Multi-nodal Malware Analysis via Distributed Detection Segmentation
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Solution Overview
Problem
Current host-level malware protection systems are inefficient in detecting sophisticated malware, particularly susceptible to zero-day attacks and often result in increased complexity leading to longer detection times and 'false positive' inefficiencies.
Innovation Solution
A multi-nodal malware analysis system where multiple nodes operate cooperatively with an analysis console to distribute malware detection processes, minimizing processing load and propagating results across nodes to enhance detection efficiency and reduce false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If host-level malware protection systems apply complex detection processes, then detection capability improves, but detection time increases and false positives increase
Solution Approach 1:
The system segments malware detection into multiple stages: initial filtering at host level using lightweight detection, then progressive analysis at intermediary nodes, and finally comprehensive analysis at centralized nodes. This segmentation allows complex detection processes to be distributed across multiple levels, improving detection capability while reducing detection time by eliminating the need for all hosts to perform full analysis.
Solution Approach 2:
The system performs preliminary malware detection and analysis at host level and intermediary nodes before final verification. Files are pre-screened using identifiers and preliminary detection results, so that when files reach centralized analysis nodes, only truly suspicious files require full analysis. This preliminary action reduces the overall detection time while maintaining high detection capability.
2Measurement precision
If host-level protection systems increase detection complexity, then malware detection accuracy improves, but system resource consumption increases
Solution Approach 1:
The detection system is segmented across multiple nodes with different resource capacities. Host systems perform lightweight initial detection, intermediary nodes perform medium-complexity analysis, and centralized nodes perform resource-intensive comprehensive analysis. This segmentation allows high detection accuracy to be achieved through distributed computing, preventing any single host from consuming excessive resources.
Solution Approach 2:
Intermediary nodes are introduced as mediators between hosts and centralized analysis nodes. These intermediary nodes perform preliminary analysis and filtering, reducing the number of files that require comprehensive analysis at centralized nodes. This intermediary layer improves detection accuracy while reducing overall system resource consumption by preventing redundant full analyses.
3Reliability
If centralized analysis console processes all malware detection, then detection thoroughness improves, but processing load at console increases
Solution Approach 1:
The centralized analysis function is segmented across multiple levels: host-level initial detection, intermediary-node analysis, and centralized-node verification. This segmentation distributes processing load away from any single analysis console while maintaining thorough detection through progressive analysis at each level. Only files that pass through multiple filtering stages reach centralized nodes for final verification.
Solution Approach 2:
Preliminary detection and filtering are performed at host and intermediary nodes before files reach the analysis console. Files are pre-processed and only those requiring further investigation are forwarded to centralized analysis. This preliminary action reduces the processing load at analysis consoles while maintaining detection thoroughness through multi-stage verification.
4Adaptability or versatility
If multiple nodes perform independent malware detection, then detection coverage improves, but false positive rate increases
Solution Approach 1:
The system implements feedback mechanisms where detection results from multiple nodes are aggregated and cross-verified. Files detected as malware by one node are verified by other nodes before final classification. This feedback loop allows broad detection coverage across multiple nodes while reducing false positives through consensus verification and result sharing among nodes.
Solution Approach 2:
Multiple nodes perform diverse detection functions (host-level filtering, intermediary analysis, centralized verification) rather than identical functions. Each node type is optimized for its specific function, and results are combined to achieve comprehensive detection. This multi-functionality approach improves detection coverage while reducing false positives through complementary detection strategies.
Data Source
AI summary
A computer-implemented method includes accessing, by an analysis console, information related to a first file received at a first host of a plurality of hosts. Each host is capable of running a corresponding set of malware detection processes. The information includes: an identifier of the first file; and data indicating a first result of the first host applying the set of malware detection processes to the first file. The identifier is generated by the first host and is usable by each of the hosts to determine whether a second file comprises content substantially equivalent to content of the first file. The analysis console generates a first output including: the identifier of the first file; and a second result indicating whether the first file comprises malware. The second result is usable by each of the hosts to determine whether the second file comprises malware. The first output is propagated to the hosts.