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Malware images

L. Nataraj, S. Karthikeyan, G. Jacob, B. S. Manjunath
2011 Proceedings of the 8th International Symposium on Visualization for Cyber Security - VizSec '11  
We propose a simple yet effective method for visualizing and classifying malware using image processing techniques.  ...  Malware binaries are visualized as gray-scale images, with the observation that for many malware families, the images belonging to the same family appear very similar in layout and texture.  ...  This automatic classification technique should be very valuable for antivirus companies and security researchers who receive hundreds of malware everyday.  ... 
doi:10.1145/2016904.2016908 dblp:conf/vizsec/NatarajKJM11 fatcat:7577o7jdt5h4zifuyndivjteki

Malware-on-the-Brain: Illuminating Malware Byte Codes with Images for Malware Classification [article]

Fangtian Zhong, Zekai Chen, Minghui Xu, Guoming Zhang, Dongxiao Yu, Xiuzhen Cheng
2022 arXiv   pre-print
VisMal converts malware samples into images and then applies a contrast-limited adaptive histogram equalization algorithm to enhance the similarity between malware image regions in the same family.  ...  In this paper, we propose a visualized malware classification framework called VisMal, which provides highly efficient categorization with acceptable accuracy.  ...  by providing automatic classification tools, and iii) offer an image-based validation method for identifying the effectiveness of classification.  ... 
arXiv:2108.04314v3 fatcat:fuwtqsbw75e7ziortly2k4ncoy

Imbalanced Malware Images Classification: a CNN based Approach [article]

Songqing Yue, Tianyang Wang
2022 arXiv   pre-print
Deep convolutional neural networks (CNNs) can be applied to malware binary detection via image classification.  ...  To validate the efficacy, we deploy the proposed weighted loss in a pre-trained deep CNN model and fine-tune it to achieve promising results on malware images classification.  ...  A malware binary file can be visualized to a digital gray image [3] .  ... 
arXiv:1708.08042v2 fatcat:khlgs6dzczgu7enatielywy7v4

Malware Analysis Using Visualized Image Matrices

KyoungSoo Han, BooJoong Kang, Eul Gyu Im
2014 The Scientific World Journal  
This paper proposes a novel malware visual analysis method that contains not only a visualization method to convert binary files into images, but also a similarity calculation method between these images  ...  In addition, we propose a technique that generates a representative image for each malware family in order to reduce the number of comparisons for the classification of unknown samples and the colored  ...  Acknowledgments This research project was supported by Ministry of Culture, Sports and Tourism (MCST) and from Korea Copyright 14 The Scientific World Journal Commission in 2013.  ... 
doi:10.1155/2014/132713 pmid:25133202 pmcid:PMC4124712 fatcat:mbi5pjh5v5fmfgtwjpfe4z7lai

Android Malware Detection Using TCN with Bytecode Image

Wenhui Zhang, Nurbol Luktarhan, Chao Ding, Bei Lu
2021 Symmetry  
With the rapid increase in the number of Android malware, the image-based analysis method has become an effective way to defend against symmetric encryption and confusing malware.  ...  To solve these problems, we combine the visual features of the XML file with the data section of the DEX file for the first time, and propose a new Android malware detection model, based on a temporal  ...  Figure 3 . 3 Visualizing XML and DEX files as gray images. Figure 4 . 4 Gray image with four different feature combinations. Figure 5 . 5 The classification structure of CNN.  ... 
doi:10.3390/sym13071107 fatcat:etl4l5xndragdlcjdtbj47x7ju

Deep Feature Extraction and Classification of Android Malware Images

Jaiteg Singh, Deepak Thakur, Farman Ali, Tanya Gera, Kyung Sup Kwak
2020 Sensors  
A fine-tuned Convolutional Neural Network (CNN) is used to automatically extract rich features from visualized malware thus eliminating the feature engineering and domain expert cost.  ...  The classification results showed that our method is able to achieve an accuracy of 92.59% using Android certificates and manifest malware images.  ...  necessary for malware analysis, and paves the path for the development of effective malware classification systems. • A CNN was fine-tuned to automatically extract the rich features from visualized malware  ... 
doi:10.3390/s20247013 pmid:33302430 pmcid:PMC7762531 fatcat:put2c5xzarh3jaxkjcnmzbk2re

Polymorphic Malware Detection by Image Conversion Technique

2020 International Journal of Engineering and Advanced Technology  
As the tracing is going to be difficult the detection and classification system needs to be flexible that can able to detect the malware in every possible environment.  ...  The objective is to increase the accuracy and efficiency of the detection as this malware can morph themselves, making it difficult to trace through anti-malware systems.  ...  Yes, it is possible to convert malware into the image as one of the proposed solutions has come from a research study called Malware Images: Visualization and Automatic Classification by Lakshmanan Nataraj  ... 
doi:10.35940/ijeat.b4999.029320 fatcat:c4xfsz2cczawjad7dk3fjaldee

Malware Binary Image Classification Using Convolutional Neural Networks

John Kiger, Shen-Shyang Ho, Vahid Heydari
2022 International Conference on Cyber Warfare and Security (ICIW)  
One of these cybersecurity tasks where machine learning may prove advantageous is malware analysis and classification.  ...  Furthermore, the proliferation of malicious files and new malware signatures increases year by year.  ...  malware detection and classification scheme.  ... 
doi:10.34190/iccws.17.1.59 fatcat:3xgqmm3syfe3lninuqg5dxeumu

Malware Detection Using Frequency Domain-Based Image Visualization and Deep Learning [article]

Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Satish Chikkagoudar, Shivkumar Chandrasekaran, B.S. Manjunath
2021 arXiv   pre-print
We propose a novel method to detect and visualize malware through image classification.  ...  Motivated by the visual similarity of these images for different malware families, we compare our deep neural network models with standard image features like GIST descriptors to evaluate the performance  ...  The views expressed in this paper are the opinions of the authors and do not represent official positions of the Department of the Navy.  ... 
arXiv:2101.10578v1 fatcat:zh5cxopdgjghzd7n5gfl7yfwja

Experiments with Malware Visualization [chapter]

Yongzheng Wu, Roland H. C. Yap
2013 Lecture Notes in Computer Science  
Visualization No definite answers: Different anti-virus software give different classificationsMalware comparison, classification and clustering is not well defined -Sharing & Evolution: Reusable  ...  components and complex co-evolution history -• Can visualization show relationships between malware?  ... 
doi:10.1007/978-3-642-37300-8_7 fatcat:ml2szmgj6zajvmu5pgf4prwvpi

High-Accuracy Malware Classification with a Malware-Optimized Deep Learning Model [article]

Rikima Mitsuhashi, Takahiro Shinagawa
2020 arXiv   pre-print
Although there have been many reports that applied CNN to malware visualization image classification, it has not been revealed how to pick out a model that fits a given malware dataset and achieves higher  ...  We propose a strategy to select a Deep learning model that fits the malware visualization images.  ...  Malware Visualization Approach As for the method of malware classification using malware visualization image, many researchers have reported results using the Malimg dataset. Nataraj et al.  ... 
arXiv:2004.05258v1 fatcat:6t5ukov5kncc7iszvgsxlm5ds4

Using Static and Dynamic Malware features to perform Malware Ascription [article]

Jashanpreet Singh Sraw, Keshav Kumar
2021 arXiv   pre-print
Malware ascription is a relatively unexplored area, and it is rather difficult to attribute malware and detect authorship.  ...  We leverage Cuckoo Sandbox and machine learning to make progress in this research. Post analysis, classification is performed using various deep learning and machine learning algorithms.  ...  Manjunath, “Malware images: visualization and automatic classification,” in 2011 International Symposium on Visualization for Cyber Security, VizSec ’11, Pittsburgh, PA, USA, July 20, 2011,  ... 
arXiv:2112.02639v1 fatcat:63y3buhsbbh65mlvdzwmpmgqqu

Malware Visualization Techniques

Ahmet EFE, Saleh Hussin S. HUSSİN
2020 International Journal of Applied Mathematics Electronics and Computers  
This paper aims to provide insights into the malware visualization techniques and its applications, most common malware types and the extracted features that used to identify the malware are demonstrated  ...  The results clarify the importance of visualization techniques and which are the most common malware as well as the most useful features.  ...  Images: Image-based technique Visualizing malware executables as grayscale images. they use visual charts to recognize an image for every malware sample [27] .  ... 
doi:10.18100/ijamec.526813 fatcat:54lixfrqxrdrlnnqmbtfpvmdve

Classification and Analysis of Android Malware Images Using Feature Fusion Technique

Jaiteg Singh, Deepak Thakur, Tanya Gera, Babar Shah, Tamer Abuhmed, Farman Ali
2021 IEEE Access  
ACKNOWLEDGMENT Jaiteg Singh and Farman Ali contributed equally and are co-first authors.  ...  Deploying a visualization-based technique, a malware variant can be visualized as an image. An image can capture even small changes.  ...  The adopted methodology would study raw bytes of malware code visualized as an image.  ... 
doi:10.1109/access.2021.3090998 fatcat:2rwq6muatnahddw6wgbjsutkxi

CNN Model to Classify Malware Using Image Feature
이미지 기능을 사용하여 맬웨어를 분류하는 CNN 모델

Espoir K. Kamundala, Chang Hoon Kim
2018 KIISE Transactions on Computing Practices  
Malware programs are common threats in the information and technology society.  ...  In this paper, we use Convolutional Neural Network to classify malware binaries using image features.  ...  The importance converting malware binary into an image is that different section of a binary can be easily seen when it is visualized, malware of same family look alike when seen as images and are different  ... 
doi:10.5626/ktcp.2018.24.5.256 fatcat:pemdhh62bne5vc6ckdufxcaiza
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