A Survey of Explainable Graph Neural Networks for Cyber Malware Analysis

Warmsley, Dana; Waagen, Alex; Xu, Jin; Liu, Zhining; Tong, Hanghang

doi:10.1109/bigdata55660.2022.10020943

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…In light of these limitations, much research is dedicated to devising methods for interpreting the predictions of deep graph networks. Such methods explore different aspects of the GNN models, usually with a focus on understanding the significance of input features, nodes, edges, and graph structures on the model's predictions [107][108][109], providing insights for the design of new GNN-based models across various domains [110].…”

Section: Explainability Of Graph Neural Networkmentioning

confidence: 99%

Explainable Graph Neural Networks: An Application to Open Statistics Knowledge Graphs for Estimating House Prices

Karamanou,

Brimos,

Kalampokis

et al. 2024

Preprint

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In the rapidly evolving field of real estate economics, the prediction of house prices continues to be a complex challenge, intricately tied to a multitude of socio-economic factors. However, traditional predictive models have often overlooked the spatial interdependencies that play a vital role in shaping housing prices. This study applies Graph Neural Networks (GNNs) on Open Statistics Knowledge Graphs to model spatial dependencies and predict house prices across Scotland’s 2011 data zones. To this end, integrated statistical indicators are retrieved from the official Scottish Open Government Data portal. The three representative GNN algorithms employed - ChebNet, GCN, and GraphSAGE - demonstrate higher prediction accuracy than traditional models, including the tabular-based XGBoost and a simple Multi-Layer Perceptron (MLP). In addition, local and global explainability are employed to increase transparency and trust in the predictions made by the most accurate GNN - GraphSAGE. The global feature importance is determined by a logistic regression surrogate model while the local, region-level understanding of the GNN predictions is achieved through the use of GNNExplainer. Explainaibility results are compared with those from a previous work that applied the XGBoost machine learning algorithm and the SHapley Additive exPlanations (SHAP) explainability framework on the same dataset. Interestingly, both global surrogate model and the SHAP approach underscored the Comparative Illness Factor, a health indicator, and the ratio of detached dwellings as the most crucial features in the global explainability. In the case of local explanations, while both methods showed similar results, the GNN approach provided a richer, more comprehensive understanding of the predictions for two specific data zones.

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Section: Explainability Of Graph Neural Networkmentioning

confidence: 99%

Explainable Graph Neural Networks: An Application to Open Statistics Knowledge Graphs for Estimating House Prices

Karamanou,

Brimos,

Kalampokis

et al. 2024

Preprint

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“…Regarding graph representation learning and GNN-based methods, the work [17] surveys GNN techniques employed for malware analysis with a focus on the prediction explainability. Other surveys review the applications of GNNs [6,[18][19][20] but none of them mention malware detection.…”

Section: Related Workmentioning

confidence: 99%

“…Explainability techniques currently exist to provide insights on the predictions performed by deep architectures such as GNNs [139]. However, very few works leverage these techniques to further improve the explainability of malware predictions with GNNs [17] and further research in this direction could be very useful to the fields of malware detection and analysis.…”

Section: Challenges and Directionsmentioning

confidence: 99%

A Survey on Malware Detection with Graph Representation Learning

Bilot¹,

Madhoun²,

Agha³

et al. 2023

Preprint

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Malware detection has become a major concern due to the increasing number and complexity of malware. Traditional detection methods based on signatures and heuristics are used for malware detection, but unfortunately, they suffer from poor generalization to unknown attacks and can be easily circumvented using obfuscation techniques. In recent years, Machine Learning (ML) and notably Deep Learning (DL) achieved impressive results in malware detection by learning useful representations from data and have become a solution preferred over traditional methods. More recently, the application of such techniques on graph-structured data has achieved state-of-the-art performance in various domains and demonstrates promising results in learning more robust representations from malware. Yet, no literature review focusing on graph-based deep learning for malware detection exists. In this survey, we provide an in-depth literature review to summarize and unify existing works under the common approaches and architectures. We notably demonstrate that Graph Neural Networks (GNNs) reach competitive results in learning robust embeddings from malware represented as expressive graph structures, leading to an efficient detection by downstream classifiers. This paper also reviews adversarial attacks that are utilized to fool graph-based detection methods. Challenges and future research directions are discussed at the end of the paper. CCS Concepts: • General and reference → Surveys and overviews; • Security and privacy → Malware and its mitigation; • Computing methodologies → Learning latent representations; Neural networks; Spectral methods.

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