Combatting Fraudulent Bots: Essential Strategies for Business Protection
As fraudulent attacks become more nuanced and disruptive, bot detection has become an ongoing challenge for global business.
Simply put, bot detection refers to identifying and distinguishing real people from non-human users. Not all bots are bad, but when used with the wrong intention, they can cause irrevocable damage. They are often the weapon of choice by fraudsters looking to engage in harmful activities such as account takeover. Malicious bots can also cost businesses big, such as in the case of pay-per-click (PPC) fraud, which can skew marketing and advertising analytics and consume budget spend.
A business’s goal for bot detection should be to prevent malicious bots from engaging in activities like spamming, hacking, and scraping private data, which can ruin their day-to-day operations and eventually cost the business (and their customers) a lot of money.
How to Detect Fraud
Common methods for bot detection include:
- IP Analysis: Examines the IP address of incoming traffic to identify suspicious patterns or known malicious sources. It can flag multiple requests from the same IP or requests from IPs associated with VPNs or proxy servers often used by bots.
- CAPTCHA: Presents challenges designed to be easy for humans but difficult for bots, such as identifying distorted text or selecting specific images. This helps verify that the user is human.
- Device fingerprinting: Collects various attributes of a user’s device (like browser type, screen resolution, and installed fonts) to create a unique fingerprint. This can identify when multiple accounts are created from the same device, a common bot tactic.
- Artificial Intelligence (AI) and Machine Learning (ML): These systems analyze large datasets to identify patterns indicative of bot behavior. They can adapt to new bot tactics over time, improving detection accuracy.
- Behavioral biometrics: Monitors user interactions like mouse movements, typing patterns, or touchscreen gestures. Bots often exhibit different behavioral patterns compared to humans, which this tool can detect.
The key is to detect and analyze customer behavioral patterns and data to identify actions unique to bots.
For example, bots may be programmed to click on links or fill out forms in a specific way or at a certain speed. These bots may also attempt to access the same page several times in a short period from various IP addresses. Using the data patterns as indicators, your business can quickly detect suspicious activity before it’s too late.
Why Are Hackers Becoming More Sophisticated?
Over the years, it has become increasingly difficult for businesses to detect hacker attacks in real-time.
Advancements in technology, driven by AI, natural language processing (NLP), and other methods, have added fuel to the fire. These advanced systems evade detection and mimic humans with increasing ease. Scary, right?
The takedown of the Genesis Marketplace earlier this year showed how bots and the criminals operating them have become organized at scale, affecting millions of people worldwide.
Developing Your Bot Detection Strategy
The truth is that fraudulent activity is big business. Last year alone, fraud cost consumers $8.8 billion and is a growing problem.
A sophisticated bot detection strategy should incorporate bot detection models that are agile and not held back by prescriptive, limited data capture approaches, like tagging. For example, data capture via tagging can potentially only be reduced to minutes with a good system in place. An alternative to tagging is a solution that captures everything – one that contextualizes and activates data in milliseconds, leading to increased conversions and more complete data sets.
Additionally, when developing your bot detection strategy, you should consider how machine learning and AI models can help identify bad bots before they strike. These types of models aid in monitoring behavior and sketchy patterns.
Conclusion
Companies that can detect malicious bots before they have a chance to wreak havoc can be better equipped to offer a seamless customer experience. Data-driven strategies, predicated on complete data sets, add an extra layer of protection to afford organizations greater assurances for their customers from the damaging effects of fraudulent activity. Organizations must continue to overcome increasingly smarter and more sophisticated bots, with strategies and partners that combine continuous monitoring with AI and ML measures and updates.
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