Web Traffic Interception for Session Reconstruction
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Solution Overview
Problem
Current data leakage detection systems face challenges in scalability, privacy preservation, accuracy, and timeliness, particularly in handling large volumes of data and detecting accidental data leakages caused by improper data retrieval criteria, known as programmatically introduced accidental data leakages (PIADL).
Innovation Solution
A system intercepts and de-duplicates web traffic data, clusters it based on data types, applies predefined rules to identify unauthorized data transmissions, uses supervised learning to classify false alarms, and employs the Kafka framework for real-time data processing, thereby enhancing scalability, accuracy, and timeliness of data leakage detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system processes large volumes of web traffic data without de-duplication, then comprehensive data leakage detection is achieved, but network strain and processing time increase significantly
Solution Approach 1:
The system segments web traffic data processing into distinct phases: interception, de-duplication, clustering, and analysis. By dividing the data stream into manageable segments and processing them through specialized components (web interceptor, clustering engine, leakage detection engine), the system maintains detection accuracy while improving processing efficiency through parallelization and optimized resource utilization.
Solution Approach 2:
The system creates de-duplicated copies of web traffic data, storing only unique data elements and their references. Instead of processing redundant duplicate data multiple times, the system maintains a copy of the original data and references to it, significantly reducing processing load while preserving the ability to detect data leakage through the cloned session data.
2Measurement precision
If the system stores and processes all web traffic data in detail, then accurate leakage detection is achieved, but memory burden and processing overhead increase
Solution Approach 1:
The system extracts and removes duplicate data elements from the web traffic stream, keeping only unique data and their references. The de-duplication process takes out redundant information while preserving the essential data needed for leakage detection, significantly reducing the quantity of data to be processed while maintaining detection accuracy through reference-based reconstruction.
Solution Approach 2:
The system transitions from storing complete data copies to storing data references and metadata. By changing the dimension of data representation from full content to reference pointers, the system reduces storage requirements and processing overhead while maintaining the ability to access and analyze data when needed for leakage detection.
3Loss of time
If the system processes web traffic data in real-time without optimization, then timely leakage detection is achieved, but network bottlenecks and system performance degradation occur
Solution Approach 1:
The system performs de-duplication and clustering operations in advance before the actual leakage detection analysis. By pre-processing the web traffic data to remove duplicates and organize it into clusters, the system reduces the computational burden during real-time detection, enabling timely responses without overwhelming system resources during critical analysis phases.
Data Source
AI summary
A system for generating user session objects is disclosed. The system intercepts web traffic data and extracts dynamic content items from the web traffic data such that the dynamic content items are separated from static content items. The system further groups the dynamic content items based on their associated user session IDs. The system then links together the dynamic content items of a group in sequence to generate a user session object, wherein the dynamic content items include one or more static content reference IDs associated with the static content items.