Bot Detection: An Interesting Overview (2021)

Introduction

Bot detection is a key security priority for all online businesses as malicious bots today are 1 in 3 of the world’s web traffic and are responsible for various security threats and monetary losses. But, detecting bot traffic is hard, as the bot developers find new ways to overcome standard firewalls and bot detection security solutions. 

Different ways of bot detection

Recent bots use AI and are indistinct from human users and impossible to detect without proper bot detection technology. Bot malware detection distinguishing malicious bots from human users is thus a complex task. The evolution of bots helps to understand how fraudsters use how to avoid bot detection techniques on IP-centric bot detector solutions.

IP-centric Solutions

The Gen-1 bots were simple in-house script crawlers performing automated tasks like web-scraping and had no session cookie allowing their discovery as bots. Gen-2 bots like Scrapy and Nutch lacked JavaScript firing, making them easy to identify. Gen-3 bots changed dramatically and appeared as browsers like CasperJS and PhantomJS, which make slow and low volume attacks and are able to overcome volume-based thresholding, needing a fingerprint or challenge test for identification. Gen-4 bots mimic non-linear mouse movements making them appear like human behaviour or hide within the user session and work on browsers like Chrome Headless and/or instrumentation frameworks like Puppeteer and Playwright. Thus the bot detection meaning makes all interfaces vulnerable. 

Traditional WAF software relies on IP reputation to manage bots assuming if an IP address shows any malicious activity, all its activity is malicious. Thus data centre proxies and website owners could use open source bot detection by blocking the IP address of the data centre proxy (available from the US’ ARIN or the EU’s RIPE services). Bot hijackers also avoid bot network detection by making use of sophisticated services like the world-wide Luminati proxy service, use residential IPs of reputed addresses and hijack vulnerable less-secure IoT bot traffic detection devices like laptops, mobile phones etc., through malicious apps. 

Behaviour-based bot detection

Known bots are found using validation and technical detection using HTTP fingerprinting (known AI/developer rule pattern-matching) and authentication as a good-bot.

New threats can be identified via behavioural and statistical detection, using a JS rendering engine, data fingerprints from server-side, session tracking and SDK inputs. The bot detection algorithm is instantly updated and deployed to all data centres providing real-time protection.

Server-side and client-side bot detection and protection

Since malicious bots forge digital signatures and fingerprints, solutions to bot detection like DataDome, use the same browsers as human users like Firefox, Chrome, Safari etc. for the integration of server and client-side, wherein they collect fingerprints, HTTP requests on the server-side to analyze each request in real-time and use client-side module records to detect and analyze a variety of features like device, browser, app, touch-screen and mouse movements through its detection engine.

Auto-pilot Bot protection 

A huge advantage is that the bot management happens in the autopilot mode needing no intervention and allowing the IT teams to fine-tune the custom-rules engine via configuration of bot protection and seamlessly integrate information into SIEM/SOC tools, server logs, analytics/ marketing tools on Mixpanel, Google Analytics, Optimizely, VWO etc.

Conclusion

In conclusion, bot detection is a specialized task, as modern bots are sophisticated and imitate human behaviour, fingerprints and signatures, making the IP-based blocking solutions obsolete. Using behaviour-based detection, and integrating server and client-side signals, means that the Bot detection algorithms can process a 100% of the HTTP requests in real-time, to detect, block and cause updating of bot protection information. Using RTBA-real-time behavioural analysis thus helps and protects the client from account takeover, DDoS attacks, web scraping, credential stuffing, sifting of spam emails and more.

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