Source URL: https://blog.cloudflare.com/protecting-apis-from-abuse-using-sequence-learning-and-variable-order-markov
Source: The Cloudflare Blog
Title: Protecting APIs from abuse using sequence learning and variable order Markov chains
Feedly Summary: At Cloudflare, we protect customer APIs from abuse. This is no easy task, as abusive traffic can take different forms, from giant DDoS attacks to low-and-slow credential stuffing campaigns. We now address this challenge in a new way: by looking outside typical volumetric measures and using statistical machine learning to find important API client request sequences.
AI Summary and Description: Yes
Summary: The text discusses API security, focusing on the concept of “sequential abuse” in API requests and how statistical machine learning techniques, specifically variable order Markov chains (VOMCs), are employed to identify and mitigate such threats efficiently. This is particularly relevant for professionals in AI, cloud computing, and information security as it addresses the growing sophistication of API attacks and the need for advanced detection mechanisms.
Detailed Description:
The blog post provides an in-depth exploration of the challenges associated with securing APIs against malicious activities that involve sequential request behaviors. Here are the key points:
– **Understanding Sequential Abuse:**
– “Sequential abuse” refers to the malicious behavior characterized by the specific order in which API requests are initiated, potentially leading to data exfiltration or abuse.
– Traditional detection methods focused on volume alone are insufficient, as malicious actors may send requests slowly to avoid detection.
– **Session Data and Behavior Modeling:**
– API sessions are defined as time-ordered sequences of HTTP requests initiated by users (e.g., authentication, checking account balance, transferring funds).
– An example illustrates the sequential nature of these requests in a banking service.
– **Statistical Machine Learning Techniques:**
– The blog describes how these techniques are being utilized to distinguish between benign and malicious API request sequences.
– Markov chains are discussed as a method to model the probabilities of endpoint requests based on previous requests. However, limitations exist in relying solely on maximum likelihood estimation.
– **Variable Order Markov Chains (VOMCs):**
– VOMCs offer a more flexible approach by allowing the context length of the preceding endpoints to vary, thus providing a more accurate modeling of API request behavior.
– The process of collapsing redundant contexts helps simplify the model while retaining statistically significant data.
– **Practical Application and Continuous Learning:**
– The implementation includes a scalable algorithm that updates continuously as data arrives, ensuring that the model remains effective without excessive CPU load.
– The technique uses a sliding window approach for sequence learning, enabling timely insights into important behavioral patterns.
– **Next Steps for Customers:**
– The blog concludes by highlighting the availability of Sequence Analytics and Sequence Mitigation tools for API Gateway users, promoting a proactive approach to API security.
This detailed methodology provides a framework for enhancing API security through advanced analytics and offers significant implications for organizations relying on APIs for critical services. Security professionals can draw from this analysis to implement similar strategies within their environments, effectively countering emerging threats in API interactions.