Towards an Automated Pipeline for Detecting and Classifying Malware through Machine Learning
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Abstract (arXiv)
The constant growth in the number of malware - software or code fragment potentially harmful for computers and information networks - and the use of sophisticated evasion and obfuscation techniques have seriously hindered classic signature-based approaches. On the other hand, malware detection systems based on machine learning techniques started offering a promising alternative to standard approaches, drastically reducing analysis time and turning out to be more robust against evasion and obfuscation techniques. In this paper, we propose a malware taxonomic classification pipeline able to classify Windows Portable Executable files (PEs). Given an input PE sample, it is first classified as either malicious or benign. If malicious, the pipeline further analyzes it in order to establish its threat type, family, and behavior(s). We tested the proposed pipeline on the open source dataset EMBER, containing approximately 1 million PE samples, analyzed through static analysis. Obtained malware detection results are comparable to other academic works in the current state of art and, in addition, we provide an in-depth classification of malicious samples. Models used in the pipeline provides interpretable results which can help security analysts in better understanding decisions taken by the automated pipeline.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 10 of its 42 references that are in the map. First posted on arXiv 10 Jun 2021.
Builds on
10 of 42 references.
References · 10
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
- 2018EMBER: An Open Dataset for Training Static PE Malware Machine Learning Modelscited by 164
- 2017Malware Detection by Eating a Whole EXEcited by 114
- 2020A Survey of Machine Learning Methods and Challenges for Windows Malware Classificationcited by 33
- 2015Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classificationcited by 21
- 2020AVClass2: Massive Malware Tag Extraction from AV Labelscited by 19
- 2013History of malwarecited by 3
- 2019Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection Techniquecited by 3
- 2019Automatic Malware Description via Attribute Tagging and Similarity Embeddingcited by 2
- 2020Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Securitycited by 1
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Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 0; 2025: 3.
6 papers.
- 2025EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifierscited by 18
- 2021MOTIF: A Large Malware Reference Dataset with Ground Truth Family Labelscited by 17
- 2023Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performancecited by 12
- 2025Semantic Preprocessing for LLM-based Malware Analysiscited by 3
- 2025Efficient Malware Detection with Optimized Learning on High-Dimensional Featurescited by 1
- 2023MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malwarecited by 0
- 2025Efficient Malware Detection with Optimized Learning on High-Dimensional Features18 Jun 2025
- 2025Semantic Preprocessing for LLM-based Malware Analysis13 Jun 2025
- 2025EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers5 Jun 2025
- 2023MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malware4 Sep 2023
- 2023Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance27 Jul 2023
- 2021MOTIF: A Large Malware Reference Dataset with Ground Truth Family Labels29 Nov 2021