Protecting against Web Application Attacks
In this digital era, companies have more Internet-based applications than ever before, and these applications face exceptional volume and various types of threats. It’s more important that the WAFs (Web Application Firewall) provide superior ways to detect and block those threats.
For the past few years, the ability to consistently block threats has remained a tough challenge for WAFs. Typically, security teams need to spend time and effort continually in altering rules and signatures to manage false positives. On the other hand, many WAFs have been deployed to a listen-only method, which detects potential threats and these threats have to monitor and solved independently by staffs. These two approaches depend on the human effort, so it’s not scalable as like automated solutions. This approach is difficult even if organizations had only a few public applications, but it became impossible if virtually all applications were public. In order to overcome this, the application security solutions have to be capable of taking real-time action, while significantly reducing the number of false positives and false negatives in the industry.
Most applications are developed using open source code and frameworks in the agile application development period. This apparently allows developers to avoid reinventing the wheel with each application and leads to faster and easy application development. The drawback of this system is, when a new vulnerability is discovered, the problem can be applied to every application using that code, exposing millions of apps on the Internet instantly. High-profile vulnerabilities in Apache Struts or the infamous Heartbleed are just a few examples, but every day there are new vulnerabilities.
If the existence of AppSec was a menace by tuning signatures and rules, the application monitoring could help to save a lot of time by applying machine learning. This approach offers great value. By making use of ML and AI models, the normal behavior and use patterns of applications can be recognized when these patterns are different from established standards. Alongside, one other benefit is there is no need a human presence for learning even on a large scale.
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