Malware Detection Structure Using k-d Tree Boundaries
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Solution Overview
Problem
Existing methods for determining whether malicious files are targeted, such as manual labeling and traditional machine learning, are inefficient due to the dynamic nature of malicious file characteristics and the difficulty in scaling, as well as poor performance when dealing with data outside the training distribution.
Innovation Solution
A computer-implemented method using a malware detection structure, like a k-dimensional tree, to identify boundaries between targeted and non-targeted malicious files, classifying new data points based on their spatial location within this structure, thereby improving classification accuracy and reducing false positives and negatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to determine whether malicious files are targeted, then classification accuracy may be maintained, but the process becomes time intensive and difficult to scale
Solution Approach 1:
The system enables automatic classification of malicious files through self-service mechanisms. The machine learning model autonomously determines whether a malicious file is targeted or non-targeted by analyzing file characteristics and comparing them against learned patterns, eliminating the need for manual analyst intervention while maintaining classification accuracy
Solution Approach 2:
The system transforms the classification problem by changing parameters from manual assessment to automated feature analysis. It extracts and analyzes multiple parameters including file size, file type, sender information, recipient information, and content characteristics, then uses these parameter changes to automatically determine targeted status through machine learning models
2Extent of automation
If traditional machine learning approaches are used for email classification, then automation is achieved, but performance deteriorates when data lies outside the training distribution
Solution Approach 1:
The system implements dynamic adaptation mechanisms that allow the classification model to adjust to new data distributions. It continuously learns from incoming emails and updates its understanding of targeted versus non-targeted patterns, enabling reliable classification even when encountering previously unseen malware characteristics or attack vectors
Solution Approach 2:
The system performs preliminary analysis by examining multiple dimensions of email data before classification, including file metadata, content analysis, sender/recipient patterns, and contextual information. This comprehensive preliminary action prepares the model to handle diverse data distributions more effectively by establishing baseline patterns across multiple parameters
3Measurement precision
If comprehensive analysis of malicious file characteristics is performed, then classification accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system segments the classification process into distinct analytical stages: initial filtering based on obvious characteristics, intermediate analysis of file properties and metadata, and final classification using machine learning models. This segmentation allows parallel processing of different feature sets, maintaining comprehensive analysis while reducing overall processing time through efficient resource allocation
Data Source
AI summary
The disclosed computer-implemented method for determining whether malicious files are targeted may include (i) applying, to a malware detection structure, a plurality of sample data points, each sample data point corresponding to at least one of a malicious file known to be targeted and a malicious file known to be non-targeted, (ii) identifying one or more boundaries of the sample data points within the malware detection structure, (iii) determining, after identifying the sample boundaries, that a new data point falls outside of the boundaries, and (iv) classifying a malicious file associated with the new data point as non-targeted in response to determining that the new data point falls outside of the sample boundaries. Various other methods, systems, and computer-readable media are also disclosed.