HIT4Mal: Hybrid image transformation for malware classification

Vu, Duc-Ly; Nguyen, Trong-Kha; Nguyên, Tam; Nguyen, Tu N.; Massacci, Fabio; Phung, Phu H.

doi:10.1002/ett.3789

Cited by 64 publications

(20 citation statements)

References 50 publications

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“…The texture feature of an image describes the arrangement of the components of each object in the image, and it is a periodically changing visual element. There are three commonly used texture feature extraction methods, which are statistics-based texture feature extraction method, signal processing-based texture feature extraction method, and structure-based feature extraction method [17][18][19][20]. The research object of the texture feature extraction method based on statistics is the gray value of the current pixel, and the extracted texture feature is the first or higher derivative statistical information of gray.…”

Section: Feature Extractionmentioning

confidence: 99%

Automatic Semantic Annotation of News Images in Mobile Internet of Things and Construction of Semantic Internet of Things System

Wang

Ling

Chen

2022

Preprint

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With the continuous development of mobile and semantic IoT (Internet of Things), the number of news images is increasing sharply. Automatic semantic annotation technology is an important tool to manage and retrieve news images. Therefore, the study is focused on the automatic semantic annotation of news images. First, the problems of the existing automatic annotation methods of news images are analyzed and studied, and then an automatic semantic annotation method of news images based on the fusion of weight and features is proposed. The proposed method extracts the colour features, texture features, and shape features of news images to determine their stability by using the standard deviation of features, and then calculates the weighting coefficient to realize automatic annotation of the news images. The results of the study in the corel5k dataset show that the accuracy of the weight-feature fusion annotation model is 60% for the colour feature, 62% for the texture feature, and 67% for the mixture of colour, shape, and texture. The accuracy of the fusion of weight and feature annotation method is 7% higher than that of the single feature annotation method. The maximum accuracy of the fusion of weight and feature annotation method is 91.2%. The study provides a reference for the research of automatic annotation of news images.

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Section: Feature Extractionmentioning

confidence: 99%

Automatic Semantic Annotation of News Images in Mobile Internet of Things and Construction of Semantic Internet of Things System

Wang

Ling

Chen

2022

Preprint

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“…In [ 10 , 11 , 12 ], the YOLO object detection is being used. It focused on face mask recognition as well as maintaining a certain distance in crowded places.…”

Section: Related Workmentioning

confidence: 99%

Face mask recogniser using image processing and computer vision approach

Sharadhi

Gururaj

Shankar

et al. 2022

Global Transitions Proceedings

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“…It is possible to analyze a sample without running it through a process known as static analysis. In contrast, dynamic analysis is the process of executing a sample to determine its behavior like how it performed in different environments [ 23 ]. But we used a different approach; we have analyzed malware files by converting executable malware files into grayscale images, so in this way, there is no harm to our system, which does not need to execute the file.…”

Section: Related Workmentioning

confidence: 99%

Digital Forensics for Malware Classification: An Approach for Binary Code to Pixel Vector Transition

Naeem

Amin

Alshamrani

et al. 2022

Computational Intelligence and Neuroscience

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The most often reported danger to computer security is malware. Antivirus company AV-Test Institute reports that more than 5 million malware samples are created each day. A malware classification method is frequently required to prioritize these occurrences because security teams cannot address all of that malware at once. Malware’s variety, volume, and sophistication are all growing at an alarming rate. Hackers and attackers routinely design systems that can automatically rearrange and encrypt their code to escape discovery. Traditional machine learning approaches, in which classifiers learn based on a hand-crafted feature vector, are ineffective for classifying malware. Recently, deep convolutional neural networks (CNNs) successfully identified and classified malware. To categorize malware, a smart system has been suggested in this research. A novel model of deep learning is introduced to categorize malware families and multiclassification. The malware file is converted to a grayscale picture, and the image is then classified using a convolutional neural network. To evaluate the performance of our technique, we used a Microsoft malware dataset of 10,000 samples with nine distinct classifications. The findings stood out among the deep learning models with 99.97% accuracy for nine malware types.

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HIT4Mal: Hybrid image transformation for malware classification

Cited by 64 publications

References 50 publications

Automatic Semantic Annotation of News Images in Mobile Internet of Things and Construction of Semantic Internet of Things System

Automatic Semantic Annotation of News Images in Mobile Internet of Things and Construction of Semantic Internet of Things System

Face mask recogniser using image processing and computer vision approach

Digital Forensics for Malware Classification: An Approach for Binary Code to Pixel Vector Transition

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