Evolution of Web Application Firewall Through Machine Learning.
Quadrant Knowledge Solutions Market Insights research provides detailed insights on evolution of Web Application Firewalls (WAF).
Owing to the growing number and complexity of web-based attacks, the necessity of WAF (Web Application Firewall) security solutions is rising. A WAF protects organizations’ web assets and their customers from web-based attacks and malicious activities. Therefore, companies catering to data-based industries such as eCommerce, financial services, lead generation, digital healthcare, and more, along with organizations that follow compliance standards like PCI DSS and HIPAA, have increased their spending on vendors providing robust WAF solutions.
Traditional security appliances, like firewalls are not adequate to assess modern web applications and defend against sophisticated cyber threats. Conventional Web Application Firewalls use a negative security model that can lead to false positives and unidentified attacks. The positive security model is time-intensive and cannot secure applications against zero-day attacks. Using machine learning techniques to provide an intelligence aspect to WAFs, which can detect zero-day attacks and learn patterns of attack vectors. ML can also support creating complex WAF configurations and log analysis.
According to Riya Tomar, Analyst at Quadrant Knowledge Solutions “A machine learning based WAF offers more benefits when compared to traditional methods of protecting applications against malicious attacks. However, the traditional methods have been in use for a while and have been successful enough in identifying and blocking threats. Therefore, we can suggest that a WAF can be deployed with a layered approach to protection by using both traditional and machine learning (positive model) methods and allow the WAF to utilize the best features of both models (positive and negative). This approach can provide an extra layer of protection, bring down false positive rates, block malicious attacks, and gain an improved threat detection rate.”
Table of Contents
- Need for ML-based WAF.
- Overview of negative security model.
- Implementing a Positive Security Model and Machine Learning with a WAF.
- Advancement in Machine Learning.
- Application of WAF in different industries.
- Conclusion.
This Market Insights is a part of Quadrant’s Web Application Firewall, Report.
https://quadrant-solutions.com/market_research/spark-matrix-web-application-firewall-waf-q4-2022
Author: Riya Tomar Analyst, Renu Bala Associate Research Director, Ayush Patidar Analyst , Quadrant Knowledge Solutions.
