# A novel DL approach to PE malware detection: exploring Glove vectorization, MCC_RCNN and feature fusion

arXiv: https://arxiv.org/abs/2101.08969 · HTML: https://mlsanity.com/paper/2101.08969/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 22 Jan 2021.

## Builds on (most cited first, 6 of 6)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Features](https://mlsanity.com/paper/1508.03096/) (2015)
- [Recent Advances in Deep Learning: An Overview](https://mlsanity.com/paper/1807.08169/) (2018)
- [Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware Classification](https://mlsanity.com/paper/1801.00318/) (2017)
- [Sequence Modeling using Gated Recurrent Neural Networks](https://mlsanity.com/paper/1501.00299/) (2015)

## Built on by (most cited first, 1 of 1)

- [VecLSTM: Trajectory Data Processing and Management for Activity Recognition through LSTM Vectorization and Database Integration](https://mlsanity.com/paper/2409.19258/) (2024)

## Abstract (arXiv)

In recent years, malware becomes more threatening. Concerning the increasing malware variants, there comes Machine Learning (ML)-based and Deep Learning (DL)-based approaches for heuristic detection. Nevertheless, the prediction accuracy of both needs to be improved. In response to the above issues in the PE malware domain, we propose the DL-based approaches for detection and use static-based features fed up into models. The contributions are as follows: we recapitulate existing malware detection methods. That is, we propose a vec-torized representation model of the malware instruction layer and semantic layer based on Glove. We implement a neural network model called MCC_RCNN (Malware Detection and Recurrent Convolutional Neural Network), comprising of the combination with CNN and RNN. Moreover, we provide a description of feature fusion in static behavior levels. With the numerical results generated from several comparative experiments towards evaluating the Glove-based vectoriza-tion, MCC_RCNN-based classification methodology and feature fusion stages, our proposed classification methods can obtain a higher prediction accuracy than the other baseline methods.