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Structural features with nonnegative matrix factorization for metamorphic malware detection

COMPUTERS & SECURITY 2021年 104卷

作者： Ling, Yeong Tyng Sani, Nor Fazlida Mohd Abdullah, Mohd Taufik Hamid, Nor Asilah Wati Abdul Univ Malaysia Sarawak Fac Comp Sci & Informat Technol Sarawak Malaysia Univ Putra Malaysia Fac Comp Sci & Informat Technol Dept Comp Sci Serdang Selangor Malaysia

Metamorphic malware is well known for evading signature-based detection by exploiting various code obfuscation techniques. Current metamorphic malware detection approaches require some prior knowledge during feature engineering stage to extract patterns and behaviors from malware. In this paper, we attempt to complement and extend previous techniques by proposing a metamorphic malware detection approach based on structure analysis by using information theoretic measures and statistical metrics with machine learning model. In particular, compression ratio, entropy, Jaccard coefficient and Chi-square tests are used as feature representations to reveal the byte information existing in malware binary file. Furthermore, by using nonnegative matrix factorization, feature dimension can be reduced. The experimental results show the Jaccard coefficient on hexadecimal byte as feature representation is effective for Windows metamorphic malware detection with an accuracy rate and F-score as high as 0.9972 and 0.9958, respectively. Whereas for Linux morphed malware detection, the Chi-square statistic test shows as effective feature representation with an accuracy rate and F-score as high as 0.9878 and 0.9901, respectively. Overall, the proposed feature representations and the technique of dimension reduction can be useful for detecting metamorphic malware. ? 2021 Elsevier Ltd. All rights reserved.

关键词： Metamorphic malware Compression ratio Entropy Jaccard coefficient Chi-square nonnegative matrix factorization

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Maximum Likelihood Estimation Based nonnegative matrix factorization for Hyperspectral Unmixing

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REMOTE SENSING 2021年第13期13卷

作者： Jiang, Qin Dong, Yifei Peng, Jiangtao Yan, Mei Sun, Yi Hubei Univ Fac Math & Stat Hubei Key Lab Appl Math Wuhan 430062 Peoples R China Anhui Univ Finance & Econ Sch Finance Bengbu 233030 Peoples R China

Hyperspectral unmixing (HU) is a research hotspot of hyperspectral remote sensing technology. As a classical HU method, the nonnegative matrix factorization (NMF) unmixing method can decompose an observed hyperspectral data matrix into the product of two nonnegative matrices, i.e., endmember and abundance matrices. Because the objective function of NMF is the traditional least-squares function, NMF is sensitive to noise. In order to improve the robustness of NMF, this paper proposes a maximum likelihood estimation (MLE) based NMF model (MLENMF) for unmixing of hyperspectral images (HSIs), which substitutes the least-squares objective function in traditional NMF by a robust MLE-based loss function. Experimental results on a simulated and two widely used real hyperspectral data sets demonstrate the superiority of our MLENMF over existing NMF methods.

关键词： hyperspectral unmixing maximum likelihood estimation nonnegative matrix factorization

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PIXEL-WISE LINEAR/NONLINEAR nonnegative matrix factorization FOR UNMIXING OF HYPERSPECTRAL DATA

PIXEL-WISE LINEAR/NONLINEAR NONNEGATIVE MATRIX FACTORIZATION...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhu, Fei Honeine, Paul Chen, Jie Tianjin Univ Ctr Appl Math Tianjin 300072 Peoples R China Normandie Univ UNIROUEN LITIS F-76000 Rouen France Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China

ISBN: (纸本)9781509066315

Nonlinear spectral unmixing is a challenging and important task in hyperspectral image analysis. The kernel-based bi-objective nonnegative matrix factorization (Bi-NMF) has shown its usefulness in nonlinear unmixing;However, it suffers several issues that prohibit its practical application. In this work, we propose an unsupervised nonlinear unmixing method that overcomes these weaknesses. Specifically, the new method introduces into each pixel a parameter that adjusts the nonlinearity therein. These parameters are jointly optimized with endmembers and abundances, using a carefully designed objective function by multiplicative update rules. Experiments on synthetic and real datasets confirm the effectiveness of the proposed method.

关键词： Hyperspectral data analysis nonlinear unmixing unsupervised learning kernel methods nonnegative matrix factorization

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Unsupervised Robust Speech Enhancement Based on Alpha-Stable Fast Multichannel nonnegative matrix factorization 21

Unsupervised Robust Speech Enhancement Based on Alpha-Stable...

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Interspeech Conference

作者： Fontaine, Mathieu Sekiguchi, Kouhei Nugraha, Aditya Arie Yoshii, Kazuyoshi RIKEN AIP Tokyo Japan Kyoto Univ Grad Sch Informat Kyoto Japan

ISBN: (纸本)9781713820697

This paper describes multichannel speech enhancement based on a probabilistic model of complex source spectrograms for improving the intelligibility of speech corrupted by undesired noise. The univariate complex Gaussian model with the reproductive property supports the additivity of source complex spectrograms and forms the theoretical basis of nonnegative matrix factorization (NMF). Multichannel NMF (MNMF) is an extension of NMF based on the multivariate complex Gaussian model with spatial covariance matrices (SCMs), and its state-of-theart variant called FastMNMF with jointly-diagonalizable SCMs achieves faster decomposition based on the univariate Gaussian model in the transformed domain where all time-frequencychannel elements are independent. Although a heavy-tailed extension of FastMNMF has been proposed to improve the robustness against impulsive noise, the source additivity has never been considered. The multivariate alpha-stable distribution does not have the reproductive property for the shape matrix parameter. This paper, therefore, proposes a heavy-tailed extension called alpha-stable FastMNMF which works in the transformed domain to use a univariate complex ff-stable model, satisfying the reproductive property for any tail lightness parameter ff and allowing the alpha-fractional Wiener filtering based on the element-wise source additivity. The experimental results show that alpha-stable FastMNMF with alpha= 1:8 significantly outperforms Gaussian FastMNMF (alpha=2).

关键词： speech enhancement nonnegative matrix factorization alpha-stable distribution joint diagonalization

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Adaptive graph-based discriminative nonnegative matrix factorization for image clustering

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2021年 95卷

作者： Zhang, Ying Li, Xiangli Jia, Mengxue Guilin Univ Elect Technol Sch Math & Comp Sci Guilin 541004 Peoples R China Guangxi Key Lab Automat Testing Technol & Instrum Guilin 541004 Peoples R China Guangxi Univ Key Lab Data Anal & Calculat Guilin 541004 Peoples R China

nonnegative matrix factorization(NMF) is an effective dimension reduction method, which is widely used in image clustering and other fields. Some NMF variants preserve the manifold structure of the original data. However, the construction of the traditional neighbor graph depends on the original data, so it may be affected by noise and outliers. Moreover, these methods are unsupervised and do not use available label information. Therefore, this paper presents an adaptive graph-based discriminative nonnegative matrix factorization(AGDNMF). AGDNMF uses the available label to construct the label matrix, such that the new representations with the same label data are aligned to the same axis. And the neighbor graph in AGDNMF is obtained by adaptive iterations. A number of experiments on many image data sets verify that AGDNMF is effective compared with the other state-of-the-art methods.

关键词： nonnegative matrix factorization nonnegative Adaptive graph regularization Semi-supervised learning

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Identification of multi-layer networks community by fusing nonnegative matrix factorization and topological structural information

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KNOWLEDGE-BASED SYSTEMS 2021年 213卷

作者： Ma, Changzhou Lin, Qiang Lin, Yong Ma, Xiaoke Xidian Univ Sch Comp Sci & Technol Xian Shaanxi Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China Northwest Minzu Univ Sch Math & Comp Sci Lanzhou Gansu Peoples R China Northwest Minzu Univ Minist Educ Key Lab Chinas Ethn Languages & Informat Technol Lanzhou Gansu Peoples R China Ningxia Normal Univ Sch Phys & Elect Informat Engn Guyuan Ningxia Peoples R China

The accumulated multi-layer networks in nature and society provide a great opportunity for revealing the mechanisms of the underlying complex systems with multiple types of interactions. Community detection in multi-layer networks aims to extract well-connected groups of vertices for all layers, which shed light into revealing the structure-function relations. The current algorithms either exploit the topological structure of multi-layer networks or explore the latent features of networks, which are criticized due to their low accuracy because they ignore the relation among various layers. To attack these problems, a novel algorithm for Multi-layer community detection by using joint nonnegative matrix factorization (MjNMF) is proposed, which simultaneously considers the topological structure and relations of layers. Specifically, MjNMF extracts features of vertices for each layer by simultaneously factorizing the adjacency matrices of all layers with a common basis matrix, where features of vertices preserve the topological structure of all layers. To obtain community structure, MjNMF decomposes the similarity matrices of vertices for all layers in concern. The smoothness strategy is adopted to connect features of various layers with community structure by learning a project matrix for each layer. Finally, MjNMF integrates feature extraction, community detection, and smoothness by formulating an overall objective function, and derives the optimization rules. The experimental results on ten multi-layer networks demonstrate the proposed algorithm significantly outperforms thirteen state-of-the-art methods in terms of various measurements. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Multi-layer networks Community detection nonnegative matrix factorization Joint learning

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Unregistered Hyperspectral and Multispectral Image Fusion with Synchronous nonnegative matrix factorization 3rd

Unregistered Hyperspectral and Multispectral Image Fusion wi...

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3rd Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Chen, Wenjing Lu, Xiaoqiang Chinese Acad Sci Xian Inst Opt & Precis Mech Key Lab Spectral Imaging Technol CAS Xian 710119 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9783030606329;9783030606336

Recently, many methods have been proposed to generate a high spatial resolution (HR) hyperspectral image (HSI) by fusing HSI and multispectral image (MSI). Most methods need a precondition that HSI and MSI are well registered. However, in practice, it is hard to acquire registered HSI and MSI. In this paper, a synchronous nonnegative matrix factorization (SNMF) is proposed to directly fuse unregistered HSI and MSI. The proposed SNMF does not require the registration operation by modeling the abundances of unregistered HSI and MSI independently. Moreover, to exploit both HSI and MSI in the endmember optimization of the desired HR HSI, the unregistered HSI and MSI fusion is formulated as a bound-constrained optimization problem. A synchronous projected gradient method is proposed to solve this bound-constrained optimization problem. Experiments on both simulated and real data demonstrate that the proposed SNMF outperforms the state-of-the-art methods.

关键词： Image fusion nonnegative matrix factorization Hyperspectral image Multispectral image

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Efficient Clustering Using nonnegative matrix factorization for Gene Expression Dataset 1

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3rd International Conference on Advanced Computing and Intelligent Engineering (ICACIE)

作者： Kherwa, Pooja Bansal, Poonam Singh, Sukhvinder Gupta, Tanishaq Maharaja Surajmal Inst Technol C-4 Janak Puri New Delhi 110058 India

ISBN: (数字)9789811510816

ISBN: (纸本)9789811510816;9789811510809

Gene expression dataset consists of a complex association of gene patterns consisting of tens or hundreds samples. Finding relevant biological information for different tasks from this complex data is really a tedious job. Text mining approaches like classification and clustering are used in the literature to discover relevant aspects of dataset for many biological applications. Gene expression also contains irrelevant data known as noise. In this paper for efficient clustering results, a very powerful dimension reduction technique is presented as preprocessing step to improve clustering results and also cluster the gene expression samples into relevant classes. In this study, the concept of nonnegative matrix factorization and non-smooth nonnegative matrix factorization, which is an extended algorithm of the basic-NNMF algorithm is used for sparser matrix factorization, and the factorization differences are observed. Later on, the performance and the accuracy of K-means, NNMF, and NS-NNMF are compared, and NS-NNMF has shown highest accuracy.

关键词： Dimension reduction Gene expression nonnegative matrix factorization Clustering

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Robust semi-supervised nonnegative matrix factorization for image clustering

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PATTERN RECOGNITION 2021年 111卷

作者： Peng, Siyuan Ser, Wee Chen, Badong Lin, Zhiping Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China

nonnegative matrix factorization (NMF) is a powerful dimension reduction method, and has received increasing attention in various practical applications. However, most traditional NMF based algorithms are sensitive to noisy data, or fail to fully utilize the limited supervised information. In this paper, a novel robust semi-supervised NMF method, namely correntropy based semi-supervised NMF (CSNMF), is proposed to solve these issues. Specifically, CSNMF adopts a correntropy based loss function instead of the squared Euclidean distance (SED) in constrained NMF to suppress the influence of non-Gaussian noise or outliers contaminated in real world data, and simultaneously uses two types of supervised information, i.e., the pointwise and pairwise constraints, to obtain the discriminative data representation. The proposed method is analyzed in terms of convergence, robustness and computational complexity. The relationships between CSNMF and several previous NMF based methods are also discussed. Extensive experimental results show the effectiveness and robustness of CSNMF in image clustering tasks, compared with several state-of-the-art methods. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： nonnegative matrix factorization Supervised information Correntropy Outliers Image clustering

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CONSTRAINED nonnegative matrix factorization FOR HYPERSPECTRAL CHANGE DETECTION

CONSTRAINED NONNEGATIVE MATRIX FACTORIZATION FOR HYPERSPECTR...

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Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS)

作者： Erturk, Alp Kocaeli Univ Lab Image & Signal Proc Kocaeli Turkey

ISBN: (数字)9781728121901

ISBN: (纸本)9781728121901

This paper presents an unmixing based change detection (UBCD) approach based on constrained nonnegative matrix factorization (NMF) for hyperspectral images. UBCD provides not only multi-output change detection, but also subpixel level information about the nature of the changes that occur in the scene. The proposed method utilizes constrained NMF with the sparsity constraint for the abundances and the minimum volume constraint for the endmembers, reducing the solution space for the matrix factorization and resulting in enhanced unmixing and change detection performance. The change detection output is obtained in terms of the temporal abundance matrix differences for each endmember, The proposed method is evaluated on synthetic and real multitemporal datasets.

关键词： Change detection hyperspectral multitemporal nonnegative matrix factorization unmixing

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