Malware Detection System Using Dynamic Node Scheduling
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Solution Overview
Problem
Existing malware detection systems face challenges in efficiently and effectively identifying malware, particularly in complex environments where sophisticated malware can evade detection, leading to vulnerabilities in systems that require rapid detection processes.
Innovation Solution
A computer-implemented method and system that determines the file type of incoming files and schedules appropriate malware detection schemes across multiple detection nodes based on policies, allowing for behavior-based analysis and prioritization of files suspected of containing malware, while also utilizing previous analysis results and dynamic scheduling to optimize detection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sophisticated malware detection schemes are used, then detection accuracy is improved, but detection time increases
Solution Approach 1:
The malware detection system is divided into multiple independent detection nodes, each capable of performing specific detection schemes. Files are segmented and distributed to different nodes for parallel processing, allowing sophisticated detection to occur simultaneously across multiple nodes rather than sequentially, thereby maintaining high detection accuracy while reducing overall detection time.
Solution Approach 2:
The system performs preliminary actions by maintaining a cache of previously analyzed files and their results. When a file is received, the system first checks if it exists in the cache or matches known malware signatures before proceeding to full analysis. This preliminary filtering allows the system to quickly handle common cases while reserving sophisticated detection resources for unknown or suspicious files.
2Reliability
If multiple malware detection schemes are applied, then detection reliability is improved, but system complexity increases
Solution Approach 1:
Different malware detection schemes are segmented and assigned to different detection nodes based on their specialization. Each node is responsible for specific detection tasks (e.g., signature-based, behavior-based, heuristic analysis), allowing the system to maintain high reliability through multiple detection approaches while managing complexity through clear separation of responsibilities.
Solution Approach 2:
The detection nodes are designed with multi-functionality, where each node can perform multiple types of detection schemes. This universality allows the system to apply diverse detection methods without proportionally increasing system complexity, as each node is capable of handling various detection tasks based on the file type and threat level.
3Measurement precision
If behavior-based analysis is performed, then detection accuracy is improved, but processing speed decreases
Solution Approach 1:
The system dynamically adjusts the depth and type of analysis based on risk assessment. For low-risk files, only quick signature-based checks are performed. For suspicious files, behavior-based analysis is dynamically initiated. This dynamic approach allows the system to maintain high detection accuracy for threats while preserving processing speed for benign files.
Solution Approach 2:
A risk assessment module acts as an intermediary between initial file reception and full behavior-based analysis. This intermediary evaluates files using lightweight criteria first, and only files that pass certain thresholds are subjected to resource-intensive behavior-based analysis, thereby maintaining both accuracy and processing speed.
4Reliability
If files are prioritized in detection queues, then critical malware detection is improved, but scheduling complexity increases
Solution Approach 1:
Different priority levels are assigned to different files based on their characteristics and risk assessment. Critical files (e.g., from untrusted sources, with suspicious headers) are placed in high-priority queues, while routine files use standard queues. This local differentiation of quality allows the system to focus resources on critical threats without overly complicating the overall scheduling mechanism.
Data Source
AI summary
According to one embodiment, a computer-implemented method for execution on one or more processors includes receiving a first file and determining a file type of the first file. The method also includes determining, according to a first policy, a plurality of malware detection schemes to apply to the first file based on the determined file type of the first file. In addition, the method includes scheduling the application of the determined plurality of malware detection schemes to the first file amongst a plurality of detection nodes according to a second policy. Further, the method includes determining, in response to determining the results of applying the plurality of malware detection schemes, that the first file is malware or determining that the first file is suspected malware according to a third policy.