mlsanityData to 14 Oct 2026

Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification

arXiv 1511.04317 ↗First posted 13 Nov 2015cs.CR cs.AIcited by 21

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Abstract (arXiv)

Modern malware is designed with mutation characteristics, namely polymorphism and metamorphism, which causes an enormous growth in the number of variants of malware samples. Categorization of malware samples on the basis of their behaviors is essential for the computer security community, because they receive huge number of malware everyday, and the signature extraction process is usually based on malicious parts characterizing malware families. Microsoft released a malware classification challenge in 2015 with a huge dataset of near 0.5 terabytes of data, containing more than 20K malware samples. The analysis of this dataset inspired the development of a novel paradigm that is effective in categorizing malware variants into their actual family groups. This paradigm is presented and discussed in the present paper, where emphasis has been given to the phases related to the extraction, and selection of a set of novel features for the effective representation of malware samples. Features can be grouped according to different characteristics of malware behavior, and their fusion is performed according to a per-class weighting paradigm. The proposed method achieved a very high accuracy (≈ 0.998) on the Microsoft Malware Challenge dataset.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Nov 2015.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 4; 2022: 0; 2023: 5; 2024: 5; 2025: 3; 2026: 4.

21 papers.

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  1. 2021Towards an Automated Pipeline for Detecting and Classifying Malware through Machine Learningcited by 6
  2. 2023AVScan2Vec: Feature Learning on Antivirus Scan Data for Production-Scale Malware Corporacited by 5
  3. 2023Towards a Practical Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via Randomized Smoothingcited by 5
  4. 2024Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systemscited by 5
  5. 2021A Comprehensive Study on Learning-Based PE Malware Family Classification Methodscited by 3
  6. 2021Analysis of Machine Learning Approaches to Packing Detectioncited by 2
  7. 2023New Approach to Malware Detection Using Optimized Convolutional Neural Networkcited by 1
  8. 2024Multiclass Classification Procedure for Detecting Attacks on MQTT-IoT Protocolcited by 1
  9. 2024Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Researchcited by 1
  10. 2025Adaptive Malware Detection using Sequential Feature Selection: A Dueling Double Deep Q-Network (D3QN) Framework for Intelligent Classificationcited by 1
  11. 2026Automated Malware Family Classification using Weighted Hierarchical Ensembles of Large Language Modelscited by 1
  12. 2021ML-based IoT Malware Detection Under Adversarial Settings: A Systematic Evaluationcited by 0
9 more
  1. 2023Malware Classification using Deep Neural Networks: Performance Evaluation and Applications in Edge Devicescited by 0
  2. 2023Smart OMVI: Obfuscated Malware Variant Identification using a novel datasetcited by 0
  3. 2024Obfuscated Malware Detection: Investigating Real-world Scenarios through Memory Analysiscited by 0
  4. 2024Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothingcited by 0
  5. 2025Rethinking and Exploring String-Based Malware Family Classification in the Era of LLMs and RAGcited by 0
  6. 2025Taming Data Challenges in ML-based Security Tasks Using Generative AIcited by 0
  7. 2026A Hybrid Approach For Malware Classification Using Secondary Features Fusioncited by 0
  8. 2026SoK: AI-Augmented Binary Reversingcited by 0
  9. 2026EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usabilitycited by 0