Browser Isolation Sandboxing for Immediate Secure Content Access
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Solution Overview
Problem
Existing security systems face a trade-off between minimizing waiting time, risk, and cost in file scanning, with machine learning techniques often failing to provide high precision in malware detection, leading to potential false positives or negatives that impact user experience.
Innovation Solution
Implementing a cloud-based sandboxing system with browser isolation that uses a trained machine learning ensemble model to quickly classify content as malicious or benign, allowing immediate access during processing, and employing prudent ensemble models to filter out unreliable predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all files are scanned by sandbox to minimize risk, then malware detection precision is improved, but waiting time and processing cost increase significantly
Solution Approach 1:
The patent segments the file analysis process into two independent parallel paths: (1) immediate static analysis providing quick preliminary results, and (2) deferred dynamic sandbox analysis for comprehensive verification. This segmentation allows the system to provide rapid initial responses while maintaining thorough security checking, resolving the contradiction between speed and precision.
Solution Approach 2:
The system performs preliminary static analysis actions immediately upon file submission, including header inspection, signature matching, and basic heuristic evaluation. These preliminary actions provide early risk assessment and enable immediate user feedback, while the more time-consuming dynamic sandbox analysis proceeds in the background to confirm or refine the initial assessment.
2Reliability
If all files are scanned by sandbox to minimize risk, then malware detection precision is improved, but processing cost increases
Solution Approach 1:
The patent segments the file analysis population into different risk categories based on static analysis results. Low-risk files (clean signatures, benign headers) are routed to expedited processing with minimal sandbox overhead, while high-risk files receive comprehensive sandbox analysis. This segmentation optimizes resource allocation and reduces overall processing costs while maintaining high detection precision for suspicious files.
Solution Approach 2:
The system applies different levels of analysis quality to different files based on their risk profile. Benign files receive basic static analysis with minimal sandbox engagement, while suspicious files receive intensive multi-layered analysis including dynamic execution, behavioral monitoring, and deep heuristic scanning. This local quality adjustment ensures high precision where needed while reducing costs for low-risk files.
3Loss of time
If machine learning model is used to quickly classify content, then waiting time is reduced, but measurement precision deteriorates due to false positives or negatives
Solution Approach 1:
The patent merges multiple classification approaches into a unified system: machine learning models provide rapid preliminary classification, while traditional signature-based detection, heuristic analysis, and dynamic sandbox behavior monitoring provide verification and correction. The combined system leverages the speed of ML for initial sorting while using more precise but slower methods to verify and correct ML predictions, thereby reducing both waiting time and false positives.
Solution Approach 2:
The system implements feedback loops where sandbox analysis results are used to refine and retrain machine learning models continuously. Misclassified files trigger model retraining with corrected labels, and sandbox behavioral data feeds back into feature engineering for improved ML accuracy. This feedback mechanism progressively improves classification precision while maintaining the speed benefits of ML-based preliminary sorting.
Data Source
AI summary
Systems and methods include responsive to a user attempting to access content in a cloud-based system, obtaining the content associated with the user; sending the content to a sandbox for processing; rendering the content within an isolated browser, thereby allowing the user to interact with the content during the processing; and receiving a verdict from the sandbox, wherein the verdict labels the content as one of malicious, benign, and unknown, and performing an action based thereon.