Cloud Forensic Worker Orchestration for Scalable Artifact Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing complexity and size of computer networks make forensic analysis after security incidents burdensome and computationally intensive, particularly when dealing with large amounts of computing devices, as existing methods are inefficient in collecting and processing forensic artifacts.
Innovation Solution
A scalable, cloud-based computer architecture that collects forensic evidence, processes it using automated and parallel processing techniques, and provides a contextual user-assisted workflow to guide users through the analysis, dynamically generating workers to handle tasks and terminating them to reduce processing burdens on cloud resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional forensic analysis methods are used on complex networks, then comprehensive artifact collection is achieved, but the process becomes extremely time-consuming and computationally intensive
Solution Approach 1:
The patent segments the forensic analysis process into discrete tasks that are stored in a task queue. Each task represents a specific analysis operation that can be independently executed. This segmentation allows the system to process forensic artifacts in manageable units rather than as a monolithic process, enabling parallel execution and reducing overall analysis time while maintaining comprehensive coverage.
Solution Approach 2:
The patent introduces a temporal dimension to the forensic analysis process by implementing phased execution. The system collects artifacts first, then processes them in subsequent phases using multiple workers. This dimensional change from sequential to phased parallel processing allows comprehensive artifact collection to be maintained while dramatically reducing the time required for analysis through concurrent task execution.
2Productivity
If more computational resources are allocated to process forensic tasks, then analysis speed increases, but cloud resource processing burden increases
Solution Approach 1:
The patent implements dynamic worker generation and termination based on task queue conditions. Workers are generated when tasks are available and terminated when the queue is empty, allowing the system to adapt computational resource allocation to actual workload demands. This dynamic approach maximizes analysis speed when needed while minimizing resource burden during idle periods, creating an elastic resource utilization model.
Solution Approach 2:
The patent discards worker processes after task completion and recovers cloud resources for future use. Rather than maintaining persistent workers that consume resources continuously, the system spawns workers only when necessary, executes tasks, then terminates them to free resources. This approach maintains high productivity during active analysis while reducing the processing burden on cloud infrastructure during idle periods.
3Ease of operation
If manual forensic analysis processes are used, then detailed investigation is possible, but the complexity of managing multiple devices and artifacts increases operator burden
Solution Approach 1:
The patent implements self-service automation where the system automatically generates tasks, manages worker processes, queues jobs, and coordinates analysis operations without manual intervention. The automated worker orchestration system handles the complexity of managing multiple devices and artifacts by autonomously generating appropriate tasks based on collected evidence and executing them through managed workers, significantly reducing operator burden while maintaining investigative thoroughness.
Data Source
AI summary
Disclosed are techniques for performing forensic analysis of computer systems in a cloud network. The techniques can include using a scalable, cloud-based, specialized computer architecture for performing the forensic analysis of computer systems.