scholarly journals A Multi-class Detection System for Android Malicious Apps Based on Color Image Features

2021 ◽  
pp. 186-206
Author(s):  
Hua Zhang ◽  
Jiawei Qin ◽  
Boan Zhang ◽  
Hanbing Yan ◽  
Jing Guo ◽  
...  
Keyword(s):  
Color Image ◽  
Detection System ◽  
Image Features ◽  
Malicious Apps

scholarly journals A Multiclass Detection System for Android Malicious Apps Based on Color Image Features

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Hua Zhang ◽  
Jiawei Qin ◽  
Boan Zhang ◽  
Hanbing Yan ◽  
Jing Guo ◽  
...  
Keyword(s):  
Color Image ◽  
Detection System ◽  
Binary Classification ◽  
Image Features ◽  
Detection Accuracy ◽  
Visualization Method ◽  
Android Apps ◽  
Malicious Apps ◽  
Color Visualization ◽  
Grayscale Images

The visual recognition of Android malicious applications (Apps) is mainly focused on the binary classification using grayscale images, while the multiclassification of malicious App families is rarely studied. If we can visualize the Android malicious Apps as color images, we will get more features than using grayscale images. In this paper, a method of color visualization for Android Apps is proposed and implemented. Based on this, combined with deep learning models, a multiclassifier for the Android malicious App families is implemented, which can classify 10 common malicious App families. In order to better understand the behavioral characteristics of malicious Apps, we conduct a comprehensive manual analysis for a large number of malicious Apps and summarize 1695 malicious behavior characteristics as customized features. Compared with the App classifier based on the grayscale visualization method, it is verified that the classifier using the color visualization method can achieve better classification results. We use four types of Android App features: classes.dex file, sets of class names, APIs, and customized features as input for App visualization. According to the experimental results, we find out that using the customized features as the color visualization input features can achieve the highest detection accuracy rate, which is 96% in the ten malicious families.

scholarly journals Malware Detection Based on Code Visualization and Two-Level Classification

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 118
Author(s):  
Vassilios Moussas ◽  
Antonios Andreatos
Keyword(s):  
Web Applications ◽  
Detection System ◽  
Malware Detection ◽  
Image Features ◽  
Malicious Code ◽  
Malware Analysis ◽  
Malicious Software ◽  
Antivirus Software ◽  
Malware Classification ◽  
Artificial Neural Network Ann

Malware creators generate new malicious software samples by making minor changes in previously generated code, in order to reuse malicious code, as well as to go unnoticed from signature-based antivirus software. As a result, various families of variations of the same initial code exist today. Visualization of compiled executables for malware analysis has been proposed several years ago. Visualization can greatly assist malware classification and requires neither disassembly nor code execution. Moreover, new variations of known malware families are instantly detected, in contrast to traditional signature-based antivirus software. This paper addresses the problem of identifying variations of existing malware visualized as images. A new malware detection system based on a two-level Artificial Neural Network (ANN) is proposed. The classification is based on file and image features. The proposed system is tested on the ‘Malimg’ dataset consisting of the visual representation of well-known malware families. From this set some important image features are extracted. Based on these features, the ANN is trained. Then, this ANN is used to detect and classify other samples of the dataset. Malware families creating a confusion are classified by a second level of ANNs. The proposed two-level ANN method excels in simplicity, accuracy, and speed; it is easy to implement and fast to run, thus it can be applied to antivirus software, smart firewalls, web applications, etc.

scholarly journals A Multi-Model Stereo Similarity Function Based on Monogenic Signal Analysis in Poisson Scale Space

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Jinjun Li ◽  
Hong Zhao ◽  
Chengying Shi ◽  
Xiang Zhou
Keyword(s):  
Color Image ◽  
Image Features ◽  
Image Feature ◽  
Similarity Function ◽  
Local Orientation ◽  
Stereo Images ◽  
Interest Points ◽  
Monogenic Signal ◽  
Local Color ◽  
Instantaneous Phase

A stereo similarity function based on local multi-model monogenic image feature descriptors (LMFD) is proposed to match interest points and estimate disparity map for stereo images. Local multi-model monogenic image features include local orientation and instantaneous phase of the gray monogenic signal, local color phase of the color monogenic signal, and local mean colors in the multiscale color monogenic signal framework. The gray monogenic signal, which is the extension of analytic signal to gray level image using Dirac operator and Laplace equation, consists of local amplitude, local orientation, and instantaneous phase of 2D image signal. The color monogenic signal is the extension of monogenic signal to color image based on Clifford algebras. The local color phase can be estimated by computing geometric product between the color monogenic signal and a unit reference vector in RGB color space. Experiment results on the synthetic and natural stereo images show the performance of the proposed approach.

scholarly journals Creating Color Image Features Using Local Contrast Method

2018 ◽  
Vol 7 (3) ◽  
pp. 367-376
Author(s):  
Ayman Al-Rawashdeh ◽  
Ziad Al-Qadi
Keyword(s):  
Image Recognition ◽  
Color Image ◽  
Digital Processing ◽  
Image Features ◽  
Local Contrast ◽  
Image Size ◽  
Data Types ◽  
Memory Space ◽  
Contrast Method ◽  
Image Pixels

Digital color images are now one of the most popular data types used in the digital processing environment. Color image recognition plays an important role in many vital applications, which makes the enhancement of image recognition or retrieval system an important issue. Using color image pixels to recognize or retrieve the image, but the issue of the huge color image size that requires accordingly more time and memory space to perform color image recognition and/or retrieval. In the current study, image local contrast was used to create local contrast victor, which was then used as a key to recognize or retrieve the image. The proposed local contrast method was properly implemented and tested. The obtained results proved its efficiency as compared with other methods.

scholarly journals A Colour Perception Detection Model Based on The Spectral Characterictics

2019 ◽  
Vol 4 (2) ◽  
pp. 87
Author(s):  
Irwan Anto Mina
Keyword(s):  
Information Needs ◽  
Euclidean Distance ◽  
Color Image ◽  
Detection System ◽  
Color Perception ◽  
Blue Color ◽  
Colour Perception ◽  
Detection Tool ◽  
Average Color

<p><em>Information needs for one's color perception are needed in the fields of medicine, engineering, astronomy, biomedicine and so on. The demand for accurate assessment of color perception must be met by the perception detection tool used. Ishihara's test, as a perception detection tool that is still used today has insufficient accuracy. This research aims to create a system that can detect a shift in one's color perception, relative to the average color perception of a number of respondents. Through plotting the respondents' perception points, in the CIE coordinate system (Commission International de I'Eclairage) XYZ can be calculated the average euclidean distance, ED, relative to the reference point and the distribution of x and y groups of perception points around the point of reference. Both size, euclidean distance and distribution are used as indicators of average color perception so that an assessment of one's color perception is given based on the results of comparison between color perception points and color perception indicators. The tool used to do the test is Delphi version 7.0 software. the research material used is the RGB (Red, Green, Blue) color image format. The results of a person's color perception study are divided into three levels, namely: (1) "normal" assessment if euclidean (ED) perceptions are smaller than the euclidean (ED) average (2) the "somewhat normal" assessment if the distribution of x and y is smaller rather than the color of perception and the distribution of x and y (3) the assessment is "abnormal" if the color of perception is greater than the max distribution of x and y. A new perception point assessment that is in level one is used to up-date prevailing perception indicators. Up-dating condition constraints affect the quality of the threshold average perception specifically and the quality of the results of the perception detection system in general.</em></p>

scholarly journals Skin Cancer Recognition Using SVM Image Processing Technique

2020 ◽  
pp. 11-15
Author(s):  
Rahul Chand Thakur ◽  
◽  
Vaibhav Panwar ◽  
Keyword(s):  
Image Processing ◽  
Skin Cancer ◽  
Supervised Learning ◽  
Detection System ◽  
Early Stage ◽  
Image Features ◽  
The Body ◽  
Image Processing Technique ◽  
Support Vector ◽  
Skin Cells

Skin cancer is considered as commonest cause of death among humans in today's world. This type of cancer shows non uniform or patchy growth of skin cells that most commonly occurs on of the certain parts of body which are more likely exposed to the light, but it can occur anywhere on the body. The majority of skin cancers can be treated if detected early. As a result, finding skin cancer early and easily will save a patient's life. Early detection of skin cancer at an early stage is now possible thanks to modern technologies. Biopsy procedure [1] is a systematic method for diagnosis skin cancer. It is achieved by extracting skin cells, after which the sample is sent to different laboratories for examination. It's a very long (in terms of time) and painful process. For primitive detection of skin cancer disease, we proposed a skin cancer detection system based on svm. It is more helpful to patients. Various methods of image processing and the supervised learning algorithm called Support Vector Machine (SVM) are used in the identification process. Epiluminescence microscopy is taken using an image and particular to several preprocessing techniques which are used in the reduction of sound artifacts and improvise quality of images. Segmentation is done by using certain thresholding techniques like OTSU. The GLCM technique must be used to remove certain image features. These characteristics are fed into the classifier as input. The Supervised learning model called (SVM) is used to distinguish data sets. It determines whether a picture is cancerous or not.

Snow Detection using In-Vehicle Video Camera with Texture-Based Image Features Utilizing K-Nearest Neighbor, Support Vector Machine, and Random Forest

2019 ◽  
Vol 2673 (8) ◽  
pp. 221-232 ◽  
Author(s):  
Md Nasim Khan ◽  
Mohamed M. Ahmed
Keyword(s):  
Real Time ◽  
Prediction Accuracy ◽  
Nearest Neighbor ◽  
Detection System ◽  
Weather Conditions ◽  
Video Camera ◽  
Image Features ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Weather Information

Snowfall negatively affects pavement and visibility conditions, making it one of the major causes of motor vehicle crashes in winter weather. Therefore, providing drivers with real-time roadway weather information during adverse weather is crucial for safe driving. Although road weather stations can provide weather information, these stations are expensive and often do not represent real-time trajectory-level weather information. The main motivation of this study was to develop an affordable in-vehicle snow detection system which can provide trajectory-level weather information in real time. The system utilized SHRP2 Naturalistic Driving Study video data and was based on machine learning techniques. To train the snow detection models, two texture-based image features including gray level co-occurrence matrix (GLCM) and local binary pattern (LBP), and three classification algorithms: support vector machine (SVM), k-nearest neighbor (K-NN), and random forest (RF) were used. The analysis was done on an image dataset consisting of three weather conditions: clear, light snow, and heavy snow. While the highest overall prediction accuracy of the models based on the GLCM features was found to be around 86%, the models considering the LBP based features provided a much higher prediction accuracy of 96%. The snow detection system proposed in this study is cost effective, does not require a lot of technical support, and only needs a single video camera. With the advances in smartphone cameras, simple mobile apps with proper data connectivity can effectively be used to detect roadway weather conditions in real time with reasonable accuracy.

scholarly journals Improving Wavelet Threshold De-noising Applied on Parts Detection

2018 ◽  
Vol 232 ◽  
pp. 04084
Author(s):  
Lizhou Cheng ◽  
Jian Mao ◽  
Hongyuan Wei
Keyword(s):  
Wavelet Transform ◽  
Noise Reduction ◽  
Detection System ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Image Features ◽  
Image Feature ◽  
Threshold Function ◽  
Image Noise Reduction ◽  
The Impact

The traditional filtering methods such as median filter and mean filter always blurrs image features, resulting in poor noise reduction effect. Wavelet transform has unique adaptability due to its variable resolution, which can better implement wavelet denoising on the basis of image feature. Aiming at the shortcoming of traditional wavelet transform threshold denoising, based on the hard threshold and soft threshold function, this paper proposes improved adaptive thresholding function. By comparing and validating, this method obtains the smaller mean square error (MSE) and higher peak signal to noise ratio. Meanwhile, this method improves the quality of detection images, and reduces the impact on images brought by noise from external enviroment and internal system. So, this can be applied to image noise reduction of the detection system.

Study of Enhancement Technology of Color Image Based on Adaptation and Nonlinear

2011 ◽  
Vol 267 ◽  
pp. 234-240
Author(s):  
Tai Le Peng ◽  
You Dong Ding ◽  
Chang Jie Zhu
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Sliding Window ◽  
Threshold Value ◽  
Image Features ◽  
Color Images ◽  
Local Characteristic ◽  
Median Filtering ◽  
Color Image Enhancement ◽  
Enhancement Technology

Loss of image features are leaded by Regions with too bright or too dark of image with noise. An adaptive and nonlinear algorithm for color image enhancement is proposed in the paper. which consists of five stages: Converting color images to grayscale images; Threshold value is ascertained by HVS and position of noise is ascertained by method of Sliding Window; According to local characteristic of position of noise, median filtering is executed by weighted template; The following thing is that image is separated into apart; At last image is adaptively enhanced according to idea of Retinex and color of image is reverted. Experiments show that ability of de-noising can be effectively improved by the algorithm and contrast of image can be improved.

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