Web Request Detection Using Client-Side Behavioral Baselines
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems struggle to accurately detect malicious requests and bot activity due to limited context and vulnerability to data manipulation, leading to missed detections and increased security risks.
Innovation Solution
Implementing client-side code to collect rich data from application context, using machine learning models to analyze HTML elements and API requests, and establishing behavioral baselines to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional security systems are used to detect malicious requests, then the system is simpler to implement, but the detection accuracy is low and false positives occur frequently
Solution Approach 1:
The system segments the detection process into multiple independent components: client-side data collection module, server-side analysis module, machine learning model module, and baseline comparison module. Each component handles a specific aspect of request analysis, improving overall detection accuracy while maintaining modularity that manages complexity.
Solution Approach 2:
The system adds a new dimension to request analysis by collecting and analyzing client-side contextual data (HTML elements, page structure, user interaction patterns) in addition to traditional server-side request parameters. This multi-dimensional approach enables more accurate differentiation between legitimate and malicious requests.
2Measurement precision
If more data is collected from client-side to improve detection accuracy, then the detection precision improves, but the amount of data processed increases
Solution Approach 1:
The system extracts only the most relevant features from client-side data, such as specific HTML element properties, page structure characteristics, and key request parameters. By selecting and extracting only essential features rather than processing all raw data, the system maintains high classification accuracy while reducing data volume for analysis.
Solution Approach 2:
Different levels of data collection and analysis are applied to different aspects of the request. Critical security-related features are analyzed in detail, while less important data is processed at a higher level or aggregated. This localized quality approach optimizes the balance between accuracy and data processing requirements.
Data Source
AI summary
Systems, methods, apparatuses, and computer program products for analyzing malware traffic with direct malware detonation. The method may include, collecting page data of a website, and information of a user request. The method may also include generating an algorithm and a baseline of authorized activity with the collected page data and the information of the user request. In addition, the method may include establishing legitimacy of the user request based on the algorithm and the baseline. Further, the method may include controlling access to a web application based on the legitimacy of the user request.