Malware Classification Assessment Using Adversarial Sample Modification
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Solution Overview
Problem
Existing malware detection systems face challenges in identifying obfuscated malware due to adversarial attacks, leading to vulnerabilities in classification models that require ongoing re-training and improvement to enhance adversarial robustness.
Innovation Solution
An automated system generates modified samples using machine learning techniques to identify vulnerabilities in malware classification models, incorporating modification models like heuristic learning, reinforcement learning, and genetic programming to improve the models' robustness by exposing them to evasive attacks during training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If malware detection systems use traditional classification models, then detection speed is maintained, but the models become vulnerable to obfuscated malware and adversarial attacks
Solution Approach 1:
The system performs preliminary actions by generating modified malware samples through adversarial attacks before the actual detection process. These modified samples are used to train the classification model in advance, making it resilient to future adversarial attacks. The assessment system proactively identifies vulnerabilities and reinforces the model before real-world deployment.
Solution Approach 2:
The system converts harmful adversarial attacks into beneficial training data. By deliberately applying adversarial modifications to malware samples and using these modified samples for training, the system transforms potential threats into opportunities for improving model robustness and detection accuracy.
2Reliability
If classification models are re-trained frequently to improve robustness, then detection accuracy improves, but computational resources and time are consumed
Solution Approach 1:
The system creates modified copies of existing malware samples through adversarial transformations rather than requiring complete re-training datasets. These copied and modified samples are used to efficiently update and reinforce the classification model with minimal computational overhead, avoiding the need for extensive re-training processes.
3Measurement precision
If the system assesses classification models using comprehensive adversarial testing, then model vulnerabilities are identified, but the assessment process becomes complex and time-consuming
Solution Approach 1:
The assessment system is designed with multi-functionality to handle various adversarial attack types and classification model variations through a unified framework. By creating a universal assessment mechanism that can evaluate multiple vulnerability scenarios simultaneously, the system reduces overall complexity while maintaining comprehensive testing capabilities.
Data Source
AI summary
A sample file that is associated with malware and a first modification model of a plurality of modification models to alter the sample file are selected. The first modification model generates a modification configuration based on the sample file. The modification configuration identifies a modification to be performed on the sample file. The sample file and the modification configuration are provided to a modification engine to generate a modified sample file. The modification configuration is adjusted based on the first modification model in response to receiving a classification from a classification model that identifies the modified sample file as being free of malware.