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Dynamic deception-based technique for crypto-ransomware detection

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International Journal of Security and Networks 2024年第4期19卷 210-223页

作者： Jagannathan, Kiruthika Sheen, Shina Department of Applied Mathematics and Computational Sciences PSG College of Technology Tamil Nadu 641004 India

Recently, deception techniques have attracted substantial attention in the research community and are emerging as a proactive attack detection and defence mechanism against ransomware. We have contributed a tool that thwarts crypto-ransomware action in Linux environment, using a file-based dynamic deception mechanism. This tool deploys and monitors decoy files in minimal locations in the file system by taking into consideration the existing ransomware behaviour and file traversal patterns. It also dynamically deploys new decoys or updates existing ones in response to legitimate file system changes, ensuring that any ransomware begins its encryption on a monitored decoy, effectively neutralising the threat. We assessed our tool using few existing Linux ransomware samples and creating new ones, as detailed in the experimental evaluation. The results indicate that the tool can thwart the crypto-ransomware samples at an early stage of encryption, as they begin encryption from a monitored decoy, thus saving original files. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Phishing

English-Chinese Translation using Neural Machine Models

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English-Chinese Translation using Neural Machine Models

2022 IEEE Conference on Telecommunications, Optics and Computer Science, TOCS 2022

作者： Liu, Qi Ou, Pengfei University of Washington Applied and Computational Mathematical Sciences Program Seattle United States University of Illinois at Urbana Champaign Department of Mathematics Urbana United States

ISBN: (纸本)9781665470537

During the last few years, neural machine translation (NMT) as a notable branch of machine translation has been increasing its popularity both in research and in practice. In particular, neural machine translation between English and Chinese, which are two of the most widely used languages all over the world, is receiving more and more attention. In this review, we discuss current mainstream models for neural machine translation, including recurrent neural network (RNN), convolution neural network (CNN), and self-attention network (SAN) or transformer. The mechanisms of these models are illustrated. Moreover, examples of studies and applications of them are analyzed. In addition, comparisons on the performance of different models are implemented. © 2022 IEEE.

关键词： Neural machine translation

An Extreme Learning Machine-Based Method for computational PDEs in Higher Dimensions

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arXiv 2023年

作者： Wang, Yiran Dong, Suchuan Center for Computational & Applied Mathematics Department of Mathematics Purdue University United States

We present two effective methods for solving high-dimensional partial differential equations (PDE) based on randomized neural networks. Motivated by the universal approximation property of this type of networks, both methods extend the extreme learning machine (ELM) approach from low to high dimensions. With the first method the unknown solution field in d dimensions is represented by a randomized feed-forward neural network, in which the hidden-layer parameters are randomly assigned and fixed while the output-layer parameters are trained. The PDE and the boundary/initial conditions, as well as the continuity conditions (for the local variant of the method), are enforced on a set of random interior/boundary collocation points. The resultant linear or nonlinear algebraic system, through its least squares solution, provides the trained values for the network parameters. With the second method the high-dimensional PDE problem is reformulated through a constrained expression based on an Approximate variant of the Theory of Functional Connections (A-TFC), which avoids the exponential growth in the number of terms of TFC as the dimension increases. The free field function in the A-TFC constrained expression is represented by a randomized neural network and is trained by a procedure analogous to the first method. We present ample numerical simulations for a number of high-dimensional linear/nonlinear stationary/dynamic PDEs to demonstrate their performance. These methods can produce accurate solutions to high-dimensional PDEs, in particular with their errors reaching levels not far from the machine accuracy for relatively lower dimensions. Compared with the physics-informed neural network (PINN) method, the current method is both cost-effective and more accurate for high-dimensional PDEs. Copyright © 2023, The Authors. All rights reserved.

关键词： Computation theory

Stability and Related Zero Viscosity Limitof Steady Plane Poiseuille-Couette Flows with No-Slip Boundary Condition

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SSRN

SSRN 2024年

作者： Jiang, Song Zhou, Chunhui Department of Mathematics Southeast University Nanjing210096 China Institute of Applied Physics and Computational Mathematics Beijing China

We study the existence and stability of smooth solutions to the steady Navier-Stokes equations near plane Poiseuille-Couetteflow in the absence of any external force in two-dimensional domain $\Omega=(0,L)\times (0,2)... 详细信息

关键词： Shear flow

Multidimensional WENO-AO Reconstructions Using a Simplified Smoothness Indicator and Applications to Conservation Laws

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Journal of Scientific Computing 2023年第1期97卷 1-29页

作者： Huang, Chieh-Sen Arbogast, Todd Tian, Chenyu Department of Applied Mathematics National Sun Yat-sen University Kaohsiung804 Taiwan Department of Mathematics C1200 University of Texas AustinTX78712–1202 United States Oden Institute for Computational Engineering and Sciences C0200 University of Texas AustinTX78712–1229 United States

Finite volume, weighted essentially non-oscillatory (WENO) schemes require the computation of a smoothness indicator. This can be expensive, especially in multiple space dimensions. We consider the use of the simple smoothness indicator σS=1NS-1∑j(u¯j-u¯m)2 , where NS is the number of mesh elements in the stencil, u¯ j is the local function average over mesh element j, and index m gives the target element. Reconstructions utilizing standard WENO weighting fail with this smoothness indicator. We develop a modification of WENO-Z weighting that gives a reliable and accurate reconstruction of adaptive order, which we denote as SWENOZ-AO. We prove that it attains the order of accuracy of the large stencil polynomial approximation when the solution is smooth, and drops to the order of the small stencil polynomial approximations when there is a jump discontinuity in the solution. Numerical examples in one and two space dimensions on general meshes verify the approximation properties of the reconstruction. They also show it to be about 10 times faster in two space dimensions than reconstructions using the classic smoothness indicator. The new reconstruction is applied to define finite volume schemes to approximate the solution of hyperbolic conservation laws. Numerical tests show results of the same quality as standard WENO schemes using the classic smoothness indicator, but with an overall speedup in the computation time of about 3.5–5 times in 2D tests. Moreover, the computational efficiency (CPU time versus error) is noticeably improved. © 2023, The Author(s).

关键词： Polynomial approximation

Long distance propagation of wave beams in paraxial regime

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arXiv 2024年

作者： Bal, Guillaume Nair, Anjali Departments of Statistics and Mathematics Committee on Computational and Applied Mathematics University of Chicago ChicagoIL60637 United States Department of Statistics Committee on Computational and Applied Mathematics University of Chicago ChicagoIL60637 United States

This paper concerns the propagation of high frequency wave-beams in highly turbulent atmospheres. Using a paraxial model of wave propagation, we show in the long-distance weak-coupling regime that the wavefields are approximately described by a complex Gaussian field whose scintillation index is unity. This provides a model of the speckle formation observed in many practical settings. The main step of the derivation consists in showing that closed-form moment equations in the Itô-Schrödinger regime are still approximately satisfied in the paraxial regime. The rest of the proof is then an extension of results derived in [5]. Copyright © 2024, The Authors. All rights reserved.

关键词： Wave propagation

Complex Gaussianity of long-distance random wave processes

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arXiv 2024年

作者： Bal, Guillaume Nair, Anjali Departments of Statistics and Mathematics and Committee on Computational and Applied Mathematics University of Chicago ChicagoIL60637 United States Department of Statistics and Committee on Computational and Applied Mathematics University of Chicago ChicagoIL60637 United States

Interference of randomly scattered classical waves naturally leads to familiar speckle patterns, where the wave intensity follows an exponential distribution while the wave field itself is described by a circularly symmetric complex normal distribution. In the Itô-Schrödinger paraxial model of wave beam propagation, we demonstrate how a deterministic incident beam transitions to such a fully developed speckle pattern over long distances in the so-called scintillation (weak-coupling) regime. Copyright © 2024, The Authors. All rights reserved.

关键词： Normal distribution

Long distance propagation of light in random media with partially coherent sources

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arXiv 2024年

Optical beam propagation in random media is characterized by familiar speckle patterns generated by intricate interference effects. Such patterns may be modified and possibly attenuated for partially coherent incident beam profiles. In the weak-coupling regime of the Itô-Schrödinger paraxial model of wave propagation, we show how the spatio-temporal statistics of the partially coherent beams interact with the statistics of the random medium to enhance or suppress scintillation effects. Copyright © 2024, The Authors. All rights reserved.

关键词： Scintillation

Shape of my heart: Cardiac models through learned signed distance functions 7

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Shape of my heart: Cardiac models through learned signed dis...

7th International Conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Verhülsdonk, Jan Grandits, Thomas Costabal, Francisco Sahli Pinetz, Thomas Krause, Rolf Auricchio, Angelo Haase, Gundolf Pezzuto, Simone Effland, Alexander Institute for Applied Mathematics University of Bonn Germany Department of Mathematics and Scientific Computing University of Graz Austria Institute for Biological and Medical Engineering Pontificia Universidad Católica de Chile Chile Center for Computational Medicine in Cardiology Università della Svizzera Italiana Switzerland FernUni Schweiz Brig Switzerland Instituto Cardiocentro Ticino EOC Switzerland Department of Mathematics University of Trento Italy

The efficient construction of anatomical models is one of the major challenges of patient-specific in-silico models of the human heart. Current methods frequently rely on linear statistical models, allowing no advanced topological changes, or requiring medical image segmentation followed by a meshing pipeline, which strongly depends on image resolution, quality, and modality. These approaches are therefore limited in their transferability to other imaging domains. In this work, the cardiac shape is reconstructed by means of three-dimensional deep signed distance functions with Lipschitz regularity. For this purpose, the shapes of cardiac MRI reconstructions are learned to model the spatial relation of multiple chambers. We demonstrate that this approach is also capable of reconstructing anatomical models from partial data, such as point clouds from a single ventricle, or modalities different from the trained MRI, such as the electroanatomical mapping (EAM). © 2024 CC-BY 4.0, J. Verhülsdonk et al.

关键词： Image segmentation