenter - assigned to all data centers like AWS, Google Cloud hosts and so on.
• Residential - assigned to all home connections.
• Mobile - assigned to mobile cell towers, satellites and so on.

To combat IP Address analysis, scrapers should high-quality use residential or mobile proxies that haven't been identified by Akamai yet.

Javascript fingerprinting and challenges

The second metric is the client's ability to execute Javascript. As most bots don't execute javascript an easy way to identify them is to serve a javascript challenge.

These challenges are often simple mathematical puzzles that use tokens distributed at other parts of the website. Reverse engineering this behavior can be tricky without using a real web browser.

To solve javascript challenges scrapers need to use real web browsers through headless browser automation. Most commonly through libraries like Puppeteer, Playwright or Selenium.

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Headless browsers can be identified through javascript fingerprinting techniques as they are just slightly different from user browsers in many different ways. Tiny details add up to a full evaluation profile which can be used to identify robots very successfully.

To combat fingerprinting, headless browsers need to be patched with fingerprint resistance and randomization.

HTTP fingerprinting and analysis

Real-user web browsers browse and connect in a few different ways that can be used to identify robot connections.

Most robots still use HTTP1.1 connections which are not sure by real browsers at all in 2024. Meaning, scrapers should use newer versions of the HTTP2 or HTTP3 protocol.

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While advanced HTTP clients like libcurl support HTTP2 pretty well they are susceptible to HTTP2 fingerprinting. This type of fingerprinting measures slight differences between the HTTP2 implementation of the client and the real browser.

Another fingerprinting technique related to HTTP is TLS fingerprinting. In this type of fingerprint, the TLS handshake is analyzed for slight differences between the client and the real browser.

To combat HTTP and TLS fingerprinting, scrapers need to use advanced HTTP2 capable clients that are TLS and HTTP fingerprint resistant.

Behavior and technical analysis

While scrapers usually just collect data without interacting with the website, they can still be identified through their behavior.

Most commonly this is done through scraper implementation mistakes that just don't happen with real users. Some examples:

• Forgetting custom request headers.
• Sending requests in different formats or encodings.
• Missing expected cookies or browser-specific headers like User-Agent.
• Visiting pages that aren't visited by real users which are known as honey pots.

These slight irregularities are tracked by Akamai in its trust score calculation so, scrapers need to be diligent and replicate requests as close to real user behavior as possible.

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Akamai logs every connection and use behavior and pattern analysis to identify robots. This means that scrapers that behave in an unusual pattern, connect in bursts or have any connection irregularities can be identified through AI-based analysis.

To combat behavior analysis, scrapers need to implement a human-like behavior pattern that is indistinguishable from a real user.

What are some websites that use Akamai?

Akamai is a widely adopted anti-bot and there are many popular websites encountered in web scraping that use it. Here's a quick list:

• Stockx.com
• Indeed.com
• Similarweb.com
• Maersk.com
• Tiktok.com
• Totaljobs.com
• Ebay.com

And many others, though, as most big targets often rotate and use multiple anti-bot technologies based on needs and performances.

Many other targets implement Akamai anti-bot protection temporarily when experiencing high traffic or bot attacks which affects web scraping as well.

Summary

Akamai utilizes sophisticated fingerprinting and analytical techniques to identify bots and scrapers, applying a dynamic trust score that reacts to different web scraping tactics either positively or negatively.

With Akamai consistently enhancing its anti-bot technologies, web scrapers face the imperative to quickly adjust to these continuously advancing protections.

In light of these challenges, we strongly recommend using a regularly updated and expertly managed web scraping API service. Please see the benchmarks for the most current comparative data!