# New Approach to Malware Detection Using Optimized Convolutional Neural Network

arXiv: https://arxiv.org/abs/2301.11161 · HTML: https://mlsanity.com/paper/2301.11161/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 60 references that are in the map.
- First posted on arXiv 26 Jan 2023.

## Builds on (most cited first, 7 of 7)

- [A Convolutional Neural Network for Modelling Sentences](https://mlsanity.com/paper/1404.2188/) (2014)
- [Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification](https://mlsanity.com/paper/1511.04317/) (2015)
- [Lightweight Classification of IoT Malware based on Image Recognition](https://mlsanity.com/paper/1802.03714/) (2018)
- [Transfer Learning for Image-Based Malware Classification](https://mlsanity.com/paper/1903.11551/) (2019)
- [Deep learning at the shallow end: Malware classification for non-domain experts](https://mlsanity.com/paper/1807.08265/) (2018)
- [Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions](https://mlsanity.com/paper/2201.00768/) (2022)
- [Classifying Malware Images with Convolutional Neural Network Models](https://mlsanity.com/paper/2010.16108/) (2020)

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

- [A Novel Approach to Malicious Code Detection Using CNN-BiLSTM and Feature Fusion](https://mlsanity.com/paper/2410.09401/) (2024)

## Abstract (arXiv)

Cyber-crimes have become a multi-billion-dollar industry in the recent years. Most cybercrimes/attacks involve deploying some type of malware. Malware that viciously targets every industry, every sector, every enterprise and even individuals has shown its capabilities to take entire business organizations offline and cause significant financial damage in billions of dollars annually. Malware authors are constantly evolving in their attack strategies and sophistication and are developing malware that is difficult to detect and can lay dormant in the background for quite some time in order to evade security controls. Given the above argument, Traditional approaches to malware detection are no longer effective. As a result, deep learning models have become an emerging trend to detect and classify malware. This paper proposes a new convolutional deep learning neural network to accurately and effectively detect malware with high precision. This paper is different than most other papers in the literature in that it uses an expert data science approach by developing a convolutional neural network from scratch to establish a baseline of the performance model first, explores and implements an improvement model from the baseline model, and finally it evaluates the performance of the final model. The baseline model initially achieves 98% accurate rate but after increasing the depth of the CNN model, its accuracy reaches 99.183 which outperforms most of the CNN models in the literature. Finally, to further solidify the effectiveness of this CNN model, we use the improved model to make predictions on new malware samples within our dataset.