Bot Detection: Identification and Prevention

Introduction

One of the major security concerns for any business or organization is (should be) Bot Detection. Around one-third of the world’s major transactions on the web, known as web traffic consists of malicious bots. These bots are generally responsible for the declining rate of integrity that businesses are facing today.

However, bot detection is a much more arduous task than ever. A lot of companies are hiring debuggers and developers to find new ways to mitigate bot traffic. Artificial Intelligence is a growing field, which is use in bot detection and makes it easier to aid in identifying bots.

Let’s look at the growing state of bot technologies and distribution, the major need for bot detection, and what steps will need to secure online assets and threats in near future.

Bot detection is more complicated than it looks

Bot developers now embrace new technologies to design their bots to bypass various bot detection systems, which makes it impossible to detect whether the individual is a human visitor or a bot.

Over the last few years, bot involvement in the internet has grown tremendously. We can classify this evolution into four main generations:

Gen 1:

bots are just simple web crawlers that are commonly find in most of the webpages. These bots are responsible for performing small scale automation tasks such as scraping information from the web page. They don’t maintain a session cookie, hence easy to identify.

Gen 2:

bots include a few common web crawlers  such as Nutch and Scrapy. They too are easy to identify since they are devoid of JavaScript firing, which is mainly responsible for easy detection.

Gen 3:

bots are different from the first two categories since they represent a whole browser. Few examples include PhantomJS and CasperJS which prelude to the slow and low-key attacks: volume-based thresholding. Detecting them requires challenge tests and fingerprinting.

Gen 4:

bots are extremely convincing that can copy human behavior or sometimes hide underneath a user session. No interface or API is safe if these bots are find and must be protect. Can be detect by a very sophisticate client-side finger printing and behavioral analysis.

The recent bot generations are much more advanced and are indistinguishable from human users. Unless some strong bot detection techniques are in use, it’s impossible to detect bots. 

Traditional bot detection solutions are no longer effective

Most common security software, such as WAFs, rely on IP tracking to block bots. This fact is based on the fact that any malicious activity from that IP address is marked as a blacklisted IP.

This method was not that effective since bot developers at a later stage evolve to use proxies. Even if website owners decide to block a particular proxy IP, bot developers managed to find alternatives to embed bots into web pages.

One of the most frequently used features by bot developers was Tor. Due to multiple exit nodes, one cannot pinpoint the actual IP address of the page. Also, it’s easy to switch to alternate exit paths and attack the same page if the former IP was blacklist. 

As a result, bot detector solutions that rely mainly on IP blocking weren’t that effective. More sophisticate detection techniques are to be consider.

Behavior-based bot detection is the future now

Since bots are capable of perfectly mimicking human behavior, it’s likely that volumetric detection will result in nothing. Similarly, IP-based security systems are not effective in fighting against malicious bot traffic.

In short, a simple approach is not viable. Effective bot detection and blocking require real-time behavioral analysis of the bot traffic.

DataDome’s bot detection software helps mobile, web applications, and APIs from bad bot traffic by collecting data and analyzing the same. For each request, more than 250 events are record and this amounts to a total of 600 billion events per day in the customer database.

Extensive usage of Machine Learning is in use, and the engine detects a malicious bot pattern every 10ms. The bots acquired are classified into two types: known bots and new threats. 

Known bots are detected through technical validation and detection (HTTP fingerprinting).

New threats are real-time challenges and are identify through statistical and behavioral detection. This uses data from the server-side fingerprints, SDK inputs, and session tracking through cookies.

As soon a new bot threat is detected, the algorithm is updated synchronously and deployed almost immediately to all the data centers. This imposes a strict mechanism and protects customers from real-time threats.

Autopilot mode for Bot protection

DataDome collects and analyzes 100% of all the customer data from web servers, and uses client-side and server-side related data to keep a track of the transactions which distinguish human users from bots. As a result, our customers can run bot management on autopilot. Hence, whenever an attack is detected, the solution applies automatically without any intervention.

The software also lets an individual user fine-tune a configuration script of their bot protection policy through a powerful custom rules engine. Also, data collected from the bot traffic can be later smoothly integrated into various server logs and online analytics tools like Mixpanel and Google Analytics.

Conclusion

Bot detection is a task for professionals since today’s sophisticated bots perfectly imitate human behavior and also evade the IP blacklisting. Stronger bot detection algorithms that rely on real-time behavioral analysis are the only effective approach to safeguard your digital assets and transactions.

written by: Prem D

reviewed by: Sayan Chatterjee

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