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Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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Computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： islam Zada Mohammed Naif Alatawi syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran k.nwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attack. on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack.of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,k.Nearest Neighbors(k.N),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes k.Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

Automatic Finding of Brain-Tumour Group Using CNN Segmentation and Moth-Flame-Algorithm,Selected Deep and Handcrafted Features

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Computers, Materials & Continua 2024年第5期79卷 2585-2608页

作者： Imad Saud Al Naimi syed Alwee Aljunid syed Junid Muhammad lmran Ahmad k.Suresh Manic Faculty of Electronic Engineering Technology University Malaysia Perils(UniMAP)Pauh Putra CampusArauPerlis02600Malaysia Department of Electrical and Communication Engineering National University of Science and TechnologyAL HailAL Seeb130Sultanate of Oman Department of Research and Innovation Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(SIMATS)ChennaiTamiNadu602105India

Augmentation of abnormal cells in the brain causes brain tumor(BT),and early screening and treatmentwill reduce its harshness in ***’s clinical level screening is usually performed with Magnetic Resonance Imaging(MRI)due to its multi-modality *** overall aims of the study is to introduce,test and verify an advanced image processing technique with algorithms to automatically extract tumour sections from brain MRI scans,facilitating improved *** research intends to devise a reliable framework.for detecting the BT region in the twodimensional(2D)MRI slice,and identifying its class with improved *** methodology for the devised framework.comprises the phases of:(i)Collection and resizing of images,(ii)Implementation and Segmentation of Convolutional Neural Network.CNN),(iii)Deep feature extraction,(iv)Handcrafted feature extraction,(v)Moth-Flame-Algorithm(MFA)supported feature reduction,and(vi)Performance *** study utilized clinical-grade brain MRI of BRATS and TCIA datasets for the *** framework.segments detected the glioma(low/high grade)and glioblastoma class *** work.helped to get a segmentation accuracy of over 98%with VGG-UNet and a classification accuracy of over 98%with the VGG16 *** study has confirmed that the implemented framework.is very efficient in detecting the BT in MRI slices with/without the sk.ll section.

关键词： Brain tumour VGG-UNet VGG16 moth-flame-algorithm classification

Design and Optimization of Printed Circuit Board Inductors for Wireless Power Transfer System

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Circuits and Systems 2013年第2期4卷 237-244页

作者： Ashraf B. islam syed k. islam Fahmida S. Tulip Department of Electrical Engineering and Computer Science University of Tennessee Knoxville USA

Wireless power transfer via inductive link.is becoming a popular choice as an alternate powering scheme for biomedical sensor electronics. Spiral printed circuit board (PCB) inductors are gaining attractions for wireless power transfer applications due to their various advantages over conventional inductors such as low-cost, batch fabrication, durability, manufacturability on flexible substrates, etc. In this work. design of a multi-spiral stack.d solenoidal inductor for biomedical application in 13.56 MHz band is presented. Proposed stack.ng method enhances the inductance density of the inductor for a given area. This paper reports an optimization technique for design and implementation of the PCB inductors. The proposed scheme shows higher inductance and better figure-of-merit values compared to PCB inductors reported in literature, which are desirable for wireless power transfer system.

关键词： Inductive Link Solenoid PCB Wireless Power Transfer Biomedical

A Framework.to Assess Possible Sources of Stak.holder Exploitation:A Developing Country Case

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International Journal of Disaster Risk.Science 2024年第1期15卷 32-44页

作者： k.mran Shafique syed Shams Tapan Sark.r School of Business University of Southern QueenslandSpringfeldQLD 4300Australia Business School University of QueenslandSt LuciaQLD 4072Australia

Solving complex post-disaster reconstruction challenges requires the altruistic involvement of heterogeneous stak.holder ***,small,more organized groups,such as government parastatals,private developers,and contractors often exploit large,unorganized groups,such as affected communities,leaving them more vulnerable to future *** on data collected from a case study in Pak.stan,this study proposed a framework.to assess,anticipate,and mitigate the exploitation of vulnerable stak.holders in post-disaster reconstruction *** framework.draws on influential management theories and utilizes reciprocal relationships between stak.holder attributes(power,legitimacy,and urgency),participation,and *** study also argued for non-binary treatment of stak.holder *** framework.will allow practitioners to address issues around the exploitation of stak.holder interests in future post-disaster reconstruction projects.

关键词： Collective action Pak.stan Post-disaster reconstruction-Stak.holder participation Stak.holder exploitation

Refined Anam-Net:Lightweight Deep Learning Model for Improved Segmentation Performance of Optic Cup and Disc for Glaucoma Diagnosis

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Computers, Materials & Continua 2024年第7期80卷 1381-1405页

作者： k.ursheed Aurangzeb syed Irtaza Haider Musaed Alhussein Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia

In this work.we aim to introduce some modifications to the Anam-Net deep neural network.DNN)model for segmenting optic cup(OC)and optic disc(OD)in retinal fundus images to estimate the cup-to-disc ratio(CDR).The CDR is a reliable measure for the early diagnosis of *** this study,we developed a lightweight DNN model for OC and OD segmentation in retinal fundus *** DNN model is based on modifications to Anam-Net,incorporating an anamorphic depth embedding *** reduce computational complexity,we employ a fixed filter size for all convolution layers in the encoder and decoder stages as the network.*** modification significantly reduces the number of trainable parameters,mak.ng the model lightweight and suitable for resource-constrained *** evaluate the performance of the developed model using two publicly available retinal image databases,namely RIM-ONE and *** results demonstrate promising OC segmentation performance across most standard evaluation metrics while achieving analogous results for OD *** used two retinal fundus image databases named RIM-ONE and Drishti-GS that contained 159 images and 101 retinal images,*** OD segmentation using the RIM-ONE we obtain an f1-score(F1),Jaccard coefficient(JC),and overlapping error(OE)of 0.950,0.9219,and 0.0781,***,for OC segmentation using the same databases,we achieve scores of 0.8481(F1),0.7428(JC),and 0.2572(OE).Based on these experimental results and the significantly lower number of trainable parameters,we conclude that the developed model is highly suitable for the early diagnosis of glaucoma by accurately estimating the CDR.

关键词： Refined Anam-Net parameter tuning deep learning optic cup optic disc cup-to-disc ratio glaucoma diagnosis

An efficient permutation approach for SbPN-based symmetric block.ciphers

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Cybersecurity 2024年第2期7卷 113-130页

作者： Mir Nazish M.Tariq Banday Insha syed Sheena Banday Department of Electronics and Instrumentation Technology University ofKashmirSrinagar 190006India

It is challenging to devise lightweight cryptographic primitives efficient in both hardware and software that can provide an optimum level of security to diverse Internet of Things applications running on low-end constrained ***,an effcient hardware design approach that requires some specific hardware resource may not be effcient if implemented in *** bit Permutation Network.based ciphers such as PRESENT and GIFT are effcient,lightweight cryptographic hardware design *** ciphers introduce confusion and diffu-sion by employing a 4×4 static substitution box and bit *** bit-wise permutation is realised by sim-ple rerouting,which is most cost-effective to implement in hardware,resulting in negligible power ***,this method is highly resource-consuming in software,particularly for large block.sized ciphers,with each single-bit permutation requiring multiple *** paper proposes a novel software-based design approach for permutation operation in Substitution bit Permutation Network.based ciphers using a bit-banding *** conventional permutation using bit rotation and the proposed approach have been implemented,analysed and compared for GIFT and PRESENT ciphers on ARM Cortex-M3-based LPC1768 development platform with k.IL MDk.used as an Integrated Development *** real-time performance comparison between conven-tional and the proposed approaches in terms of memory(RAM/ROM)footprint,power,energy and execution time has been carried out using ULINk.ro and ULINk.lus debug adapters for various code and speed optimisation *** proposed approach substantially reduces execution time,energy and power consumption for both PRE-SENT and GIFT ciphers,thus demonstrating the effciency of the proposed method for Substitution bit Permutation Network.based symmetric block.ciphers.

关键词： Lightweight cryptography PRESENT GIFT SbPN Cortex-M LPC1768 Bit-band memory

Conservation laws,Lie symmetries,self adjointness,and soliton solutions for the Selk.v–Schnak.nberg system

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Communications in Theoretical Physics 2024年第2期76卷 29-44页

作者： k.shif Ali Aly R Seadawy syed T R Rizvi Noor Aziz Department of Mathematics COMSATS University IslamabadLahore CampusPakistan Mathematics Department Faculty of ScienceTaibah UniversityAl-Madinah Al-Munawarah41411Saudi Arabia

In this article,we explore the famous Selk.v–Schnak.nberg(SS)system of coupled nonlinear partial differential equations(PDEs)for Lie symmetry analysis,self-adjointness,and conservation ***,miscellaneous soliton solutions lik. dark.bright,periodic,rational,Jacobian elliptic function,Weierstrass elliptic function,and hyperbolic solutions of the SS system will be achieved by a well-k.own technique called sub-ordinary differential *** these results are displayed graphically by 3D,2D,and contour plots.

关键词： Selk.v-Schnak.nberg system Lie symmetry analysis conservation laws adjointness integrability

Detection of onset of agglomeration in a bubbling fluidized bed biomass combustor using reactive Eulerian computational fluid dynamics

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Particuology 2024年第7期90卷 504-515页

作者： Abdullah Tasleem Atta Ullah Fei Li Qun Yi William Nimmo syed Sheraz Daood Department of Chemical Engineering Pakistan Institute of Engineering and Applied Sciences(PIEAS)Lehtrar RoadP.O.Nilore45650IslamabadPakistan Institute of Process Engineering Chinese Academy of SciencesBeijing100190China School of Chemical Engineering and Pharmacy Wuhan Institute of TechnologyWuhan430205China Energy Engineering Group Energy 2050Department of Mechanical EngineeringUniversity of SheffieldSheffieldS37RDUnited Kingdom Institute of Energy and Environmental Engineering Faculty of ElectricalEnergy&Environmental EngineeringUniversity of the PunjabQuaid-e-Azam CampusLahorePakistan Energy Engineering Research and Development Centre Faculty of ElectricalEnergy&Environmental EngineeringUniversity of the PunjabQuaid-e-Azam CampusLahorePakistan

The choice of a type of combustion technology to be used for heat or power generation depends on economic,technical,operational and fuel availability *** benefits associated with the evolving mark.t driven by the fluidised bed combustion(FBC)technology cannot be overlook.d especially when gauged at 65 GWth of worldwide installed capacity alongside added benefits of handling fuel variation,low pollutant emissions and high combustion *** or biomass waste will continue to have a vital role to play in the future FBC technology-based power *** often contains high levels of inorganic species that can form stick. agglomerates posing a significant risk.to boiler operation resulting in unscheduled *** added complexity of the behaviour of the fuel and bed material mix highlights the requirement for simulation models to identify agglomeration to help improve the overall performance and reliability of FBC *** resolve this problem,this research devised a simulation strategy for the detection of agglomeration using the Eulerian–Eulerian *** developed modelling strategy is validated with the experimental data available in literature for two-dimensional simplified geometry of a pilot-scale fluidised bed *** model results were found promising and robust to predict bed defluidisation times and other parameters consistent with the experimental data.

关键词： Fluidized bed combustion Eulerian Models Biomass Agglomeration Defluidisation

Enhancing Sk.n Cancer Diagnosis with Deep Learning:A Hybrid CNN-RNN Approach

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Computers, Materials & Continua 2024年第4期79卷 1497-1519页

作者： syeda Shamaila Zareen Guangmin Sun Mahwish k.ndi syed Furqan Qadri Salman Qadri Faculty of Information Technology Beijing University of TechnologyBeijing100124China Computer Science International Engineering Collage Maynooth UniversityKildareW23 F2H6Irland Research Center for Data Hub and Security Zhejiang LabHangzhou311121China Computer Science Department MNS University of AgricultureMultan59220Pakistan

Sk.n cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network.(CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance sk.n cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 sk.n lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing sk.n cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.

关键词： Sk.n cancer classification deep learning Convolutional Neural Network.CNN) RNN ResNet-50