SeqNet: An Efficient Neural Network for Automatic Malware Detection
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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.
Key facts. Cited by 1 paper 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
31 of 62 references.
References · 31
- 2017Attention Is All You Needcited by 60,361
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
- 2013Visualizing and Understanding Convolutional Networkscited by 2,290
- 2016Adversarial examples in the physical worldcited by 1,769
- 2019On the Relationship between Self-Attention and Convolutional Layerscited by 300
- 2020On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Locationcited by 142
19 more
- 2021BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learningcited by 123
- 2017Malware Detection by Eating a Whole EXEcited by 115
- 2018TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Timecited by 108
- 2018Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executablescited by 62
- 2015Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Featurescited by 48
- 2016Adversarial Perturbations Against Deep Neural Networks for Malware Classificationcited by 42
- 2019Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networkscited by 40
- 2020Overinterpretation reveals image classification model pathologiescited by 28
- 2020DSRNA: Differentiable Search of Robust Neural Architecturescited by 27
- 2021Delving into Data: Effectively Substitute Training for Black-box Attackcited by 27
- 2020Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detectioncited by 24
- 2019Dynamic Malware Analysis with Feature Engineering and Feature Learningcited by 23
- 2019On Training Robust PDF Malware Classifierscited by 19
- 2021Get a Model! Model Hijacking Attack Against Machine Learning Modelscited by 14
- 2020SIGL: Securing Software Installations Through Deep Graph Learningcited by 11
- 2021Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Modelscited by 11
- 2020A Performance-Sensitive Malware Detection System Using Deep Learning on Mobile Devicescited by 9
- 2020DeepMAL -- Deep Learning Models for Malware Traffic Detection and Classificationcited by 8
- 2017Android Malware Detection using Deep Learning on API Method Sequencescited by 4
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