It’s impossible to count the number of bots, scripts, and algorithms that exist to infiltrate and harm your business. There are just too many. But it is possible to categorize them, because almost all automated threats fall into one of eight categories—different types of bot attacks. Understanding how to counter each type of bot attack will protect you against even the most advanced automated threats.

Here are the eight types of bot attacks:

1. Scraping
2. Scalping
3. Account Takeover
4. Fake Account Creation
5. Card Fraud
6. Layer 7 DDoS Attacks
7. Vulnerability Scanning
8. Ad Fraud

Scraping

Scraping is the automated collection of data from your websites, apps, and APIs, done mostly for malicious purposes like undercutting your prices or reposting your content for a fraudster’s financial gain. Scraping is often performed by a large number of bots through distributed proxies.

Scraping is the most common type of bot attack, and is increasingly used as a “gateway threat” to other, more damaging attacks like scalping. In fact, our customer data shows that scraping and scalping alone account for 98% of all bot attacks.

Common tools used for scraping prevention include:

• Traditional CAPTCHAs: Only about 50% effective today. Fraudsters can easily bypass traditional CAPTCHAs using CAPTCHA farms and CAPTCHA solve bots.
• Web Application Firewalls (WAFs): WAFs can block known threats but cannot detect new threats, meaning you must manually add new rules after attacks succeed.

Both traditional CAPTCHAs and WAFs fall short of stopping today’s advanced scraping attacks, which use bad bots to request access to your websites, applications, and APIs from distributed and often residential IPs.

Scalping

Scalping involves purchasing limited-availability goods to resell at a higher cost, generally using bots that can complete the checkout process far quicker than any human can—so fraudsters can snatch up and hoard as much of your inventory as possible. Online scalping is almost entirely automated.

Anti-bot techniques like device/browser fingerprinting, IP reputation, and behavioral analysis can help determine if a user is a human or a bot, so bots can be blocked without interrupting the human user experience.

The most effective bot detection techniques are expensive to execute manually, draining both money and time from enterprise teams who try to manage detection in-house. An efficient solution must evolve with the threat landscape and use the most up-to-date anti-bot techniques possible.

Account Takeover

Account takeover (ATO) occurs when malicious actors use bots to gain control of user accounts. These accounts are then generally used for fraudulent purposes, such as stored value theft, identity theft, credit card information theft, and fraudulent transactions. To execute a successful ATO attack, bots use credential stuffing to test countless username-password combinations so they can gain access to user accounts and data.

All too often, ATO attacks go unnoticed until bots rack up a large number of failed login attempts across several accounts. Enterprises often discover ATO too late, when the damage has already escalated. In fact, IBM reports that stolen or compromised credentials are the most common cause of data breaches and take the longest to identify—averaging 327 days to be discovered by businesses and costing on average $150,000 more when compared with other data breaches.

ATO can be mitigated somewhat by multi-factor authentication (MFA). But MFA does not always stop an ATO attack, because a wide range of techniques can work around it, like man-in-the-middle attacks, hijacked authentication APIs, SIM swaps, and social engineering.

Fake Account Creation

Fake account creation happens when fraudsters use bots to create fake user accounts on your website or application for malicious activity, such as spreading malware, influencing product reviews, or distributing false information.

Card Fraud

Card fraud, carding, and card cracking encompass anything related to the fraudulent use of payment card data. Both carding and card cracking use bots to test and guess missing values from stolen card data to make fraudulent purchases or transfer/steal funds.

Card fraud attacks can be detected by monitoring high volumes of small orders, orders with high shipping costs, IP address geolocation, data input and transaction speed, the address verification system (AVS), and card verification value (CVV). Trying to monitor all relevant details manually is very time-consuming, so it’s best to automate this process.

Layer 7 DDoS Attacks

Layer 7 DDoS (distributed denial of service) attacks target the application layer in the OSI model, typically in a “low and slow” manner, using extremely slow HTTP or TCP traffic that appears to be legitimate. DDoS attacks are perpetrated by huge botnets that can overwhelm most web infrastructures.

Today’s easy access to bots as a service (BaaS) and machine learning (ML) tools make DDoS attacks more common and allow for them to last longer than previous attacks. They can be manually mitigated by increasing network capacity (which quickly becomes costly), creating new WAF rules, manual IP filters, and ad-hoc network analysis. However, each manual option is too slow. Additionally, IP-based filtering (including WAFs) is ineffective against the thousands of proxy IP addresses used by bots.

Vulnerability Scanning

Fraudsters leverage malicious vulnerability scanning to find potential weaknesses across your mobile apps, websites, and APIs that they can then target and exploit for online fraud. Vulnerability scanning can be performed manually by humans, but is typically automated and completely performed by software programs or bots.

Ad Fraud

Ad fraud refers to the deliberate practice of engaging with online ads to generate illegitimate revenue or waste advertisers’ budgets. This is often done en masse with bots that imitate human interactions to trick security systems. Ad fraud results in advertisers paying for fake impressions, clicks, or conversions that have no real value or potential for customer engagement. It also skews campaign data, making it challenging for online marketers to figure out the actual performance of an ad.

Ad fraud can be mitigated by using advanced fraud detection tools that can analyze traffic patterns, detect anomalies, and identify fraudulent activities such as unusual click patterns or improbable conversion rates. Regularly auditing and analyzing your campaign data for anomalies is also crucial to identify and stop fraudulent activities like ad fraud.

In Conclusion

Traditional ways of protecting yourself against all types of bot attacks fall short. A WAF relies too heavily on IP-based filtering, a CAPTCHA is a nuisance for humans and largely ineffective against bots, and an in-house detection solution is often too cumbersome to implement and too difficult to maintain.

DataDome is a sophisticated bot and fraud solution that will protect you against all eight types of bot attacks. It integrates seamlessly with your existing tech stack, doesn’t take much time to set up, and operates in the background without requiring human intervention. Book a live demo today to see how it works, or try DataDome for free today.