Network Content Scheduling via Heuristic Filtering and Virtual Machine Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting malicious network content are inefficient and scalable, often resulting in false positives and significant delays, as they require processing all network content through a virtual machine, which is computation-intensive and not suitable for large networks.
Innovation Solution
A system that uses a tap to monitor and copy network data, applying heuristics to identify suspicious content, and utilizing a virtual machine pool to process only flagged content, allowing for real-time analysis and reducing computational load by prioritizing suspicious data flows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all network content is processed through a virtual machine, then detection accuracy is improved, but computational load and processing time increase significantly
Solution Approach 1:
The system segments network content analysis into two distinct stages: a heuristic analysis stage that performs initial filtering and a virtual machine execution stage that performs deep analysis. This segmentation allows the system to process only a subset of content through the computationally intensive virtual machine, resolving the contradiction between detection accuracy and processing throughput.
Solution Approach 2:
The system performs preliminary heuristic analysis on network content before it is processed by the virtual machine. This preliminary action identifies and filters out benign content, ensuring that only potentially malicious content undergoes the resource-intensive virtual machine execution, thereby improving overall processing efficiency while maintaining detection accuracy.
2Reliability
If network content is scanned thoroughly for malware, then detection reliability is improved, but time delay increases
Solution Approach 1:
The analysis process is segmented into rapid heuristic scanning and selective virtual machine execution. The heuristic layer provides immediate results for the majority of content, while only suspected malicious content is forwarded for virtual machine analysis, reducing overall processing time while maintaining high detection reliability.
Solution Approach 2:
The system applies partial action by using lightweight heuristic analysis for the bulk of network content and reserving full virtual machine analysis for only the most suspicious content. This partial application of resources achieves sufficient detection reliability without the time penalty of exhaustive analysis on all content.
3Difficulty of detecting and measuring
If a virtual machine is used to analyze network content, then detection capability is improved, but system complexity increases
Solution Approach 1:
The system architecture is segmented into a simple heuristic analysis layer and a specialized virtual machine execution layer. This segmentation allows the complex virtual machine functionality to be isolated and applied only where necessary, rather than being the primary processing mechanism, thereby reducing overall system complexity while maintaining enhanced detection capability.
Solution Approach 2:
The system introduces an intermediary heuristic analysis layer between network content and the virtual machine. This intermediary acts as a filter and triage mechanism that prepares and selects content for virtual machine processing, simplifying the interaction between the complex virtual machine and the rest of the system while preserving detection capability.
4Reliability
If all network traffic is monitored and analyzed, then security coverage is improved, but network performance degradation increases
Solution Approach 1:
The system applies partial analysis to network traffic by using fast heuristic scanning for initial assessment and reserving deep virtual machine analysis for only the most suspicious content. This partial action approach maintains comprehensive security coverage for potential threats while minimizing the impact on overall network throughput.
Solution Approach 2:
The system performs preliminary heuristic filtering on all network traffic to identify and isolate potentially malicious content before it requires deep analysis. This preliminary action ensures that security coverage is maintained for all traffic while allowing the majority of benign traffic to pass through with minimal processing delay, preserving network performance.
Data Source
AI summary
A system is described for scheduling the processing of items of suspicious network content to determine whether these items contain malicious network content. The system features a memory and an analyzer that may comprise a processor-based digital device in which at least one virtual machine (VM) and a scheduler operates. The scheduler is configured to generate an order of processing of a plurality of items of network content by the processor based on a plurality of probability scores, each corresponding to an item of network content. The analyzer is configured to process the items of network content in at least the virtual machine by replaying these items in accordance with the order of processing. The virtual machine is configured with a software profile corresponding to each of the processed items and being adapted to monitor behavior of each of the items during processing, thereby to detect malicious network content.