AMD-FCG: An Enhanced Function Call Graph Dataset with Integrated Topological Features for Malware Detection and Classification

• URL: http://arxiv.org/abs/2606.06815v1
• Date: Fri, 05 Jun 2026 01:37:25 GMT
• Title: AMD-FCG: An Enhanced Function Call Graph Dataset with Integrated Topological Features for Malware Detection and Classification
• Abstract summary: This paper introduces AMD-FCG, an enhanced Function Call Graph dataset integrated with topological features of malwares.<n>It can be used to develop and deploy more efficient and innovative malware detection systems.
• Score: 0.7093777708568951
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As malware illustrates a complex structure and behavior, detection of these has been a significant challenge in the domain of cybersecurity along with related services in daily life. So, it becomes crucial to have a reliable and adaptive solution to address the issue. Among the several detection methods developed over the years, one of the most reliable ones is studying and analyzing the structural and behavioral patterns of malware. These patterns of sophisticated malware can be obtained with the help of Function Call Graphs (FCGs). However, to effectively cover numerous groups of families of malware, it is required to have a sufficiently large dataset for the system to operate on. In order to ensure accuracy and robustness of the system, the dataset should comprise samples of different malwares and a benign application for secure execution of the detection process. This paper introduces AMD-FCG, an enhanced Function Call Graph dataset integrated with topological features of malwares. The framework enhances the detection procedure, streamlining the workflow for cybersecurity professionals and also eliminating the need for dynamic analysis and extensive processing. Therefore, it can be used to develop and deploy more efficient and innovative malware detection systems.

Related papers

• Performance analysis of Machine learning algorithms for predicting malware [0.0]
  Malware poses a persistent and evolving threat to modern computing systems.<n>Traditional signature-based antivirus solutions often fail to identify newly emerging malware.<n>This study proposes a machine learning-based malware detection framework.
  arXiv  Detail & Related papers  (2026-08-03T03:24:58Z)
• One-Class Intrusion Detection with Dynamic Graphs [46.453758431767724]
  Machine learning-based intrusion detection constitutes a promising approach for improving security.<n>We propose a novel intrusion detection method, TGN-SVDD, which builds upon modern dynamic graph modelling and deep anomaly detection.<n>We demonstrate its superiority over several baselines for realistic intrusion detection data and suggest a more challenging variant of the latter.
  arXiv  Detail & Related papers  (2025-08-18T12:36:55Z)
• Certifiably robust malware detectors by design [48.367676529300276]
  We propose a new model architecture for robust malware detection by design.<n>We show that every robust detector can be decomposed into a specific structure, which can be applied to learn empirically robust malware detectors.<n>Our framework ERDALT is based on this structure.
  arXiv  Detail & Related papers  (2025-08-10T09:19:29Z)
• MalVol-25: A Diverse, Labelled and Detailed Volatile Memory Dataset for Malware Detection and Response Testing and Validation [0.0]
  Existing datasets often lack diversity, comprehensive labelling, and the complexity necessary for effective machine learning and agentic AI training.<n>To fill this gap, we developed a systematic approach for generating a dataset that combines automated malware execution in controlled virtual environments with dynamic monitoring tools.<n>The resulting dataset comprises clean and infected memory snapshots across multiple malware families and operating systems, capturing detailed behavioural and environmental features.
  arXiv  Detail & Related papers  (2025-07-05T10:45:45Z)
• Expert-in-the-Loop Systems with Cross-Domain and In-Domain Few-Shot Learning for Software Vulnerability Detection [38.083049237330826]
  This study explores the use of Large Language Models (LLMs) in software vulnerability assessment by simulating the identification of Python code with known Common Weaknessions (CWEs)<n>Our results indicate that while zero-shot prompting performs poorly, few-shot prompting significantly enhances classification performance.<n> challenges such as model reliability, interpretability, and adversarial robustness remain critical areas for future research.
  arXiv  Detail & Related papers  (2025-06-11T18:43:51Z)
• A Novel Approach to Malicious Code Detection Using CNN-BiLSTM and Feature Fusion [2.3039261241391586]
  This study employs the minhash algorithm to convert binary files of malware into grayscale images. The study utilizes IDA Pro to decompile and extract opcode sequences, applying N-gram and tf-idf algorithms for feature vectorization. A CNN-BiLSTM fusion model is designed to simultaneously process image features and opcode sequences, enhancing classification performance.
  arXiv  Detail & Related papers  (2024-10-12T07:10:44Z)
• Automated Phishing Detection Using URLs and Webpages [35.66275851732625]
  This project addresses the constraints of traditional reference-based phishing detection by developing an LLM agent framework. This agent harnesses Large Language Models to actively fetch and utilize online information. Our approach has achieved with accuracy of 0.945, significantly outperforms the existing solution(DynaPhish) by 0.445.
  arXiv  Detail & Related papers  (2024-08-03T05:08:27Z)
• Profile of Vulnerability Remediations in Dependencies Using Graph Analysis [40.35284812745255]
  This research introduces graph analysis methods and a modified Graph Attention Convolutional Neural Network (GAT) model. We analyze control flow graphs to profile breaking changes in applications occurring from dependency upgrades intended to remediate vulnerabilities. Results demonstrate the effectiveness of the enhanced GAT model in offering nuanced insights into the relational dynamics of code vulnerabilities.
  arXiv  Detail & Related papers  (2024-03-08T02:01:47Z)
• Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection [0.5475886285082937]
  This study conducts a thorough examination of malware detection using machine learning techniques. The aim is to advance cybersecurity capabilities by identifying and mitigating threats more effectively.
  arXiv  Detail & Related papers  (2024-03-04T17:22:43Z)
• Discovering Malicious Signatures in Software from Structural Interactions [7.06449725392051]
  We propose a novel malware detection approach that leverages deep learning, mathematical techniques, and network science. Our approach focuses on static and dynamic analysis and utilizes the Low-Level Virtual Machine (LLVM) to profile applications within a complex network. Our approach marks a substantial improvement in malware detection, providing a notably more accurate and efficient solution.
  arXiv  Detail & Related papers  (2023-12-19T23:42:20Z)
• A survey on hardware-based malware detection approaches [45.24207460381396]
  Hardware-based malware detection approaches leverage hardware performance counters and machine learning prowess. We meticulously analyze the approach, unraveling the most common methods, algorithms, tools, and datasets that shape its contours. The discussion extends to crafting mixed hardware and software approaches for collaborative efficacy, essential enhancements in hardware monitoring units, and a better understanding of the correlation between hardware events and malware applications.
  arXiv  Detail & Related papers  (2023-03-22T13:00:41Z)
• Visual Causality Analysis of Event Sequence Data [32.74361488457415]
  We introduce a visual analytics method for recovering causalities in event sequence data. We extend the Granger causality analysis algorithm on Hawkes processes to incorporate user feedback into causal model refinement.
  arXiv  Detail & Related papers  (2020-09-01T04:28:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.