# SeqNet: An Efficient Neural Network for Automatic Malware Detection

arXiv: https://arxiv.org/abs/2205.03850 · HTML: https://mlsanity.com/paper/2205.03850/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 62 references that are in the map.
- First posted on arXiv 8 May 2022.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [On the Relationship between Self-Attention and Convolutional Layers](https://mlsanity.com/paper/1911.03584/) (2019)
- [On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location](https://mlsanity.com/paper/2003.07064/) (2020)
- [BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learning](https://mlsanity.com/paper/2108.00352/) (2021)
- [Malware Detection by Eating a Whole EXE](https://mlsanity.com/paper/1710.09435/) (2017)
- [TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time](https://mlsanity.com/paper/1807.07838/) (2018)
- [Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables](https://mlsanity.com/paper/1803.04173/) (2018)
- [Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Features](https://mlsanity.com/paper/1508.03096/) (2015)
- [Adversarial Perturbations Against Deep Neural Networks for Malware Classification](https://mlsanity.com/paper/1606.04435/) (2016)
- [Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks](https://mlsanity.com/paper/1904.08554/) (2019)
- [Overinterpretation reveals image classification model pathologies](https://mlsanity.com/paper/2003.08907/) (2020)
- [DSRNA: Differentiable Search of Robust Neural Architectures](https://mlsanity.com/paper/2012.06122/) (2020)
- [Delving into Data: Effectively Substitute Training for Black-box Attack](https://mlsanity.com/paper/2104.12378/) (2021)
- [Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detection](https://mlsanity.com/paper/2012.09390/) (2020)
- [Dynamic Malware Analysis with Feature Engineering and Feature Learning](https://mlsanity.com/paper/1907.07352/) (2019)
- [On Training Robust PDF Malware Classifiers](https://mlsanity.com/paper/1904.03542/) (2019)
- [Get a Model! Model Hijacking Attack Against Machine Learning Models](https://mlsanity.com/paper/2111.04394/) (2021)
- [SIGL: Securing Software Installations Through Deep Graph Learning](https://mlsanity.com/paper/2008.11533/) (2020)
- [Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models](https://mlsanity.com/paper/2106.05505/) (2021)
- [A Performance-Sensitive Malware Detection System Using Deep Learning on Mobile Devices](https://mlsanity.com/paper/2005.04970/) (2020)
- [DeepMAL -- Deep Learning Models for Malware Traffic Detection and Classification](https://mlsanity.com/paper/2003.04079/) (2020)

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

- [The State-of-the-Art in AI-Based Malware Detection Techniques: A Review](https://mlsanity.com/paper/2210.11239/) (2022)

## Abstract (arXiv)

Malware continues to evolve rapidly, and more than 450,000 new samples are captured every day, which makes manual malware analysis impractical. However, existing deep learning detection models need manual feature engineering or require high computational overhead for long training processes, which might be laborious to select feature space and difficult to retrain for mitigating model aging. Therefore, a crucial requirement for a detector is to realize automatic and efficient detection. In this paper, we propose a lightweight malware detection model called SeqNet which could be trained at high speed with low memory required on the raw binaries. By avoiding contextual confusion and reducing semantic loss, SeqNet maintains the detection accuracy when reducing the number of parameters to only 136K. We demonstrate the effectiveness of our methods and the low training cost requirement of SeqNet in our experiments. Besides, we make our datasets and codes public to stimulate further academic research.