Cloud Sandbox Exploit Detection for Zero-Day Malware Scoring
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Solution Overview
Problem
Existing cloud-based malware detection systems face challenges in providing zero-day/zero-hour protection and improving malware detection efficacy, attribution, and scoring, particularly in identifying and classifying unknown malicious content.
Innovation Solution
A cloud-based sandbox system that utilizes a dynamic rules engine to analyze unknown content, perform exploit detection by running rules on obtained events, and adjust scores based on the analysis results, incorporating static and dynamic analysis to classify content as malware or clean.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud-based malware detection systems are used, then malware detection capabilities are improved, but the ability to provide zero-day/zero-hour protection against rapidly morphing threats is insufficient
Solution Approach 1:
The system performs preliminary actions by executing unknown content in a cloud-based sandbox environment before it can cause harm. The sandbox预先 establishes a controlled execution environment where malicious behavior can be observed and analyzed proactively, enabling the system to detect zero-day threats before they spread to production systems.
Solution Approach 2:
The cloud-based sandbox acts as an intermediary between the unknown content and the production environment. It mediates the analysis process by capturing execution events, API calls, and behavioral patterns of suspicious content without exposing the actual system to potential malware, thus enabling zero-day detection while maintaining system safety.
2Measurement precision
If dynamic analysis is performed in a cloud-based sandbox, then malware detection accuracy is improved, but analysis time and processing duration increase
Solution Approach 1:
The system implements periodic action by analyzing execution events at specific intervals and stages during sandbox operation. Rather than continuously processing all data, the system periodically captures key events, API calls, and behavioral patterns at critical junctures, reducing overall analysis time while maintaining detection accuracy through strategic sampling.
Solution Approach 2:
The system applies partial action by focusing analysis on the most significant execution events and behavioral patterns rather than processing every single event generated during sandbox execution. It prioritizes capturing critical malware indicators while filtering out redundant data, achieving high detection accuracy with reduced processing overhead.
3Reliability
If exploit detection rules are run on all events, then detection thoroughness is improved, but system complexity and processing overhead increase
Solution Approach 1:
The system applies local quality by running exploit detection rules selectively on specific event types and execution stages rather than uniformly on all events. Different detection rules are applied to different event categories (e.g., API calls, file operations, network activity) based on their relevance to particular exploit patterns, reducing overall system complexity while maintaining comprehensive detection coverage.
Solution Approach 2:
The detection system is segmented into multiple specialized rule sets that handle different exploit types and event categories separately. This segmentation allows the system to process events through targeted detection pathways rather than a monolithic rule engine, reducing processing overhead while maintaining thorough detection through specialized analysis for each event type.
Data Source
AI summary
Computer-implemented systems and methods include receiving unknown content in a cloud-based sandbox; performing an analysis of the unknown content in the cloud-based sandbox; obtaining events based on the analysis; running one or more exploit detection rules on the events; and providing a score based on a result of the one or more rules. The systems and methods can include classifying the unknown content as malware or clean based on the score. The analysis can include a static analysis and a dynamic analysis, with the events generated based thereon.