Robust Malware Classification via Deep Graph Networks on Call Graph Topologies

Errica, Federico; Iadarola, Giacomo; Martinelli, Fabio; Mercaldo, Francesco; Micheli, Alessio

doi:10.14428/esann/2021.es2021-82

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…Errica et al . (Errica et al ., 2020) used nested cross-validation (CV) to fairly compare different GNN models. Briefly, a nested CV contains an external and an internal CV, where CV can choose the k-fold or holdout technique.…”

Section: Methodsmentioning

confidence: 99%

DeepCIP: a multimodal deep learning method for the prediction of internal ribosome entry sites of circRNAs

Zhou

Yao

et al. 2022

Preprint

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Motivation: Circular RNAs (circRNAs) have been found to have the potential to code proteins. Internal ribosome entry sites (IRESs) are key RNA regulatory elements for the translation of proteins by circRNAs through a cap-independent mechanism. IRES can be identified by bicistronic assay, but the method is time-consuming and laborious. Therefore, it is important to develop computational methods for facilitating IRES identification, evaluation, and design in circRNAs. Results: In this study, we proposed DeepCIP, a multimodal deep learning approach for circRNA IRES prediction, by exploiting both sequence and structure information. As far as we know, DeepCIP is the first predictor for circRNA IRESs, which consists of an RNA processing module, an S-LSTM module, a GCN module, a feature fusion module, and an ensemble module. The comparative studies show that DeepCIP outperforms other comparative methods and justify the effectiveness of the sequence model and structure model of DeepCIP for extracting features. We found that the integration of structural information on the basis of sequence information effectively improves predictive performance. For the real circRNA IRES prediction, DeepCIP also outperforms other methods. DeepCIP may facilitate the study of the coding potential of circRNAs as well as the design of circRNA drugs. DeepCIP as a standalone program is freely available at https://github.org/zjupgx/DeepCIP.

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Section: Methodsmentioning

confidence: 99%

DeepCIP: a multimodal deep learning method for the prediction of internal ribosome entry sites of circRNAs

Zhou

Yao

et al. 2022

Preprint

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“…Malware Android apps from CICMalDroid2020 dataset are used for evaluation, with a best F1-score of 92.23%. For the detection of obfuscated malware, the paper [40] leverages a call graph where nodes are attributed with nodes' out-degree only. The Contextual Graph Markov Model (CGMM) [118] is used to learn the embeddings that are then classified using a standard feed-forward network, achieving a macro F1-score of 97.2%.…”

Section: Fcg Approachesmentioning

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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“…• Errica et al [40] propose a deep Hidden Markov Model for temporal graphs. The model consists of a stack of layers which perform probabilistic message passing, trained with Expectation-Maximization.…”

Section: Special Session's Contributionsmentioning

confidence: 99%

Graph Representation Learning

Bacciu,

Errica,

Micheli

et al. 2023

ESANN 2023 Proceesdings

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In a broad range of real-world machine learning applications, representing examples as graphs is crucial to avoid a loss of information. For this reason, in the last few years, the definition of machine learning methods, particularly neural networks, for graph-structured inputs has been gaining increasing attention. In particular, Deep Graph Networks (DGNs) are nowadays the most commonly adopted models to learn a representation that can be used to address different tasks related to nodes, edges, or even entire graphs. This tutorial paper reviews fundamental concepts and open challenges of graph representation learning and summarizes the contributions that have been accepted for publication to the ESANN 2023 special session on the topic.

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Robust Malware Classification via Deep Graph Networks on Call Graph Topologies

Cited by 5 publications

References 10 publications

DeepCIP: a multimodal deep learning method for the prediction of internal ribosome entry sites of circRNAs

DeepCIP: a multimodal deep learning method for the prediction of internal ribosome entry sites of circRNAs

A Survey on Malware Detection with Graph Representation Learning

Graph Representation Learning

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