Malware Detection Using Static Feature Graph Representation Learning

Temel Kavramlar

A feature graph-based malware detection method, MFGraph, is proposed to characterize applications by learning feature-to-feature relationships, achieving improved detection accuracy while mitigating the impact of concept drift.

Özet

The paper introduces a feature graph-based malware detection method, MFGraph, to address the limitations of existing feature fusion-based detection methods.
Key highlights:

MFGraph constructs a feature graph using static features extracted from binary PE files to capture the relationships between different features.
A deep graph convolutional network is employed to learn the representation of the feature graph, and a three-layer perceptron is used as the classifier.
Experiments on the EMBER dataset show that MFGraph achieves an AUC score of 0.98756 on the malware detection task, outperforming other baseline models.
MFGraph exhibits superior stability and is the least affected by concept drift, with the AUC score decreasing by only 5.884% in one year.

The paper demonstrates that modeling the feature relationships using a graph structure can provide a more comprehensive characterization of binary files, leading to improved malware detection performance and robustness against concept drift.

İstatistikler

The EMBER dataset contains 800K samples, with 80% used for training and 20% for testing.
The distribution of benign and malware samples in the EMBER dataset varies over the 12 months of 2018.

Alıntılar

"Malware can greatly compromise the integrity and trustworthiness of information and is in a constant state of evolution."
"Existing feature fusion-based detection methods generally overlook the correlation between features. And mere concatenation of features will reduce the model's characterization ability, lead to low detection accuracy."
"MFGraph exhibits the most stable performance and effectively mitigates the impact of concept drift."

Önemli Bilgiler Şuradan Elde Edildi

Feature graph construction with static features for malware detection

by Binghui Zou,... : arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16362.pdf

Daha Derin Sorular

How can the feature graph construction be further optimized to capture more comprehensive relationships between features?

To optimize the feature graph construction for capturing more comprehensive relationships between features, several strategies can be implemented:

Feature Engineering: Instead of relying solely on the static features extracted from the binary PE files, additional feature engineering techniques can be applied to derive new features that may provide more insights into the characteristics of the malware. These new features can then be incorporated into the feature graph construction process.

Graph Structure Refinement: The structure of the feature graph can be refined by considering different types of relationships between features. For example, incorporating weighted edges to signify the strength of the relationship between features or introducing different types of nodes to represent various feature categories can enhance the graph's representational power.

Graph Embedding Techniques: Utilizing advanced graph embedding techniques, such as node2vec or GraphSAGE, can help in capturing more nuanced relationships between features in the graph. These techniques can generate low-dimensional vector representations of nodes that preserve the graph's structural information.

Graph Convolutional Networks (GCNs): Implementing more sophisticated GCN architectures with multiple layers and attention mechanisms can improve the model's ability to propagate information across the feature graph and capture complex feature interactions.

Hyperparameter Tuning: Fine-tuning the hyperparameters of the graph construction process, such as the number of layers, learning rate, and dropout rate, can optimize the graph's structure for better feature relationship representation.

By incorporating these optimization strategies, the feature graph construction process can be enhanced to capture more comprehensive relationships between features, leading to improved malware detection accuracy.

How can the potential limitations of the graph representation learning approach be addressed, and what are these limitations?

Graph representation learning approaches, such as graph convolutional networks (GCNs), have shown great promise in capturing structural information and relationships in graph data. However, they also come with certain limitations that need to be addressed:
Limitations:

Over-smoothing: In deep GCNs, information from distant nodes may get diluted or over-smoothed as it propagates through multiple layers, leading to a loss of important local information.

Scalability: GCNs may face scalability issues when dealing with large graphs, as the computational complexity increases with the number of nodes and edges.

Generalization: GCNs may struggle to generalize well to unseen or out-of-distribution data, especially in the presence of concept drift or evolving malware variants.

Addressing Limitations:

Regularization Techniques: Applying regularization techniques like dropout or L2 regularization can help prevent over-smoothing in deep GCNs and improve model generalization.

Graph Sampling: Utilizing graph sampling techniques can help in handling scalability issues by working with smaller, representative subsets of the graph data.

Adaptive Learning: Implementing adaptive learning mechanisms that can dynamically adjust the model's parameters based on the evolving data distribution can enhance the model's ability to adapt to concept drift.

Ensemble Methods: Combining multiple GCN models or incorporating ensemble learning techniques can improve the model's robustness and generalization capabilities.

By addressing these limitations through appropriate techniques and strategies, the graph representation learning approach can be enhanced to overcome challenges and improve its effectiveness in malware detection tasks.

How can the proposed MFGraph method be extended to handle dynamic features or a combination of static and dynamic features for malware detection?

Extending the MFGraph method to handle dynamic features or a combination of static and dynamic features for malware detection involves the following steps:

Dynamic Feature Extraction: Incorporate mechanisms to extract dynamic features from the behavior of the malware during runtime, such as API calls, system calls, network traffic patterns, and memory usage. These dynamic features can provide real-time insights into the malware's actions and behavior.

Feature Fusion: Develop a framework to fuse static and dynamic features into a unified feature representation. This fusion can be achieved through techniques like concatenation, attention mechanisms, or graph-based fusion to capture both the structural information from static features and the temporal dynamics from dynamic features.

Temporal Graph Convolution: Modify the graph convolutional network architecture to incorporate temporal information and handle dynamic features. Temporal graph convolutional networks or recurrent graph neural networks can be utilized to model the evolving relationships between features over time.

Adaptive Learning: Implement adaptive learning algorithms that can adjust the model's parameters based on the changing nature of dynamic features and concept drift. Techniques like online learning or continual learning can help the model adapt to new information and evolving malware behaviors.

Evaluation and Validation: Validate the extended MFGraph model on datasets containing both static and dynamic features to assess its performance in detecting malware accurately and efficiently.

By extending the MFGraph method to handle dynamic features and integrating a combination of static and dynamic features, the model can enhance its detection capabilities and provide a more comprehensive understanding of malware behaviors in real-world scenarios.