Web Client Storage Anomaly Detection via Interception
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Solution Overview
Problem
The increasing size limits of Web Storage containers in modern browsers pose a threat of malicious data storage, leading to instability and denial of service attacks, as existing mechanisms struggle to manage and monitor storage allocation requests effectively across multiple origins and subdomains.
Innovation Solution
A mechanism for anomaly detection on a web client that monitors and manages Web Storage allocation requests using a monitoring module, which intercepts storage instructions, checks against a database or remote service for legitimacy, and enforces storage policies to prevent malicious activity, employing intrusion detection algorithms and machine learning for intelligent decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the allowable size of Web Storage containers is increased, then more data can be stored for legitimate applications, but the system becomes vulnerable to malicious data storage attacks and denial of service
Solution Approach 1:
The system performs preliminary analysis of storage requests before allowing data to be written to Web Storage containers. A monitoring module intercepts and analyzes each storage request using intrusion detection algorithms and machine learning models to determine legitimacy before the storage operation proceeds, preventing malicious data from being stored while allowing legitimate large-scale storage operations.
Solution Approach 2:
An intermediary monitoring module is introduced between the web page and the Web Storage container. This module acts as a gatekeeper that receives storage requests, analyzes them using security algorithms, and only permits legitimate requests to proceed to the storage container, thereby protecting against malicious attacks while enabling legitimate storage needs.
2Reliability
If a monitoring module is implemented to detect malicious storage requests, then security against attacks is improved, but the complexity of the web client system increases
Solution Approach 1:
The monitoring module utilizes machine learning models and intrusion detection algorithms that automatically analyze storage requests without requiring manual intervention or complex configuration. The system self-adjusts and learns from patterns in the data, reducing the operational complexity despite the added security functionality.
Solution Approach 2:
The monitoring module is designed to handle multiple types of storage requests from various origins and subdomains using a unified analysis framework. By implementing a universal monitoring mechanism that can detect and respond to different attack patterns through machine learning, the system achieves comprehensive security without proportionally increasing complexity.
3Measurement precision
If storage requests from multiple origins and subdomains are monitored, then detection accuracy improves, but the processing time and system resources increase
Solution Approach 1:
The monitoring module employs machine learning algorithms that can quickly evaluate storage requests by focusing on the most critical anomaly indicators. Rather than performing exhaustive analysis on every single parameter of every request, the system uses trained models to identify key suspicious patterns, achieving high detection accuracy with reduced processing overhead.
Solution Approach 2:
The system dynamically adjusts monitoring parameters and analysis depth based on the characteristics of storage requests. For requests from trusted origins or with typical patterns, the system uses faster verification methods. For suspicious or unfamiliar requests, more comprehensive analysis is performed, optimizing the balance between detection accuracy and processing time.
Data Source
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AI summary
Embodiments for anomaly detection on a web client are generally described herein. A processor on the web client is monitored, where a web browser is installed on the web client, with the web browser arranged to render a web page, the web page including content originating from a plurality of origins. A request from the web page to store data on the web client is intercepted, with the request originating from a particular origin of the plurality of origins. The request is analyzed and approved or denied based on the particular origin.