The discussion in this book has been relatively abstract to this point. With new technologies, such as AI and ML, it can be difficult to picture how these tools will improve the efficiency and workflow of your security team. More important, it can be difficult to understand how using these tools can help you save money or at least get a better return on your investment. This chapter presents one case study that focuses on the use of AI and ML to detect and mitigate a sophisticated bot attack. This type of attack is a common problem and is an especially good fit for AI and ML technologies, as well as being easy to implement.
Case Study: Global Media Company Fights Scraping Bots
To better understand how AI and ML can thwart malicious bots, this case study discusses how a global media company with a large marketing presence that includes more than 50,000 websites and which runs more than 20,000 pay-per-click campaigns (Figures 7-1 and 7-2) at any given time took preventive action when experiencing a high volume of sophisticated scraping bots.
Figure 7-1. Typical traffic from the customer (number of HTTPS Requests per 5 minutes, over 24 hours). Note that the figure also shows blocked attack traffic (traffic identified by the WAF, Access Control, and Bot Manager are blocked) and caching performance (in green)
Figure 7-2. Typical traffic from the ...
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