mlsanityData to 14 Oct 2026

Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systems

arXiv 2410.24017 ↗First posted 31 Oct 2024cs.CR cs.AIcited by 5

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Abstract (arXiv)

The proliferation of malware, particularly through the use of packing, presents a significant challenge to static analysis and signature-based malware detection techniques. The application of packing to the original executable code renders extracting meaningful features and signatures challenging. To deal with the increasing amount of malware in the wild, researchers and anti-malware companies started harnessing machine learning capabilities with very promising results. However, little is known about the effects of packing on static machine learning-based malware detection and classification systems. This work addresses this gap by investigating the impact of packing on the performance of static machine learning-based models used for malware detection and classification, with a particular focus on those using visualisation techniques. To this end, we present a comprehensive analysis of various packing techniques and their effects on the performance of machine learning-based detectors and classifiers. Our findings highlight the limitations of current static detection and classification systems and underscore the need to be proactive to effectively counteract the evolving tactics of malware authors.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 22 of its 65 references that are in the map. First posted on arXiv 31 Oct 2024.

Builds on

22 of 65 references.

References · 22

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  4. 2021EfficientNetV2: Smaller Models and Faster Trainingcited by 1,135
  5. 2018EMBER: An Open Dataset for Training Static PE Malware Machine Learning Modelscited by 165
  6. 2017Malware Detection by Eating a Whole EXEcited by 115
  7. 2018TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Timecited by 108
  8. 2020Dos and Don'ts of Machine Learning in Computer Securitycited by 102
  9. 2020Functionality-preserving Black-box Optimization of Adversarial Windows Malwarecited by 58
  10. 2015Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Featurescited by 48
  11. 2018Microsoft Malware Classification Challengecited by 46
  12. 2020Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detectioncited by 24
10 more
  1. 2015Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classificationcited by 21
  2. 2020Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detectioncited by 18
  3. 2023Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performancecited by 13
  4. 2018Malware Dynamic Analysis Evasion Techniques: A Surveycited by 6
  5. 2022Fast & Furious: Modelling Malware Detection as Evolving Data Streamscited by 6
  6. 2022Towards a Fair Comparison and Realistic Evaluation Framework of Android Malware Detectors based on Static Analysis and Machine Learningcited by 6
  7. 2022Fusing Feature Engineering and Deep Learning: A Case Study for Malware Classificationcited by 6
  8. 2023Survey of Malware Analysis through Control Flow Graph using Machine Learningcited by 5
  9. 2021Analysis of Machine Learning Approaches to Packing Detectioncited by 2
  10. 2024The Malware as a Service ecosystemcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2025Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classificationcited by 1
  2. 2026Automated Malware Family Classification using Weighted Hierarchical Ensembles of Large Language Modelscited by 1
  3. 2026Beyond the Wrapper: Identifying Artifact Reliance in Static Malware Classifiers using TRUSTEEcited by 0
  4. 2026Learning to Look Benign: Targeted Evasion of Malware Detectors via API Import Injectioncited by 0
  5. 2026A Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attributioncited by 0