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Research > Evolution of Web Application Firewall through Machine Learning

Evolution of Web Application Firewall through Machine Learning

03.05.2024

Price:$ 1,500

Evolution of Web Application Firewall through Machine Learning

Report Description:

This Quadrant Knowledge Solutions market insight provides an overview of the evolution of Web Application Firewalls through the intervention of Machine Learning. As the cyber threat landscape is changing, it calls for more sophisticated security tools, especially when it comes to the ones that protect web applications. Web Application firewalls are evolving to protect web applications, one such advancement to WAF is the use of Machine Learning algorithms which will eventually improve the detection rate, reduce false positives, and protect against zero-day attacks. It makes the work of security professionals easier by making the detection process manageable. It also offers the same level of security to both large and small applications.

The report also covers how Machine Learning has played a vital role in improving the capabilities of Web Application Firewalls.

Explore this market insight to gain an understanding of:

  • The need to implement a competitive defense solution to protect applications.
  • Learn about the existing security models and the complexities related to these models.
  • Gain insight into the role of newer security models.
  • Gain an understanding of the role played by Machine learning in identifying threats and its advancements.
  • Understanding the application of Web Application Firewall in different industries.
  • And gain an insight into the future roadmap of Web Application Firewall.

According to Riya Tomar, an Analyst at Quadrant Knowledge Solutions “Machine Learning plays a crucial role in Web Application Firewalls (WAFs) by enhancing their ability to detect and mitigate various forms of cyber threats. Through the utilization of advanced algorithms, WAFs can analyse vast amounts of web traffic data in real time, allowing them to identify patterns indicative of malicious activity such as SQL injection, cross-site scripting (XSS), and other forms of attacks. Machine Learning algorithms enable WAFs to adapt and evolve, learning from past incidents and continuously improving their threat detection capabilities. This dynamic approach empowers WAFs to stay ahead of emerging threats and provide proactive protection for web applications, safeguarding against potential vulnerabilities and breaches. "

This Market Insight is a part of Quadrant’s Information Security research.

Author: Riya Tomar (Analyst) at Quadrant Knowledge Solutions

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