mlsanityData to 7 Oct 2026

Survey of Malware Analysis through Control Flow Graph using Machine Learning

arXiv 2305.08993 ↗First posted 15 May 2023cs.CR cs.LGcited by 5

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Abstract (arXiv)

Malware is a significant threat to the security of computer systems and networks which requires sophisticated techniques to analyze the behavior and functionality for detection. Traditional signature-based malware detection methods have become ineffective in detecting new and unknown malware due to their rapid evolution. One of the most promising techniques that can overcome the limitations of signature-based detection is to use control flow graphs (CFGs). CFGs leverage the structural information of a program to represent the possible paths of execution as a graph, where nodes represent instructions and edges represent control flow dependencies. Machine learning (ML) algorithms are being used to extract these features from CFGs and classify them as malicious or benign. In this survey, we aim to review some state-of-the-art methods for malware detection through CFGs using ML, focusing on the different ways of extracting, representing, and classifying. Specifically, we present a comprehensive overview of different types of CFG features that have been used as well as different ML algorithms that have been applied to CFG-based malware detection. We provide an in-depth analysis of the challenges and limitations of these approaches, as well as suggest potential solutions to address some open problems and promising future directions for research in this field.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 10 of its 40 references that are in the map. First posted on arXiv 15 May 2023.

Builds on

10 of 40 references.

References · 10

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 875
  3. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
  4. 2020Parameterized Explainer for Graph Neural Networkcited by 423
  5. 2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 280
  6. 2018NetLSD: Hearing the Shape of a Graphcited by 51
  7. 2018Microsoft Malware Classification Challengecited by 46
  8. 2020Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detectioncited by 12
  9. 2023A Survey on Malware Detection with Graph Representation Learningcited by 11
  10. 2017Android Malware Clustering through Malicious Payload Miningcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 1.

5 papers.

  1. 2024Use of Graph Neural Networks in Aiding Defensive Cyber Operationscited by 7
  2. 2024Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systemscited by 5
  3. 2025The Application of Transformer-Based Models for Predicting Consequences of Cyber Attackscited by 1
  4. 2025A Benchmark Dataset for Graph Regression with Homogeneous and Multi-Relational Variantscited by 0
  5. 2026Temporal Generalization and Explanation Stability of Control Flow Graph Neural Networks for Malware Detectioncited by 0