Source URL: https://blog.cloudflare.com/training-a-million-models-per-day-to-save-customers-of-all-sizes-from-ddos
Source: The Cloudflare Blog
Title: Training a million models per day to save customers of all sizes from DDoS attacks
Feedly Summary: In this post we will describe how we use anomaly detection to watch for novel DDoS attacks. We’ll provide an overview of how we build models which flag unusual traffic and keep our customers safe.
AI Summary and Description: Yes
Summary: This text discusses advanced techniques for detecting and mitigating DDoS attacks through an innovative anomaly detection pipeline that leverages statistical modeling and machine learning. This is highly relevant for professionals in the fields of cloud security and information security, as it addresses the challenges associated with evolving threats in a landscape of increasing cybersecurity risks.
Detailed Description: The content elaborates on Cloudflare’s approach to providing continuous DDoS protection, emphasizing the importance of anomaly detection in identifying novel threats that traditional methods may overlook. Below are the main points of this approach:
– **Always-On DDoS Protection**:
– Protection operates continuously across Cloudflare’s global network, analyzing incoming traffic in real-time to identify patterns associated with past DDoS attacks.
– Dynamic fingerprinting helps flag malicious traffic, preventing it from reaching customer websites.
– **Challenges with Traditional Detection**:
– Detection relies heavily on identifying known threat patterns, making it challenging to spot novel threats, which often require human analysis.
– DDoS detection is made more complex due to the sheer volume of traffic (over 60 million HTTP requests per second) and the varying loads that legitimate sites experience.
– **Volumetric Models**:
– DDoS attacks are characterized by abnormal traffic volumes. A naive model based on z-scores can effectively flag these when traffic is consistent.
– However, actual traffic patterns are rarely stable, leading to issues in detecting smaller attacks during off-peak hours when server capacities are lower.
– **Time Series Forecasting Limitations**:
– Traditional time series models like SARIMA face challenges due to the requirement for extensive historical datasets and the cost of frequent retraining, underscoring the impracticality of using them for real-time attack detection.
– **Multidimensional Analysis**:
– Proposed a holistic method utilizing various traffic characteristics (e.g., user browser distribution) that should remain consistent during normal conditions.
– The approach expands upon z-scores in multidimensional space using Principal Component Analysis (PCA) to manage correlations and normalizations, enabling robust anomaly detection.
– **Training and Scalability**:
– Cloudflare employs an automated system that trains approximately 1 million models daily using Apache Airflow and Kubernetes, ensuring that the model adapts to changing traffic patterns and remains effective over time.
– Instead of training models for each customer, a representative sample is used to develop general insights that can enhance protection for all customers.
– **Practical Implications**:
– The methodology detailed outlines a robust framework for cybersecurity professionals to consider for improved DDoS mitigation strategies.
– Key insights include the significance of continuous model training, allowance for traffic variability, and the importance of an adaptable detection system in a complex threat environment.
This text serves as a practical guide for security professionals to understand evolving threats and the importance of advanced statistical methods in safeguarding digital infrastructure.