Quadrant Knowledge Solutions Market Insights research provides detailed information about how machine learning in DDoS solution is helping to minimize the mitigation time.
Fueled by technological advancements and other factors like the Russo-Ukraine war, DDoS attacks are becoming more complex as well as more difficult to detect and mitigate. A majority of organizations have reported facing such attacks during the last few years. In addition, the COVID-induced accelerated digital transformation initiatives across the globe are exposing organizations to newer threats such as ransomware, phishing, and whaling. To overcome the challenges, DDoS Mitigation providers are leveraging machine learning (ML) algorithms with negative and positive protection models and rate limiting to offer robust DDoS Mitigation solutions and minimize the mitigation time with minimum human involvement.
DDoS solution providers are leveraging ML to create real-time detection and mitigation capabilities against sophisticated DDoS attacks. Vendors are also leveraging ML for efficient traffic clustering, automating various processes, and developing robust anomaly detection models. Additionally, Organizations can deploy ML-based DDoS solutions to reduce sophisticated attacks, automatically stop attacks in real-time, analyze vast amounts of traffic data, and spot harmful attack patterns.
According to Sachin Birajdar, Analyst at Quadrant Knowledge Solutions, “DDoS Mitigation solution providers are integrating machine learning to detect threats, trends, reduce response time without harming genuine traffic, deliver a smooth user experience, as well as increase the accuracy, speed, and scalability of DDoS mitigation solutions. DDoS Mitigation providers can leverage ML to provide improved bandwidth capacity, effectively connect with network and data centers, set backup and duplication, configure applications and protocols for resilience, and enhance organizational efficiency and security.” Sachin advises, “Organizations looking for effective DDoS Mitigation products should look for the vendors offering robust DDoS Mitigation solutions with ML based automatic diversion, continuous support in the form of training and frequent updates, 24*7 real-time support, self-service capabilities, and secured, managed services.”
Table of Contents
Machine Learning in DDoS Mitigation
Advantages of leveraging ML in DDoS Mitigation
- Zero-Day Mitigation
- Enhanced threat detection and mitigation
- Task Automation
- Minimized Mitigation time
- Lesser Mitigation Costs
- Improvement in the overall Security
- Enhanced user experience
Types of Machine Learning algorithms
- Navies Bayes Classifier Module in DDoS Mitigation
- C4.5 Classifier Module in DDoS Mitigation
- Support Vector Machine (SVM) Module in DDoS Mitigation
- K-Nearest Neighbor (KNN) Module in DDoS Mitigation
- K-Means Clustering Module in DDoS Mitigation
- Fuzzy c-means (FCM) clustering Module in DDoS Mitigation
Final Word
This Market Insight is a part of Quadrant’s Integrated Risk Management practice.
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