CDN Manager Detecting Dynamic Content Attacks via Image Analysis
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Solution Overview
Problem
Content delivery networks (CDNs) are vulnerable to cache poisoning, dynamic content attacks, and illegal insertion of tracking pixels, which can lead to security breaches and denial-of-service scenarios, compromising user data and service availability.
Innovation Solution
A CDN manager system that employs machine learning algorithms, such as convolutional neural networks, to analyze HTTP requests, hash web page codes, and distribute them via a distributed ledger, simulating user device interactions to differentiate between legitimate and malicious requests, and detect unauthorized code insertions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If CDNs cache web content on distributed servers to reduce latency and bandwidth use, then content delivery efficiency is improved, but the system becomes vulnerable to cache poisoning attacks where malicious content can be inserted into the cache
Solution Approach 1:
The patent introduces an intermediary verification mechanism that acts as a mediator between the CDN cache and the content delivery process. This intermediary validates content integrity through cryptographic hashing and verification protocols, preventing malicious content from being propagated while maintaining the speed benefits of caching. The intermediary layer checks content signatures and hashes before allowing cached content to be served, thus resolving the contradiction between fast delivery and content integrity.
2Reliability
If CDNs redirect dynamic content requests to the origin server, then content freshness is maintained, but the origin server becomes overwhelmed by attack traffic causing denial-of-service
Solution Approach 1:
The patent applies local quality by enabling CDN edge servers to perform local validation of dynamic content requests using cryptographic verification mechanisms. Instead of blindly redirecting all dynamic requests to the origin server, edge servers can locally verify request legitimacy through content signatures and hashing. This allows legitimate dynamic content requests to be served from the edge while filtering out malicious traffic, thus maintaining content freshness without overwhelming the origin server.
3Reliability
If rate limiting is applied to dynamic requests to protect the origin server, then server overload is prevented, but legitimate users are incorrectly blocked along with attackers
Solution Approach 1:
The patent replaces the mechanical rate-limiting system with a cryptographic verification system. Instead of simply counting and limiting requests based on thresholds, the new system uses content-based verification through hashing and digital signatures to identify and filter malicious requests. This substitution allows for more precise differentiation between legitimate and malicious traffic, protecting the server without incorrectly blocking legitimate users who may exceed arbitrary rate limits.
4Loss of information
If tracking pixels are inserted into web pages for analytics, then user behavior data is collected, but attackers can exploit these pixels to steal user data including device information and IP addresses
Solution Approach 1:
The patent implements a copying mechanism where a cryptographic hash or signature copy of the legitimate tracking pixel is created and verified. Instead of relying solely on the original tracking pixel code, the system creates a verified copy or validation layer that authenticates the pixel's legitimacy. This allows analytics data collection to continue while blocking unauthorized or malicious pixel insertions, as the verification mechanism can distinguish between legitimate analytics code and attacker-inserted malicious pixels.
Data Source
AI summary
Security mechanisms for content delivery networks (“CDNs”) are disclosed herein. One security mechanism can be used to mitigate or prevent dynamic content attacks. A system can execute a CDN manager to perform operations. In particular, the CDN manager can receive a plurality of hypertext transfer protocol (“HTTP”) requests, and parse a plurality of headers from the plurality of HTTP requests to determine a plurality uniform resource locators (“URLs”). The CDN manager can generate a plurality of web page images associated with the plurality of URLs. The CDN manager can execute a machine learning algorithm, such as a convolution neural network, to perform an analysis of the plurality of web page images. Based upon the analysis of the plurality of web page images, the CDN manager can determine whether the plurality of HTTP requests are for the same web page, which can be indicative of a dynamic content attack.