ShieldSquare is now Radware Bot Manager

ShieldSquare is now Radware Bot Manager

Block bot traffic

How to Block Bot Traffic

Blocking bots requires finesse and accuracy, because you want to accurately detect bad bots with minimal false positives (when legitimate users are mistaken for bots) as well as false negatives (when bad bots are mistaken for legitimate users). Commonly used methods of blocking bots generally use rule-based measures such as blocking IP ranges, countries, and data centers known to host bots. Web Application Firewalls (WAF) and Access Control Lists (ACL) have also been used to block bad bots, but they are usually not effective when it comes to detecting advanced bots that can mimic human behavior and rotate through thousands of IP addresses and device IDs.

Why conventional bot detection approaches fail

Previous generations of bots were relatively primitive and could be easily detected and blocked because of certain characteristics such as:

  • Inability to run JavaScript
  • Use of headless browsers
  • IP addresses known to host bots
  • Use of Web automation tools
  • Behavioral characteristics such as machine-like mouse movements and page traversals

With the increasing sophistication of bots today, such basic interaction-based detection methods are ineffective against the latest generations of bots. This is why bot detection today using shallow characteristics is no longer effective, and instead discovering the intent of every visitor to a website, app and API is crucial when it comes to reliable and effective bot detection and management. In-house bot detection measures that some businesses initially deploy have several disadvantages compared to a specialized solution, and thus generally fail to detect the most advanced and malicious bots.

Bot detection requires a specialized approach

Today’s fourth-generation bots are far more sophisticated and human-like in their behavior when compared to the primitive first- and second-generation bots. They use large numbers of IP addresses and device IDs to carry out ‘low and slow’ attacks which help them evade conventional rule-based security measures such as IP blacklists, WAFs and ACLs. With recent advances in ‘behavior hijacking’ techniques, bots can be programmed to change their observable characteristics and behavior to mimic those of humans using PCs and smartphones. This has made it virtually impossible for conventional security systems to differentiate between advanced bots and humans. Due to the challenge of identifying advanced bots, intent detection is an essential capability for Bot Risk Management (BRM) solutions.

How diffrent generations of bots can be detected

The need for a dedicated bot management solution

Conventional bot mitigation solutions try to analyze visitors’ interactions with the website or app — such as mouse movements, touch patterns, and page traversals. However, these approaches are becoming increasingly ineffective, because bots with advanced human-like interaction capabilities can evade these measures

Instead of only analyzing interactions, a solution such as Radware Bot Manager tries to understand the intent of highly sophisticated non-human traffic by using proprietary techniques such as Intent-based Deep Behavior Analysis (IDBA) that leverage AI and Machine Learning. As bots continue to evolve, such approaches provide significantly higher levels of accuracy in detecting bots. Here’s a quick and handy overview of the core functionalities you should look for in a bot management solution.

WhitePaper

eBook

The Ultimate Guide to Bot Management

The-Big-Bad-Bot-Report

Whitepaper

Development of In-House Bot Management Solutions and Their Pitfalls

Product_Brief

Blog

An Overview of the Core Functionality Needed in a Bot Management Solution


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