The Ultimate Guide to Detecting and Blocking Web Scrapers

Web scraping is part of the $21.6 billion alternative data economy, which has dominated web scraping as a standard technical task, forcing it to encompass everything on the internet. Web and large language model (LLM) scraping can generate serious fraud, and that’s the issue with the massive data quantities that are now almost exclusively freely available online.

Not all data scraping is bad, but we’re seeing a massive increase in the number of malicious bot scraping cases, with bots now making up 42% of web traffic. Yes, not all bad, but malicious bots accounted for approximately 37% of all global web traffic in 2025, marking the sixth consecutive year of growth, according to data published to SC Media.

There are numerous ways to prevent or reduce unwanted bot scraping, so we’ve created an ultimate guide to detecting and blocking web scrapers.

How to Know if It’s Scraping Traffic

You need to understand if it’s malicious scraping before removing it, starting with effective detection. To do that, you have several trusted methods:

• Identify behavioral anomalies: Use Google Analytics 4 or server logs to look for abnormal session durations, extremely high engagement rates (low bounce rates), unusual traffic spikes, and high volume from single IPs.
• Check for technical indicators: Direct traffic spikes, unusual user-agents, failed CAPTCHAs, and unexpected 429 errors.
• Analyze content usage: Reduced revenue, lower conversions, and spam content or junk data.

The method you want to use depends on the type of scraper. There are multiple types and methods they use, but some of the main ones include:

• Browser extensions
• Software-based tools
• Cloud-based scrapers
• AI/LLM-powered scrapers

There are more than that, but those are the main ones we’re seeing, and each of them has different motivations and approaches that define the type of identification method you should use.

Some bots are self-identifying, literally, and you’ll see their presence in the network. They’re generally fine, and you don’t need to worry about them.

For the sophisticated bots that you don’t want, you can use services like DataDome’s Web and LLM Management that outsmart malicious bots. The DataDome solution uses an AI system that analyzes 5 trillion daily signals, blocking even the most sophisticated attacks and providing granular control over legitimate bot access. 

You can allow verified bots and LLM scrapers to access your content as you please, especially if you’re trying to maximize SEO visibility, optimize content for Generative Engine Optimization (GEO), or monetize through pay-per-crawl models.

The Different Types of Scrapers and Identifying Them

It’s worth knowing more about the different types of scrapers and the basics of how they operate.

Self-identifying scrapers

Self-identifying scrapers are generally non-malicious bots that will announce themselves. They’re easy to find, and security teams allow them to scrape content. They use an HTTP user agent header for identification, and they have explicit permission to scrape and should provide value to the targets of their scraping. Some of the categories of scrapers that are self-identifying include:

• Search engine bots and crawlers (Google, Bing, Facebook, etc).
• Performance or security monitoring (Thousand Eyes, Blue Triangle, Bug Crowd).
• Archiving (The Internet’s Archives).

More often than not, a list of IP addresses used by the scraper is accessible, so it’s even easier to identify them. You can also search your server logs in the user-agent field and look for the user agent string that many scrapers use to identify themselves. Follow the link to learn about the user agent string, which is just basic identification information.

Impersonating scrapers

Now we’re getting to the malicious bots, and as you can imagine from the name, impersonating scrapers isn’t good.

The fact that user agents are self-reported is good and bad because they’re easily identified but easily spoofed. All scrapers are identifiable, even a Google bot, if they submit the Google bot user agent string. It’s an impersonation tactic used by malicious or unwanted scrapers because they’re smart enough to know that most websites and APIs permit traffic from known entities such as Google bots for purposes like SEO or general online visibility.

With the verified strings, malicious bots get complete access to the data they want.

Artificial intelligence scrapers

Artificial intelligence (AI) scrapers are one of the most common now. AI requires massive amounts of data to train models, and of course, they’re scraping to do it.

The issue of data leaks, data usage, and data theft is no secret in AI tools and platforms. Companies are constantly scraping free online data and using it to power for-profit AI models and services. 

Sometimes, it competes against companies that didn’t know their content was scraped.

It’s a big potential for lawsuits, with one of the most high-profile being the class action lawsuit filed in California by 16 claimants against OpenAI’s ChatGPT.

IP-based identification

Scrapers can easily identify themselves using their IP addresses. You can do a Whois lookup of the IP address to see the company owning the IP address, and thus, the company owning the scraper.

You will see some scrapers using IPs from their personally registered ASNs, which also shows the name of the entity. You might not always see the scraper’s exact identity, but you know that multiple requests from unexpected locations can signal automated scraping activity.

Reverse DNS lookups can also be effective, defined as a query that uses the DNS to locate the domain name associated with an IP address. You can put the IP address of a potential scraper into free reverse DNS lookup services, and the domain associated with that IP address should appear.

Unknown Scrapers

Most scrapers won’t identify themselves because they don’t want to be caught. Networks and security teams generally don’t know they’re there.

The methods of identification that we included in the first section (analyzing traffic patterns, high-velocity scraping patterns, etc) are great, as well as the methods we’ve mentioned throughout, such as IP analysis and behavior/session analysis.

DataDome for Blocking Unwanted Web Scraping and Content Theft

DataDome has a dedicated solution for detecting and blocking web scraping and content theft in real time so you don’t need to know ‘how to protect website from scraping’ – they’ll do it for you. It uses an AI-powered engine analysis for every request, distinguishing between legitimate users and malicious bots in milliseconds. Businesses can easily block scraping attempts before data extraction, protecting sensitive content, pricing, and intellectual property.

DataDome provides full visibility into scraping activity, showing where attacks originate and what content they’re targeting. You have total control and flexibility to allow trusted bots while blocking harmful ones, maintaining security and performance.