Detecting and bot detection for suspicious traffic is a critical part of web bot detection, as bad bots can degrade site performance and compromise user experience. Whether it is for malicious purposes such as breaking into accounts or scanning for attack targets, or just “gray” bots that are too noisy (APIs that call your system too often or partner bots pinging your systems constantly), identifying and addressing these sources of traffic can help improve security and optimize site performance.

Most automated activity occurs over the Internet, and bots use a wide range of tools to interact with websites and carry out transactions. Some are useful, for example, search engine bots that index content or customer service bots to assist users with online interactions. Others are harmful, ranging from simple hacking scripts to sophisticated botnets used to execute attacks, distribute spam, or generate advertising revenue.

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Various approaches to web bot detection can be applied to identify these activities, including traffic analysis, IP address cross-referencing and filtering, blacklists of known bots, and user behaviour analyses such as mouse movements or clicks. More advanced bots can further evade detection by disguising their automated nature or mimicking human-like behaviours.

Traffic-based analysis is an immediate and versatile approach to detecting bots, leveraging a continuous stream of data across the course of a session, capturing all interaction traces between a device and the server. It also captures a wider set of indicative markers, spanning HTTP requests and responses, timestamps, content length, cookies, language settings, and more. However, this approach also collects personal data and is subject to privacy concerns, particularly if this data is used for profiling or tracking over time.