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Image-Based Malware Classification Using VGG19 Network and Spatial Convolutional Attention

Mazhar Javed Awan, Osama Ahmed Masood, Mazin Abed Mohammed, Awais Yasin, Azlan Mohd Zain, Robertas Damaševičius, Karrar Hameed Abdulkareem
2021 Electronics  
In this paper, we propose a spatial attention and convolutional neural network (SACNN) based on deep learning framework for image-based classification of 25 well-known malware families with and without  ...  The results indicate that our proposed model can be used for image-based malware detection with high performance, despite being simpler as compared to other available solutions.  ...  Figure 5 . 5 Down sampling inside a convolutional neural network with pooling layer.process of down sampling in the CNN model.  ... 
doi:10.3390/electronics10192444 fatcat:ijnf3k3275gcdhae7sidiumw2i

SeqNet: An Efficient Neural Network for Automatic Malware Detection [article]

Jiawei Xu and Wenxuan Fu and Haoyu Bu and Zhi Wang and Lingyun Ying
2022 arXiv   pre-print
Malware continues to evolve rapidly, and more than 450,000 new samples are captured every day, which makes manual malware analysis impractical.  ...  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.  ...  Acknowledgements This research has been partially supported by the National Natural Science Foundation of China (61872202), the Natural Science Foundation of Tianjin (19JCYBJC15500), 2019 Tianjin New Generation  ... 
arXiv:2205.03850v1 fatcat:34ngzqhcxbatxiterwzrqicrfi

A Novel Malware Detection and Family Classification Scheme for IoT Based on DEAM and DenseNet

Changguang Wang, Ziqiu Zhao, Fangwei Wang, Qingru Li, Athanasios V. Vasilakos
2021 Security and Communication Networks  
In this paper, a new simple and effective attention module of Convolutional Neural Networks (CNNs), named as Depthwise Efficient Attention Module (DEAM), is proposed and combined with a DenseNet to propose  ...  With the rapid increase in the amount and type of malware, traditional methods of malware detection and family classification for IoT applications through static and dynamic analysis have been greatly  ...  With the development of neural networks in recent years, static analysis and dynamic analysis are often combined with neural networks for malware detection and family classification.  ... 
doi:10.1155/2021/6658842 fatcat:u2lplch3ardynn2tggpdxca7gy

MSAAM: A Multiscale Adaptive Attention Module for IoT Malware Detection and Family Classification

Changguang Wang, Ziqiu Zhao, Fangwei Wang, Qingru Li
2022 Security and Communication Networks  
By replacing the attention module in the traditional CliqueNet with the designed MSAAM, we present a new method to process the produced gray-scale images converted from the malware and thus get better  ...  unbalanced numbers of malware samples.  ...  of a convolutional neural network attention mechanism.  ... 
doi:10.1155/2022/2206917 doaj:f48720d8af8e43d8be88f826ad19911c fatcat:yb2pv5vf7rhg5lqgu5a2vubdwq

Android Malware Detection Technology Based on Lightweight Convolutional Neural Networks

Genchao Ye, Jian Zhang, Huanzhou Li, Zhangguo Tang, Tianzi Lv, Usman Habib
2022 Security and Communication Networks  
It transforms Android malware classes.dex, Androidmanifest.xml, and resource.arsc into RGB images and uses the lightweight convolutional neural network to extract the features of RGB images automatically  ...  However, most of these methods are based on the largescale convolutional neural network model (such as VGG16).  ...  the sample of this category in the neural network.  ... 
doi:10.1155/2022/8893764 fatcat:cy2fekitbffbzajklgf7rrwtd4

Android Malware Analysis and Detection Based on Attention-CNN-LSTM

Luo shiqi
2019 Journal of Computers  
According to this method, the binary executable files are transformed to represent hashes which are matched with a database of known malware samples.  ...  Dynamic analysis methods: the dynamic analysis methods of malware (such as active defense technology and cloud killing technology) have been used by more security vendors with the development of anti-malware  ...  In deep learning filed, a convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural networks, most commonly applied to analyzing visual imagery.  ... 
doi:10.17706/jcp.14.1.31-43 fatcat:v3q3ukdjsjfe5mj2ee6p7ppk7q

Visual Detection for Android Malware using Deep Learning

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Then we used the deep learning technique convolutional neural network to identify Android application as benign or as malware.  ...  Finally, the proposed model succeeds to detect the entire 200 android applications (100 benign applications and 100 malware applications) with an accuracy of over 99% as shown in experimental results.  ...  Our architecture of CNN consists of two sets of convolutions, and pooling layers, followed by a fully-connected neural network classifier.  ... 
doi:10.35940/ijitee.a8132.1110120 fatcat:ejoz2kgpnjcohaiwhoyzzaf7zi

MalCaps: A Capsule Network Based Model for the Malware Classification

Xiaoliang Zhang, Kehe Wu, Zuge Chen, Chenyi Zhang
2021 Processes  
The research on malware detection enabled by deep learning has become a hot issue in the field of network security.  ...  In this paper, we draw on the idea of image classification in the field of computer vision and propose a novel malware detection method based on capsule network architecture with hyper-parameter optimized  ...  This data can be found at: https://www.kaggle.com/c/malware-classification/data, accessed on 12 April 2021. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/pr9060929 fatcat:zes4r4biwrdyvck6bcu47pgdmy

How to Make Attention Mechanisms More Practical in Malware Classification

Xin Ma, Shize Guo, Haiying Li, Zhisong Pan, Junyang Qiu, Yu Ding, Feiqiong Chen
2019 IEEE Access  
Also, we construct a new classification framework based on attention mechanism and Convolutional Neural Networks mechanism.  ...  Machine learning has been widely used in the field of malware classification, but some emerging studies, such as attention mechanisms, are rarely applied in this field.  ...  DATA AVAILABILITY Research Data Related to this Submission Title: Microsoft 2015 contest dataset Repository: malware classification dataset url: https://www.kaggle.com/c/malware-classification/  ... 
doi:10.1109/access.2019.2948358 fatcat:6f4it2hipnenlijgycu4eo32ze

Android Malware Detection Using TCN with Bytecode Image

Wenhui Zhang, Nurbol Luktarhan, Chao Ding, Bei Lu
2021 Symmetry  
Then the image size is unified and input to the designed neural network with three different convolution methods for experimental validation.  ...  At present, the existing Android malware bytecode image detection method, based on a convolution neural network (CNN), relies on a single DEX file feature and requires a large amount of computation.  ...  detection model by applying the time series convolution neural network to detect the bytecode image of malware for the first time.  ... 
doi:10.3390/sym13071107 fatcat:etl4l5xndragdlcjdtbj47x7ju

Data Augmentation Based Malware Detection using Convolutional Neural Networks [article]

Ferhat Ozgur Catak, Javed Ahmed, Kevser Sahinbas, Zahid Hussain Khand
2020 arXiv   pre-print
by using a convolutional neural network model.  ...  This paper aims at providing an image augmentation enhanced deep convolutional neural network (CNN) models for the detection of malware families in a metamorphic malware environment.  ...  of convolutional neural network to improve image classification.  ... 
arXiv:2010.01862v1 fatcat:m65kzeod2bfp5au4bj3q67xoua

Detecting Malware with an Ensemble Method Based on Deep Neural Network

Jinpei Yan, Yong Qi, Qifan Rao
2018 Security and Communication Networks  
We perform experiments on more than 40,000 samples including 20,650 benign files collected from online software providers and 21,736 malwares provided by Microsoft.  ...  Then MalNet uses CNN and LSTM networks to learn from grayscale image and opcode sequence, respectively, and takes a stacking ensemble for malware classification.  ...  Acknowledgments This work is partially supported by the National Natural Science Foundation of China under Grant no. 61672421.  ... 
doi:10.1155/2018/7247095 fatcat:tmyysltalvhu7olp3ajynd2aqe

Review of Android Malware Detection Based on Deep Learning

Zhiqiang Wang, Qian Liu, Yaping Chi
2020 IEEE Access  
With the widespread application of deep learning in recent years, the method of detecting Android malware using deep learning has gradually attracted widespread attention from scholars at home and abroad  ...  In order to solve this problem, this study analyzes and summarizes the latest research results by investigating a large number of the latest domestic and international academic papers, summarizing malware  ...  ACKNOWLEDGMENT This research was financially supported by the Key Lab of Information Network Security, Ministry of Public Security (C19614), the Fundamental Research Funds for the Central Universities(  ... 
doi:10.1109/access.2020.3028370 fatcat:tujn3ghssrfkffzafat7l3cnse

R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections [article]

TonTon Hsien-De Huang, Hung-Yu Kao
2018 arXiv   pre-print
The convolution neural network that can learn without prior extraction of features fits well in response to the rapid iteration of Android malware.  ...  The color image is input to the convolutional neural network for automatic feature extraction and training. The data was collected from Jan. 2017 to Aug 2017.  ...  ACKNOWLEDGEMENT This work would not have been possible without the valuable dataset offered by Cheetah Mobile. Special thanks to Dr. Chia-Mu Yu for his support on this research.  ... 
arXiv:1705.04448v5 fatcat:6yp5nztkonc75jupownho7besu

Efficient Malware Classification by Binary Sequences with One-Dimensional Convolutional Neural Networks

Wei-Cheng Lin, Yi-Ren Yeh
2022 Mathematics  
Many proposed methods convert malware executables into grayscale images and apply convolutional neural networks (CNNs) for malware classification.  ...  However, converting malware executables into images could twist the one-dimensional structure of binary codes.  ...  In [32] , the authors adopt the ResNet-like network with Bi-LSTM and attention mechanism to improve the performance of the detection method.  ... 
doi:10.3390/math10040608 fatcat:bzg56ce3sbblbhqnjmalrrwajm
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