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

Kasada is a popular anti-bot protection service, widely employed by a variety of websites to deter unauthorized automated access.

While the focus of automated access control often lies on security risks, systems like Kasada, functioning as Web Application Firewalls (WAF), can also inadvertently block benign web scraping bots.

This guide will explore Kasada's anti-bot technology in detail and discuss legitimate strategies for circumventing it to facilitate effective web scraping.

What is Kasada WAF?

Kasada provides a Web Application Firewall (WAF) service, establishing a robust barrier between a website and its visitors to ensure security.

This middleware layer filters incoming traffic distinguishing between bot-driven activities and legitimate human interactions.

Although most web scrapers are non-malicious, they are automated and frequently caught by Kasada's complex anti-bot technology.

How to bypass Kasada?

The Kasada 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 Kasada anti-bot protected targets:

• realtor.com

	Service	Success %	Speed	Cost $/1000	🔗
1	Scrapfly	99% -1	5.9s +1.7	$0.78 +0.01	code
2	Scrapingdog	99% +14	8.6s +0.1	$2.2 =	code
3	Scraperapi	93% -7	10.1s +3.8	$4.9 =	code
4	WebScrapingAPI	91% +1	9.4s +=	$4.9 =	code
5	Scrapingbee	86% +3	3.9s -0.3	$3.27 =	code
6	Zenrows	73% -6	4.3s +0.3	$2.76 =	code
7	Scrapingant	0%	-	-	code

Data range Jun 20 - Jun 27

Realtor.com property listing and search pages

more on realtor.com

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

How does the Kasada anti-bot works?

Kasada 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 Kasada 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 Kasada 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 Kasada 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 Kasada 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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Kasada 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 Kasada?

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

• Realtor.com
• Realestate.com.au
• Target.com
• Footlocker.com
• Hotels.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 Kasada anti-bot protection temporarily when experiencing high traffic or bot attacks which affects web scraping as well.

Summary

Kasada employs cutting-edge fingerprinting and analysis methods to detect bots and scrapers, assigning a dynamic trust score based on web scraper capabilities and overall behavior.

As Kasada continually advances its anti-bot measures, it is crucial for web scrapers to rapidly evolve and adapt to these sophisticated defenses.

Given these ongoing developments, we highly recommend adopting a state-of-the-art, continuously maintained web scraping API service. Consult the benchmarks for the latest performance comparisons!