Automated Bots Protection (Advanced Bots Protection) offers comprehensive protection of web applications, mobile apps and APIs from automated threats like bots by providing precise bot management across all channels, combining behavioral modeling for granular intent analysis, collective bot intelligence and fingerprinting of browsers, devices and machines.
Organizations rely on robotic process automation, essentially the use of bots, to be more efficient and boost productivity. Good bots, like those used to crawl websites for web indexing, content aggregation and market intelligence, free human resources to focus on other responsibilities. Of concern are the bad bots deployed by bad actors to disrupt network services, steal data, perform fraudulent activities and even spread fake news.
Bots have evolved significantly since their origins as simple scripting tools that used command-line interfaces. Bot developers now use JavaScript and HTML5 web technologies to enable bots to leverage full-fledged browsers. The bots are programmed to mimic human behavior when interacting with a website or app to move the mouse, tap and swipe on mobile devices and generally try to simulate real visitors in order to evade security systems.
At the moment we can divide bots into four types:
Modern Bot Protection solutions face a dual challenge: to identify attacker bots which are increasingly sophisticated at emulating human users and to distinguish malicious bots from legitimate bots, which can be very important for an organization’s day to day operations.
Currently, three main approaches are used to detect and manage bots:
uses static analysis tools to identify header information and web requests known to correlate with bad bots. This technique is passive and can only detect known and active bots.
uses active challenges or tests that are difficult or impossible for bots to perform to identify bots. Commonly used challenges include CAPTCHA verification, the ability to run JavaScript, of the acceptance of cookies.
evaluates the activity of potential users and matches that activity against known patterns to verify user identity. This technique uses several profiles to classify activity and distinguish between human users, good bots, and bad bots.
The advantage of having a solution that is able to detect even the most sophisticated type of bots is that it allows an organization to protect their infrastructure against the most common type of automated bot attacks:
Account takeover (ATO) is an attack in which criminals take unauthorized ownership of online accounts using stolen usernames and passwords
In carding attacks, criminals use bots to test lists of recently stolen credit or debit card information on merchant sites
Skewed analytics are the result of activity and interaction data of your web traffic that are erroneous due to a high volume of non-human traffic.
Web scraping is a process where bots crawl websites to continuously capture pricing data and product descriptions at scale.
In denial of inventory attacks, bad actors use malicious hoarder bots to an item thousands of times to a shopping cart over the course of a few days until the item’s inventory is depleted