Context-Aware Malware Repair Module Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing security software uses a lowest common denominator approach to repair malware infections, which often results in suboptimal repairs due to lack of context information utilization, failing to restore the computer to its original state effectively.
Innovation Solution
A security module that interfaces with repair modules to leverage context information, generating infection and repair objects with scores for thoroughness and complexity, allowing for selective and optimized repair strategies ranging from fine-grained to coarse-grained repairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If security software uses a lowest common denominator approach to repair malware infections, then the repairs can be applied across all computers, but the repairs are often suboptimal and fail to restore the computer to its original state
Solution Approach 1:
The patent applies local quality by transitioning from a uniform repair approach to context-aware repairs. The system analyzes specific context information for each infected computer (such as backup availability, system configuration, infection type) and selects repair actions tailored to that local context. This allows each computer to receive the most appropriate repair strategy for its specific situation rather than a one-size-fits-all approach.
Solution Approach 2:
The patent implements dynamics by making the repair selection process adaptive and flexible. The system dynamically evaluates multiple potential repair actions, scores them based on context information, and selects the optimal repair strategy. This dynamic approach allows the security software to adapt to different infection scenarios and context conditions, improving repair effectiveness while maintaining broad applicability.
2Reliability
If security software collects and analyzes context information for each computer, then repair quality improves, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex repair decision-making process into modular components. The system collects various types of context information separately (backup status, system configuration, infection details), evaluates each independently, and combines them to form an overall repair strategy. This modular approach manages complexity by breaking down the problem into manageable segments that can be processed and evaluated systematically.
Solution Approach 2:
The patent uses an intermediary approach by introducing a context analysis layer between malware detection and repair execution. This intermediary component collects, processes, and evaluates context information, then uses this analysis to guide repair selection. The intermediary manages the complexity by serving as a buffer that translates raw context data into actionable repair decisions, simplifying the overall system architecture.
3Ease of operation
If security software presents multiple repair options to users, then users can choose the most appropriate repair, but user confusion increases due to lack of context understanding
Solution Approach 1:
The patent implements feedback by using context information to evaluate and score multiple potential repair actions, then presenting the highest-scoring options to the user. The system provides feedback to the user about why certain repairs are recommended based on the analyzed context, helping users understand the rationale behind each option. This feedback mechanism reduces user confusion by explaining the connection between system state and repair recommendations.
Solution Approach 2:
The patent applies partial action by presenting a limited subset of the most relevant repair options to users rather than all possible repairs. The system evaluates multiple potential repairs, scores them based on context information, and presents only the top-scoring options that are most likely to be appropriate. This partial presentation approach reduces user interface complexity while still providing meaningful choice.
Data Source
AI summary
A security module interfaces with a set of infection repair modules. Each repair module can perform a specific repair of an infection. Some of the repair modules utilize context information about the computer, such as the availability of a non-infected backup file. Further, the repairs performed by some repair modules are fine-grained while repairs of other repair modules are coarse-grained. The security module identifies malicious software infecting a computer and generates an infection object for each infection. The security module selectively routes the infection objects to the repair modules. In response, the repair modules provide repair objects representing repairs that the repair modules can perform on the infection. The repair objects have scores describing their thoroughness and complexity. The security module ranks the repair objects based on their scores and selects certain repairs for repairing the malicious software infections.