Malware Detection Ensemble Using Bayesian Subset Selection
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Solution Overview
Problem
Current malware detection systems face inefficiencies due to the need to run multiple detection procedures, leading to increased execution time and high false alarm rates, which divert resources away from more critical tasks and make it difficult to keep pace with evolving threats.
Innovation Solution
A data-driven ensemble-based approach that selects a subset of malware detection procedures using a Bayesian model based on likelihood and applies machine learning to aggregate results, reducing unnecessary procedures and false alarms while preserving detection coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple malware detection procedures are run to improve detection coverage, then detection reliability is improved, but detection time increases
Solution Approach 1:
The system dynamically adjusts the number and type of detection procedures executed based on file characteristics. The Bayesian model evaluates metadata and historical data to determine the optimal subset of procedures for each specific file, making the detection process adaptive rather than static. This resolves the contradiction by allowing full coverage when needed while reducing procedures for low-risk files.
Solution Approach 2:
The system changes the parameter of detection procedure selection from a fixed set to a dynamically determined subset based on likelihood calculations. By using Bayesian probability to adjust which procedures are executed based on file metadata and historical performance, the system optimizes the balance between comprehensive detection and execution time.
2Measurement precision
If multiple malware detection procedures are run to improve detection accuracy, then false alarm rate increases, but resource usage increases
Solution Approach 1:
The system applies partial action by executing only the necessary subset of detection procedures rather than running all available procedures. The Bayesian model determines the optimal number of procedures needed based on file characteristics, avoiding excessive execution of unnecessary procedures while maintaining sufficient detection accuracy.
Solution Approach 2:
The system uses its own historical detection data and performance metrics to make intelligent decisions about which procedures to execute. By learning from past detections and false alarms, the system self-optimizes its resource allocation, using historical data to predict which procedures are most likely to be effective for each file type.
3Productivity
If a subset of malware detection procedures is selected based on likelihood to reduce detection time, then productivity is improved, but detection reliability may deteriorate
Solution Approach 1:
The system performs preliminary analysis of file metadata and characteristics before executing detection procedures. The Bayesian model pre-calculates the likelihood of each procedure being effective based on historical data, allowing the system to select the optimal subset in advance. This preliminary action ensures that the most relevant procedures are executed while maintaining detection reliability.
Solution Approach 2:
The system incorporates feedback from historical detection results and false alarm data into the Bayesian model. By continuously learning from past performance, the system refines its ability to select procedures that maintain high detection reliability while optimizing for speed. The feedback loop ensures that reliability is preserved even as the system reduces the number of procedures executed.
Data Source
AI summary
Techniques are provided for data-driven ensemble-based malware detection. An exemplary method comprises obtaining a file; extracting metadata from the file; obtaining a plurality of malware detection procedures; selecting a subset of the plurality of malware detection procedures to apply to the file utilizing a likelihood that each of the plurality of malware detection procedures will result in a malware detection for the file based on the extracted metadata; applying the selected subset of the malware detection procedures to the file; and processing results of the subset of malware detection procedures using a machine learning model to determine a probability of the file being malware.