Application Delivery Security via Multi-Score Classification
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Solution Overview
Problem
Existing systems face challenges in efficiently and reliably detecting and preventing malicious or fraudulent applications from being delivered via an application delivery server, leading to wasted resources, cyber security issues, and delays in the review process due to the large number of applications and updates.
Innovation Solution
A data processing system that analyzes applications by determining classification scores based on historical execution, simulated actions, and comparisons with approved applications, allowing for the prevention of delivery or disabling of fraudulent actions, using classifiers to improve the efficiency and reliability of the review process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional application review processes are used, then security detection can be performed, but the review process causes delays and inefficiency due to the large number of applications
Solution Approach 1:
The system performs preliminary analysis of application metadata, permissions, and code patterns before full installation and testing. This preliminary action includes static code analysis, permission validation, and comparison with known malicious patterns, allowing the system to filter out obviously malicious applications without requiring complete review of each application.
Solution Approach 2:
The system creates virtual copies of applications in sandboxed environments for testing instead of installing on real devices. It also uses simulated user interactions and automated test scripts to replicate actual usage scenarios, enabling efficient security validation without requiring physical device deployment.
2Measurement precision
If comprehensive application testing is performed, then detection accuracy improves, but resource consumption and review time increase
Solution Approach 1:
The review process is divided into multiple stages: initial metadata analysis, static code scanning, sandboxed execution testing, and post-installation monitoring. Each stage focuses on specific detection objectives and filters applications at different levels of scrutiny, allowing the system to allocate resources efficiently while maintaining high detection accuracy.
Solution Approach 2:
Different analysis methods and scrutiny levels are applied to different parts of the application based on risk assessment. High-risk components such as permission requests, network calls, and system API interactions receive more intensive analysis, while low-risk sections undergo lighter validation, optimizing the balance between detection accuracy and review time.
3Reliability
If manual review processes are used, then detailed analysis can be performed, but the large number of applications causes processing delays
Solution Approach 1:
The system employs automated self-service mechanisms including machine learning models that automatically classify applications as safe, suspicious, or malicious based on learned patterns. Automated decision-making algorithms handle routine cases without human intervention, freeing reviewers to focus on complex edge cases while maintaining consistent and scalable analysis reliability.
Solution Approach 2:
The system introduces automated analysis tools, sandbox environments, and machine learning models as intermediaries between the application and human reviewers. These intermediaries perform preliminary filtering, risk assessment, and initial classification, reducing the burden on manual reviewers and enabling the system to handle larger volumes of applications with maintained reliability.
Data Source
AI summary
Analyzing or debugging applications is provided. The system identifies an action for an application provided by a developer. The system determines a first classification score based on historical execution of the action. The system generates a machine generated action for the application based on metadata associated with the application. The system determines a second classification score based on a comparison of the action with the machine generated action. The system selects, via a matching program, a second application that matches the application. The system determines a third classification score based on a comparison of an action approved for the second application with the action provided by the application developer. The system updates a delivery control parameter based on the first classification score, the second classification score and the third classification score. The system controls delivery of the application based on the delivery control parameter.