Adversarial Malware Detection via Code Cave Payload Injection
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Solution Overview
Problem
IoT devices are vulnerable to malware due to the lack of trustworthy defensive mechanisms, making them susceptible to adversarial attacks that exploit their binary feature values and code caves, which conventional machine learning models struggle to detect effectively.
Innovation Solution
A system and method utilizing machine learning models trained with adversarial samples to classify executable files by extracting features, applying feature mapping, and determining code caves for payload insertion, enabling robust detection and prevention of adversarial malware threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine learning models are used for malware detection, then the system is simple to implement, but the detection accuracy against adversarial samples is low
Solution Approach 1:
The system performs preliminary action by generating adversarial samples during the training phase and injecting them into code caves before the model is deployed. This pre-preparation of adversarial examples enables the model to learn robust features against attacks, improving detection accuracy without adding complexity during runtime operation
Solution Approach 2:
The system segments the training process into distinct phases: extracting benign samples, identifying code caves, generating adversarial payloads, and combining them into training datasets. This segmentation allows systematic handling of complex adversarial training while maintaining manageable system architecture
2Reliability
If adversarial samples are generated and inserted into code caves, then the model becomes robust against adversarial attacks, but the training process becomes more complex
Solution Approach 1:
The system employs self-service by using the trained model itself to generate adversarial samples through its own feature extraction and classification capabilities. The model analyzes benign samples, identifies code caves, and generates adversarial payloads autonomously, reducing the need for external adversarial sample generation tools and simplifying the overall training pipeline
Solution Approach 2:
The system applies parameter changes by modifying the training dataset composition to include adversarial samples with injected payloads at identified code caves. By changing the parameters of the training data (adding adversarial examples with specific payload injections), the model learns to recognize and resist adversarial patterns, improving robustness while managing training complexity through controlled parameter modification
3Measurement precision
If feature mapping and component determination are applied, then the classification accuracy improves, but the processing time increases
Solution Approach 1:
The system extracts only the most relevant feature components from the full feature set by determining prominent features and selecting key components for classification. This extraction process reduces the dimensionality of input data to the classifier, maintaining high classification accuracy while significantly reducing processing time by eliminating redundant feature computations
Data Source
AI summary
There is provided systems and methods for adversarial sample generation and adversarial malware threat prevention. The method including: receiving an input executable sample; extracting features of the input executable sample and applying feature mapping to determine components of the features; determining a binary classifier representing whether the executable sample is adversarial using one or more machine learning models, the one or more machine learning models taking the components as input, the one or more machine learning models trained using, at least, generated adversarial samples, generating the generated adversarial samples includes determining code caves in training executable samples and inserting generated payloads as benign samples at the determined code caves; and where the binary classifier indicates adversarial, dropping the input executable sample, otherwise outputting the input executable sample.