Dynamic Malware Report Analysis With Neural-Boosting Ensemble
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Solution Overview
Problem
Existing malware detection techniques, such as signature matching and heuristics, are ineffective against evolving malware due to the increasing variety and number of software types, leading to high false positive and false negative rates.
Innovation Solution
An ensemble malware detector using natural language processing (NLP) techniques on dynamic malware analysis reports, combining text-based features from different pre-processing pipelines, including a neural network with an embedding layer and a boosting model, to improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signature matching and heuristic techniques are used for malware detection, then detection speed is maintained, but detection accuracy deteriorates due to high false positive and false negative rates
Solution Approach 1:
The system segments the malware detection task into multiple independent analysis components: static analysis, dynamic analysis, behavioral analysis, and machine learning classification. Each component processes specific features independently and their results are aggregated, allowing complex detection to be broken down into manageable segments that improve accuracy without overwhelming system complexity
Solution Approach 2:
The system merges multiple detection approaches (signature-based, heuristic, machine learning, behavioral analysis) into a unified ensemble detector. By combining the outputs of multiple independent detectors through aggregation functions, the system achieves superior detection accuracy that exceeds individual methods while maintaining manageable complexity through modular architecture
2Measurement precision
If multiple pre-processing pipelines are used to extract diverse text-based features, then detection accuracy improves, but computational complexity increases
Solution Approach 1:
The system performs preliminary feature extraction and text processing in separate pre-processing pipelines before the main detection phase. By pre-computing token-based features, n-gram statistics, and other text characteristics, the system prepares data in advance for efficient processing during detection, reducing real-time computational burden while maintaining high accuracy
Solution Approach 2:
The system dynamically selects and adjusts which pre-processing pipelines to execute based on the specific malware sample being analyzed. Not all pipelines run for every sample - the system adapts the processing depth and breadth to the characteristics of the input, optimizing resource usage while maintaining detection accuracy when needed
3Reliability
If ensemble methods combining neural networks and boosting models are used, then false positive and false negative rates decrease, but model complexity increases
Solution Approach 1:
The system introduces an aggregation layer as an intermediary between the neural network and boosting model components. This intermediary layer combines the outputs of multiple base detectors through weighted voting or ensemble functions, mediating their decisions to produce a final classification. This structure enables the system to leverage the strengths of different model types while managing complexity through a clear modular interface
Data Source
AI summary
A malware detector has been designed that uses a combination of NLP techniques on dynamic malware analysis reports for malware classification of files. The malware detector aggregates text-based features identified in different pre-processing pipelines that correspond to different types of properties of a dynamic malware analysis report. From a dynamic malware analysis report, the pre-processing pipelines of the malware detector generate a first feature set based on individual text tokens and a second feature set based on n-grams. The malware detector inputs the first feature set into a trained neural network having an embedding layer. The malware detector then extracts a dense layer from the trained neural network and aggregates the extracted layer with the second feature set to form an input for a trained boosting model. The malware detector inputs the cross-pipeline feature values into the trained boosting model to generate a malware detection output.