A Hierarchical Graph-Based Neural Network for Malware Classification

Wang, Shuai; Zhao, Yuran; Liu, Gongshen; Su, Bo

doi:10.1007/978-3-030-92273-3_51

Cited by 8 publications

(6 citation statements)

References 8 publications

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“…In this part, our model employs AutoGNN with Explicit Link Information [46] algorithm to construct edge feature engineering of the multi-source heterogeneous network. The AutoGNN model can automate the appropriate GNN architecture design for the given data [47] and introduce edge embedding in an explicit way.…”

Section: Edge Feature Representationmentioning

confidence: 99%

AutoEdge-CCP: A novel approach for predicting cancer-associated circRNAs and drugs based on automated edge embedding

Chen,

Wang,

Wang

et al. 2024

PLoS Comput Biol

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The unique expression patterns of circRNAs linked to the advancement and prognosis of cancer underscore their considerable potential as valuable biomarkers. Repurposing existing drugs for new indications can significantly reduce the cost of cancer treatment. Computational prediction of circRNA-cancer and drug-cancer relationships is crucial for precise cancer therapy. However, prior computational methods fail to analyze the interaction between circRNAs, drugs, and cancer at the systematic level. It is essential to propose a method that uncover more valuable information for achieving cancer-centered multi-association prediction. In this paper, we present a novel computational method, AutoEdge-CCP, to unveil cancer-associated circRNAs and drugs. We abstract the complex relationships between circRNAs, drugs, and cancer into a multi-source heterogeneous network. In this network, each molecule is represented by two types information, one is the intrinsic attribute information of molecular features, and the other is the link information explicitly modeled by autoGNN, which searches information from both intra-layer and inter-layer of message passing neural network. The significant performance on multi-scenario applications and case studies establishes AutoEdge-CCP as a potent and promising association prediction tool.

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Section: Edge Feature Representationmentioning

confidence: 99%

AutoEdge-CCP: A novel approach for predicting cancer-associated circRNAs and drugs based on automated edge embedding

Chen,

Wang,

Wang

et al. 2024

PLoS Comput Biol

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“…Although FCGs offer a global view of function calls executed by the program, they generally lack the intra-procedural information that CFGs provide. To address this, some approaches can be employed by jointly using FCGs and CFGs, where embeddings from CFGs are integrated into the nodes of the FCGs, to capture both intra-procedural and inter-procedural semantic [32,33,53]. In the case of Android malware analysis, a prevalent approach is to statically extract the API call sequences from the application and represent them using a FCG [56,58,59,82].…”

Section: Common Graphmentioning

confidence: 99%

“…A similar CFG based on an assembly is employed in the paper [53]. First, the semantic of functions is computed using random walk and the BERT [138] language model.…”

Section: Cfg Approaches For Windows Malware Detectionmentioning

confidence: 99%

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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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“…al. [23] proposed the Rule-based Representation Learner (RRL) which automatically learns interpretable nonfuzzy rules for data representation and classification. In Wei et.…”

Section: Introductionmentioning

confidence: 99%

Explaining with Greater Support: Weighted Column Sampling Optimization for q-Consistent Summary-Explanations

Chen¹,

Dai²,

Xia³

et al. 2023

Preprint

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Machine learning systems have been extensively used as auxiliary tools in domains that require critical decision-making, such as healthcare and criminal justice. The explainability of decisions is crucial for users to develop trust on these systems. In recent years, the globally-consistent rule-based summary-explanation and its max-support (MS) problem have been proposed, which can provide explanations for particular decisions along with useful statistics of the dataset. However, globally-consistent summary-explanations with limited complexity typically have small supports, if there are any. In this paper, we propose a relaxed version of summaryexplanation, i.e., the 𝑞-consistent summary-explanation, which aims to achieve greater support at the cost of slightly lower consistency.The challenge is that the max-support problem of 𝑞-consistent summary-explanation (MSqC) is much more complex than the original MS problem, resulting in over-extended solution time using standard branch-and-bound solvers. To improve the solution time efficiency, this paper proposes the weighted column sampling (WCS) method based on solving smaller problems by sampling variables according to their simplified increase support (SIS) values. Experiments verify that solving MSqC with the proposed SIS-based WCS method is not only more scalable in efficiency, but also yields solutions with greater support and better global extrapolation effectiveness.

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A Hierarchical Graph-Based Neural Network for Malware Classification

Cited by 8 publications

References 8 publications

AutoEdge-CCP: A novel approach for predicting cancer-associated circRNAs and drugs based on automated edge embedding

AutoEdge-CCP: A novel approach for predicting cancer-associated circRNAs and drugs based on automated edge embedding

A Survey on Malware Detection with Graph Representation Learning

Explaining with Greater Support: Weighted Column Sampling Optimization for q-Consistent Summary-Explanations

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