Temporal: Bot or human? Detecting malicious bots with machine... - Benjamin Fabre • Antoine Vastel
Channel: OWASP Foundation
Date: 2022-01-06

Temporal: Bot or human? Detecting malicious bots with machine learning in 2021 

SPEAKERS
Benjamin Fabre • Antoine Vastel

ABSTRACT
Detecting malicious bots has become an extremely complex task. Bot developers deliberately design their software to bypass bot detection systems. They attack from perfect browsers and mobile apps, leveraging exactly the same browsers as humans or headless browsers like Headless Chrome. They know how to forge attributes that are commonly used for bot detection: they manipulate HTTP headers and their values and order, and change their browser fingerprints. Bad bots are also distributed in extremely elaborate ways. Many use residential IPs with excellent reputations, and they make very few requests per IP — sometimes only one. Finally, the best bots perfectly mimic human behavior. For example, they can imitate realistic mouse movements and keyword strokes, using generative adversarial networks.

So what does it take to efficiently distinguish advanced bots from real humans?

This talk will reveal the inner workings of a modern bot detection engine. We will see which signals are collected, and how they are enriched. We will discuss why it is mandatory to analyze both server-side and client-side signals. We will explore the challenges of authenticating good bots, and how to detect frameworks such as Puppeteer extra stealth, Playwright, Selenium and Headless Chrome. Finally, we will take a deep dive into machine learning approaches for bad bot detection, with a demonstration of how the respective strengths of supervised and unsupervised machine learning can be combined for maximum predictive accuracy.

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