# Malware Classification Using Deep Boosted Learning

arXiv: https://arxiv.org/abs/2107.04008 · HTML: https://mlsanity.com/paper/2107.04008/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 40 references that are in the map.
- First posted on arXiv 8 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [A Survey of the Recent Architectures of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1901.06032/) (2019)
- [Lightweight Classification of IoT Malware based on Image Recognition](https://mlsanity.com/paper/1802.03714/) (2018)
- [Deep learning at the shallow end: Malware classification for non-domain experts](https://mlsanity.com/paper/1807.08265/) (2018)
- [Classification and Region Analysis of COVID-19 Infection using Lung CT Images and Deep Convolutional Neural Networks](https://mlsanity.com/paper/2009.08864/) (2020)
- [Transfer Learning and Meta Classification Based Deep Churn Prediction System for Telecom Industry](https://mlsanity.com/paper/1901.06091/) (2019)
- [HeNet: A Deep Learning Approach on Intel^circledR Processor Trace for Effective Exploit Detection](https://mlsanity.com/paper/1801.02318/) (2018)
- [Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection Technique](https://mlsanity.com/paper/1910.10958/) (2019)

## Built on by (most cited first, 5 of 5)

- [IoT Malware Detection Architecture using a Novel Channel Boosted and Squeezed CNN](https://mlsanity.com/paper/2202.04121/) (2022)
- [RS-FME-SwinT: A Novel Feature Map Enhancement Framework Integrating Customized SwinT with Residual and Spatial CNN for Monkeypox Diagnosis](https://mlsanity.com/paper/2410.01216/) (2024)
- [CE-RS-SBCIT A Novel Channel Enhanced Hybrid CNN Transformer with Residual, Spatial, and Boundary-Aware Learning for Brain Tumor MRI Analysis](https://mlsanity.com/paper/2508.17128/) (2025)
- [Small Object Detection using Deep Learning](https://mlsanity.com/paper/2201.03243/) (2022)
- [Comparative Analysis of Imbalanced Malware Byteplot Image Classification using Transfer Learning](https://mlsanity.com/paper/2310.02742/) (2023)

## Abstract (arXiv)

Malicious activities in cyberspace have gone further than simply hacking machines and spreading viruses. It has become a challenge for a nations survival and hence has evolved to cyber warfare. Malware is a key component of cyber-crime, and its analysis is the first line of defence against attack. This work proposes a novel deep boosted hybrid learning-based malware classification framework and named as Deep boosted Feature Space-based Malware classification (DFS-MC). In the proposed framework, the discrimination power is enhanced by fusing the feature spaces of the best performing customized CNN architectures models and its discrimination by an SVM for classification. The discrimination capacity of the proposed classification framework is assessed by comparing it against the standard customized CNNs. The customized CNN models are implemented in two ways: softmax classifier and deep hybrid learning-based malware classification. In the hybrid learning, Deep features are extracted from customized CNN architectures and fed into the conventional machine learning classifier to improve the classification performance. We also introduced the concept of transfer learning in a customized CNN architecture based malware classification framework through fine-tuning. The performance of the proposed malware classification approaches are validated on the MalImg malware dataset using the hold-out cross-validation technique. Experimental comparisons were conducted by employing innovative, customized CNN, trained from scratch and fine-tuning the customized CNN using transfer learning. The proposed classification framework DFS-MC showed improved results, Accuracy: 98.61%, F-score: 0.96, Precision: 0.96, and Recall: 0.96.