Distributed Malware Detection via Segmented Analysis
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Solution Overview
Problem
Current host-level malware protection systems are inadequate against sophisticated malware, particularly zero-day attacks, and face inefficiencies due to complexity in detection methods, which can lead to longer execution times and increased susceptibility to attacks.
Innovation Solution
A computer-implemented method for distributed malware analysis that offloads analysis to an analysis console, allowing multiple clients to share the cost of malware analysis while minimizing the risk of infecting the console and optimizing communication through secure, encrypted channels, using a two-stage analysis process where clients perform initial detection and the console performs further verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If host-level malware protection systems use complex detection methods, then detection capability improves, but execution time increases
Solution Approach 1:
The malware detection system is segmented into multiple independent detection modules, each specializing in different malware types or detection techniques. This allows parallel execution of multiple detection methods simultaneously, improving overall detection capability without proportionally increasing execution time, as different modules can operate concurrently rather than sequentially
Solution Approach 2:
The system performs preliminary analysis actions on files before full malware detection is executed. Quick preliminary checks filter out obviously safe files or identify high-priority suspects, allowing the complex detection methods to be applied only where necessary, thus reducing overall execution time while maintaining detection reliability
2Ease of operation
If host-level malware protection is used, then local detection is achieved, but susceptibility to sophisticated attacks increases
Solution Approach 1:
The malware detection system is designed with multi-functional capabilities that can detect various types of malware including zero-day attacks, polymorphic malware, and fileless malware through multiple detection techniques. The system combines signature-based detection, behavior analysis, sandboxing, and heuristic methods in a single unified platform, making it universally effective against diverse attack vectors while maintaining ease of local operation
Solution Approach 2:
The system introduces intermediary components such as sandbox environments and behavioral analysis layers that mediate between the host system and potential malware. These intermediaries allow safe observation and analysis of suspicious activities without directly exposing the host system to malicious code, thereby improving resistance to sophisticated attacks while maintaining local detection capability
3Measurement precision
If centralized malware analysis is performed, then detection accuracy improves, but processing load on analysis console increases
Solution Approach 1:
The analysis console is segmented into multiple specialized analysis engines or workers, each handling specific types of malware analysis or different client files. This segmentation distributes the processing load across multiple independent units rather than concentrating all processing on a single console, reducing the burden on any single analysis component while maintaining centralized coordination for accurate detection
Solution Approach 2:
The system applies partial analysis actions at the client level before submitting files to the analysis console. Clients perform initial filtering, basic scanning, and preparation of suspicious files, sending only the most critical cases to the centralized console. This partial action reduces the volume of files requiring full centralized analysis, lowering processing load while maintaining detection accuracy through the combination of local and centralized analysis
Data Source
AI summary
According to one embodiment, a computer-implemented method includes accessing, using one or more processing units, a first file of a plurality of files requested to be analyzed for malware. Each of the plurality of files corresponds to a respective remote client of a plurality of remote clients. Further, the method includes: processing, using the one or more processing units, an analysis of the first file for malware; and generating an output comprising an indication of whether the first file comprises malware. The method also includes accessing, using the one or more processing units, an address for a first remote client of the plurality of remote clients. The first remote client is the respective remote client corresponding to the first file. In addition, the method includes: sending, using the one or more processing units, the output in a communication addressed to the first remote client corresponding to the first file.