Securing Apps and APIs in 2023:
See Wallarm Demo for CISOs and Practitioners!
Close
Privacy settings
We use cookies and similar technologies that are necessary to run the website. Additional cookies are only used with your consent. You can consent to our use of cookies by clicking on Agree. For more information on which data is collected and how it is shared with our partners please read our privacy and cookie policy: Cookie policy, Privacy policy
We use cookies to access, analyse and store information such as the characteristics of your device as well as certain personal data (IP addresses, navigation usage, geolocation data or unique identifiers). The processing of your data serves various purposes: Analytics cookies allow us to analyse our performance to offer you a better online experience and evaluate the efficiency of our campaigns. Personalisation cookies give you access to a customised experience of our website with usage-based offers and support. Finally, Advertising cookies are placed by third-party companies processing your data to create audiences lists to deliver targeted ads on social media and the internet. You may freely give, refuse or withdraw your consent at any time using the link provided at the bottom of each page.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Whitepaper

Wallarm AI Engine: How It Works

The main task of the run-time application security is to protect modern applications and APIs. In this endeavor the solutions face a number of challenges:

  • Applications are different both in structure and in content. Things that are harmful to one application may be perfectly normal for another.
  • User behavior varies between both the applications and the individual application functions. For example several login calls every second may indicate a credential stuffing attack, while several data layer queries per second may be a normal function of building a correlated data set.
  • The number of known attacks keeps growing, with attack patterns (or signatures) sometimes being hidden within nested protocols
  • Straightforward implementation of attack detection based on signatures often results in a high rate of false positives and false negatives.

Download this whitepaper to learn how Wallarm solves the difficult task of effective application security by relying on AI and machine learning including a unique combination of hierarchical clusterization, statistical n-gram based models, recurrent neural networks and reinforcement learning.

panasonic logo
miro logo
rappi logo
semrush logo
tipalti logo
wargaming logo
gannett logo
acronis logo
uz leuven logo
workforce logo
sunquest logo
omio logo
RESOURCES
Ready to protect your APIs?

Sign up for free. Get started in minutes.