Modeling correlation between android permissions based on threat and protection level using exploratory factor plane analysis

The evolution of mobile technology has increased correspondingly with the number of attacks on mobile devices. Malware attack on mobile devices is one of the top security challenges the mobile community faces daily. While malware classification and detection tools are being developed to fight malware infection, hackers keep deploying different infection strategies, including permissions usage. Among mobile platforms, Android is the most targeted by malware because of its open OS and popularity. Permissions is one of the major security techniques used by Android and other mobile platforms to control device resources and enhance access control. In this study, we used the t-Distribution stochastic neighbor embedding (t-SNE) and Self-Organizing Map techniques to produce a visualization method using exploratory factor plane analysis to visualize permissions correlation in Android applications. Two categories of datasets were used for this study: the benign and malicious datasets. Dataset was obtained from Contagio, VirusShare, VirusTotal, and Androzoo repositories. A total of 12,267 malicious and 10,837 benign applications with different categories were used. We demonstrate that our method can identify the correlation between permissions and classify Android applications based on their protection and threat level. Our results show that every permission has a threat level. This signifies those permissions with the same protection level have the same threat level.PTDF/ED/PHD/AMA/1245/17/17: Petroleum Technology Development Fund (PTDF)

Denoising Berbasis Estimasi Varian Noise Adaptif pada Koefisien Wavelet Homogen

Noise selalu menjadi masalah bagi citra baik secara domain spasial maupun domain frekuensi. Denoising adalah upaya untuk mengatasi noise. Banyak teknik yang dilakukan untuk denoising sehingga denoising masih menjadi topik penelitian yang terus diupayakan. Homogenitas koe�sien wavelet adalah salah satu teknik denoising melalui pengkalisi�kasian koe�sien wavelet menjadi dua jenis yaitu koe�sien sinyal dan koe�sien terkontaminasi noise menggu- nakan ant colony optimization (ACO) berbasis klasi�kasi sebelum dilakukan estimasi terhadap koe�sien wavelet tersebut. Estimasi varian noise pada teknik homogenitas ini menggunakan median absolute deviation (MAD), namun estimasi berbasis MAD dikhawatirkan melewatkan koe�sien noise yang memiliki yang lebih besar daripada nilai hasil estimasi dan detil sinyal yang lebih kecil daripada nilai estimasi. Estimasi varian adaptif yang merupakan kombinasi antara estimasi MAD dengan threshold adaptif yang dioperasikan pada setiap blok citra di dalam setiap level penguraian wavelet diusulkan untuk mengurangi resiko kesalahan estimasi varian noise berbasis MAD. Percobaan pada metode yang diusulkan dilakukan dengan menggunakan metode AntShrink sebagai metode pembanding dan tiga basis wavelet yaitu Daubechies, Haar, dan Symllet dengan standar deviasi �n 10, 25, 50, dan 75. Hasil percobaan menunjukkan kualitas citra hasil denoising pada metode yang diusulkan masih kalah unggul dibandingkan hasil dari metode AntShrink secara visual. Pada sebagian besar hasil percobaan yang dilakukan menggu- nakan pengukuran PSNR dan SSIM, hasil dari metode yang diusulkan unggul terhadap metode AntShrink terutama pada �n > 50. Selain itu, degradasi nilai hasil pengukuran yang relatif kecil pada metode yang diusukan membuktikan bahwa metode yang diusulkan memiliki ketahanan terhadap kasus noise yang besar. namun pada pengukuran NQM, hasil denoising dari metode Antshrink selalu unggul dibandingkan hasil denoising pada metode yang diusulkan. ============================================================================================================== Noise has been a problem for image on spatial domain or frequency domain. Denoising is tbhe way to tackle the problem that is occured by noise. There are too many denoising techniques so denoising methods are still being sought untill now. Homogenous classi�cation for wavelet coe�cients is one of denoising method kind which is classifying wavelet coe�ents become two kind of coe�cients, signal coe�ecients and noisy coe�ecients by using ant colony optimization (ACO) based classi�cation. The noise variance estimation on this method uses median absolute deviation (MAD). But, MAD passes noisy coe�cients which have larger number than estimation value and signal details which have smaller number than estimation value. Adaptive noise variance estimation which is combination of MAD and adaptive thresholding is proposed to decrease the risk of estimation error form MAD. The experiment for proposed method uses AntShrink as comparator method and three wavelet bases such as Daubechies, Haar, and Symmlet with standart deviation �n 10, 25, 50, 75. The experiment result shows that the denoised image from AntShrink outperforms the denoised image from proposed method visually. On PSNR and SSIM measurement, denoised image from proposed method outperforms the denoised image from AntShrink on �n > 50. Beside that, the degradation of resulting value of proposed method shows that the proposed method has better denoising quality than AntShrink on big noise case. But on NQM measurement, denoising result on AntShrink outperforms the priposed method

Denoising Citra Berbasis Estimasi Varian Noise Adaptif Pada Koefisien Wavelet Homogen

Noise selalu menjadi masalah bagi citra baik secara domain spasial maupun domain frekuensi. Denoising adalah upaya untuk mengatasi noise. Banyak teknik yang dilakukan untuk denoising sehingga denoising masih menjadi topik penelitian yang terus diupayakan. Homogenitas koefisien wavelet adalah salah satu teknik denoising melalui pengkalisifikasian koefisien wavelet menjadi dua jenis yaitu koefisien sinyal dan koefisien terkontaminasi noise menggu- nakan ant colony optimization (ACO) berbasis klasifikasi sebelum dilakukan estimasi terhadap koefisien wavelet tersebut. Estimasi varian noise pada teknik homogenitas ini menggunakan median absolute deviation (MAD), namun estimasi berbasis MAD dikhawatirkan melewatkan koefisien noise yang memiliki yang lebih besar daripada nilai hasil estimasi dan detil sinyal yang lebih kecil daripada nilai estimasi. Estimasi varian adaptif yang merupakan kombinasi antara estimasi MAD dengan threshold adaptif yang dioperasikan pada setiap blok citra di dalam setiap level penguraian wavelet diusulkan untuk mengurangi resiko kesalahan estimasi varian noise berbasis MAD. Percobaan pada metode yang diusulkan dilakukan dengan menggunakan metode AntShrink sebagai metode pembanding dan tiga basis wavelet yaitu Daubechies, Haar, dan Symllet dengan standar deviasi �n 10, 25, 50, dan 75. Hasil percobaan menunjukkan kualitas citra hasil denoising pada metode yang diusulkan masih kalah unggul dibandingkan hasil dari metode AntShrink secara visual. Pada sebagian besar hasil percobaan yang dilakukan menggu- nakan pengukuran PSNR dan SSIM, hasil dari metode yang diusulkan unggul terhadap metode AntShrink terutama pada �n > 50. Selain itu, degradasi nilai hasil pengukuran yang relatif kecil pada metode yang diusukan membuktikan bahwa metode yang diusulkan memiliki ketahanan terhadap kasus noise yang besar. namun pada pengukuran NQM, hasil denoising dari metode Antshrink selalu unggul dibandingkan hasil denoising pada metode yang diusulkan. ====================================================================================================== Noise has been a problem for image on spatial domain or frequency domain. Denoising is tbhe way to tackle the problem that is occured by noise. There are too many denoising techniques so denoising methods are still being sought untill now. Homogenous classification for wavelet coeficients is one of denoising method kind which is classifying wavelet coefients become two kind of coeficients, signal coefiecients and noisy coefiecients by using ant colony optimization (ACO) based classification. The noise variance estimation on this method uses median absolute deviation (MAD). But, MAD passes noisy coeficients which have larger number than estimation value and signal details which have smaller number than estimation value. Adaptive noise variance estimation which is combination of MAD and adaptive thresholding is proposed to decrease the risk of estimation error form MAD. The experiment for proposed method uses AntShrink as comparator method and three wavelet bases such as Daubechies, Haar, and Symmlet with standart deviation �n 10, 25, 50, 75. The experiment result shows that the denoised image from AntShrink outperforms the denoised image from proposed method visually. On PSNR and SSIM measurement, denoised image from proposed method outperforms the denoised image from AntShrink on �n > 50. Beside that, the degradation of resulting value of proposed method shows that the proposed method has better denoising quality than AntShrink on big noise case. But on NQM measurement, denoising result on AntShrink outperforms the priposed method

AUTOMATED ESTIMATION, REDUCTION, AND QUALITY ASSESSMENT OF VIDEO NOISE FROM DIFFERENT SOURCES

Estimating and removing noise from video signals is important to increase either the visual quality of video signals or the performance of video processing algorithms such as compression or segmentation where noise estimation or reduction is a pre-processing step. To estimate and remove noise, effective methods use both spatial and temporal information to increase the reliability of signal extraction from noise. The objective of this thesis is to introduce a video system having three novel techniques to estimate and reduce video noise from different sources, both effectively and efficiently and assess video quality without considering a reference non-noisy video. The first (intensity-variances based homogeneity classification) technique estimates visual noise of different types in images and video signals. The noise can be white Gaussian noise, mixed Poissonian- Gaussian (signal-dependent white) noise, or processed (frequency-dependent) noise. The method is based on the classification of intensity-variances of signal patches in order to find homogeneous regions that best represent the noise signal in the input signal. The method assumes that noise is signal-independent in each intensity class. To find homogeneous regions, the method works on the downsampled input image and divides it into patches. Each patch is assigned to an intensity class, whereas outlier patches are rejected. Then the most homogeneous cluster is selected and its noise variance is considered as the peak of noise variance. To account for processed noise, we estimate the degree of spatial correlation. To account for temporal noise variations a stabilization process is proposed. We show that the proposed method competes related state-of-the-art in noise estimation. The second technique provides solutions to remove real-world camera noise such as signal-independent, signal-dependent noise, and frequency-dependent noise. Firstly, we propose a noise equalization method in intensity and frequency domain which enables a white Gaussian noise filter to handle real noise. Our experiments confirm the quality improvement under real noise while white Gaussian noise filter is used with our equalization method. Secondly, we propose a band-limited time-space video denoiser which reduces video noise of different types. This denoiser consists of: 1) intensity-domain noise equalization to account for signal dependency, 2) band-limited anti-blocking time-domain filtering of current frame using motion-compensated previous and subsequent frames, 3) spatial filtering combined with noise frequency equalizer to remove residual noise left from temporal filtering, and 4) intensity de-equalization to invert the first step. To decrease the chance of motion blur, temporal weights are calculated using two levels of error estimation; coarse (blocklevel) and fine (pixel-level). We correct the erroneous motion vectors by creating a homography from reliable motion vectors. To eliminate blockiness in block-based temporal filter, we propose three ideas: interpolation of block-level error, a band-limited filtering by subtracting the back-signal beforehand, and two-band motion compensation. The proposed time-space filter is parallelizable to be significantly accelerated by GPU. We show that the proposed method competes related state-ofthe- art in video denoising. The third (sparsity and dominant orientation quality index) technique is a new method to assess the quality of the denoised video frames without a reference (clean frames). In many image and video applications, a quantitative measure of image content, noise, and blur is required to facilitate quality assessment, when the ground-truth is not available. We propose a fast method to find the dominant orientation of image patches, which is used to decompose them into singular values. Combining singular values with the sparsity of the patch in the transform domain, we measure the possible image content and noise of the patches and of the whole image. To measure the effect of noise accurately, our method takes both low and high textured patches into account. Before analyzing the patches, we apply a shrinkage in the transform domain to increase the contrast of genuine image structure. We show that the proposed method is useful to select parameters of denoising algorithms automatically in different noise scenarios such as white Gaussian and real noise. Our objective and subjective results confirm the correspondence between the measured quality and the ground-truth and proposed method rivals related state-of-the-art approaches