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Bad bots have landed...and they're disrupting your fraud solution

Author: Sammi Jones


Cybercriminals always have a new trick up their sleeve. The latest? Using bots to disguise and commit fraudulent activities across the digital landscape.

In 2022, roughly 47.4% of all internet traffic came from bots, a 5.1% increase from 2021. As for “bad” bot traffic fueled by malicious intent? For the fourth year in a row, the volume of bad bot traffic skyrocketed to a whopping 30.2%, up 2.5% from 2021.

As technology evolves and generative AI becomes more sophisticated, fraudsters are leveraging bad bots faster and easier than ever before (even ChatGPT can write code for a bots). From stealing personal data to manipulating accounts, bad bots are costing companies billions of dollars annually and targeting businesses across a wide array of industries such as travel, retail, and financial services.

What can your company do to combat bad bots? How can you be sure your fraud solution is firing on all cylinders to keep attacks at bay? First, you need to think like a cybercriminal. And second, you need to leverage advanced bot detection technology that can adapt to fraudsters on a whim.

What is a bot?

The internet is swarming with bots. Simply put, a bot is a software application that’s programmed to automate a series of steps and tasks. Bots are used to perform repetitive tasks at fast paces, such as opening a page or clicking a link, and the clever ones imitate or replace a person’s behavior.

While a bot itself isn’t inherently good or bad, a programmer’s intent decides its fate. A “good” bot performs useful and helpful tasks — search engine crawlers review websites for content, ecommerce bots deliver price comparison results. Whereas a “bad” bot performs harmful actions — spam bots insert links to phishing sites, credential stuffing bots use compromised credentials to log in to financial services, and ad bots use fake clicks to drive up ad spend.

Bots in fraud: an ever-evolving threat

Much to the fraudster’s delight, bad bots are rapidly evolving and becoming better at outsmarting the fraud detection solutions many companies already have in place. While the bots of yesterday lacked cookies and behaved like clunky robots, the bots of today spoof user and device IDs and can mimic human behaviors to appear like regular users.

Types of fraud bots today include:

  • Form filling which can be used for credential stuffing, account enumeration, content scraping, form spam, and carding (unauthorized use of credit cards).
  • API traffic which can be used to impersonate users, emulate devices, and launch full-scale DDoS (Distributed Denial-of-Service) attacks to flood servers and disrupt the normal flow of traffic.

Over the last decade cybercriminals have also simplified their ability to carry out larger fraudulent attacks thanks to botnets. Sophisticated backbones of cybercrime, botnets are “robot networks” of computers and devices that are infected with malware and controlled by a single entity. Fraudsters simply write the malware that drives the bad bots and, because the networks are centrally controlled, they’re able to scale up millions of computers and carry out large cyberattacks by exploiting security gaps and vulnerabilities in software and on websites.

How to beat fraudsters at their own game

To stay ahead of sneaky fraudsters and their malicious bots, companies must leverage an advanced bot detection solution that spots the behavioral differences between good users (humans) and bad users (bots). Companies must also ensure their bot detection solution utilizes machine learning (ML) to continually adapt and stay at the forefront of evolving bot activities.

See the human, spot the bot

While bad bots are getting better at mimicking human behaviors, an advanced bot detection solution uses behavioral biometrics to distinguish real users from phonies. The right technology captures signals on how visitors behave in real time. Those captured signals are compiled into a robust ID graph that analyzes and compares the data, including behavioral biometrics, to detect bot activity. For example, a bot that’s not committing fraud wouldn’t typically log in. Some other indicators include:

  • Interaction with the site or app
  • Interaction with specific pages
  • Number of pages in a session and navigational patterns
  • Behaviors exhibited within a session (such as scrolling, typing speed, and device orientation)
  • Known block list of servers and origins

By detecting, capturing, and analyzing signals on how a visitor behaves, companies can utilize rich behavioral data to accurately differentiate good visitors from bad visitors.

Combat the enemy with adaptive machine learning

When faced with a roadblock, bad bots are quick to evolve. Is your company’s existing bot detection solution agile enough to do the same?

The right advanced bot detection solution uses machine learning models and artificial intelligence to not only identify bad bots before they strike, but also adapt to evolving bot technology. By monitoring visitor behavior, traffic patterns, and the latest bot schemes and strategies, companies can reduce their risks of falling victim to bad bots.

Outsmart the cybercriminal

When it comes to fraudulent activities, a cybercriminal is always eager to pull a new bad-bot-trick from their sleeve. The best way to combat these illusive and deceptive schemes is to ensure your company is using an advanced fraud solution that captures behavioral biometrics in real time, and leverages machine learning models to stay ahead of new threats as they emerge. Top-tier technology and advanced data insight is always a winning combination.

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