Picking the bad bots out of the crowdIt’s quite easy to distinguish a bot from a human if you have the right skills, experience and technology. But distinguishing a bad bot from a good one is much more complex due to bad bots using different camouflage methods.
The GlobalDots 2019 Bad Bot Report has found 523 different types of bot disguise. Most of the bad bots (55.4%) pretend to be Google Chrome, with Firefox being second, and the Android mobile browser coming in third. The list also includes Safari, Internet Explorer, Safari Mobile, Opera mobile browser, Googlebot and Bingbot crawlers, and many others.
The process of bot identification and segregation is also tricky because almost 74% of bad bots are advanced persistent bots (APBs) - complex bots that use a mixture of technologies and attack methods. They are the hardest to detect because they come from different subnetworks, change IP addresses randomly, and hide behind anonymous proxy servers, Java scripts and peer-to-peer networks. These sophisticated bots can automatically search for necessary information and vulnerabilities and often mimic human behavior successfully.
In addition, botnet managers usually have access to settings and can modify the environment to launch bot attacks. Therefore, it is extremely ineffective to identify bots only by request logs monitoring, as some antiviruses do.
At Variti, we believe it is important to compare data with other information, including IP address, statistics, technical metrics, behavior patterns, and many other factors. For example, we pay attention to the peculiarities of code execution and various browser extensions since bots do not work exactly the same as real browsers.
In general, to identify an advanced and sophisticated bad bot, a combination of technical and behavioral analysis, statistics and reputation data is needed... and of course a little bit of magic.