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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 Kanwal 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 attacks 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(KNN),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

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UltraSegNet:A Hybrid Deep Learning Framework for Enhanced Breast Cancer Segmentation and Classification on Ultrasound Images

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computers, Materials & Continua 2025年第5期83卷 3303-3333页

作者： Suhaila Abuowaida Hamza Abu Owida Deema Mohammed Alsekait Nawaf Alshdaifat Diaa Salama Abd Elminaam Mohammad Alshinwan Department of Computer Science Faculty of Prince Al-Hussein Bin Abdallah II for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Medical Engineering Department Faculty of EngineeringAl-Ahliyya Amman UniversityAmman19328Jordan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Faculty of Computers Science Misr International UniversityCairo11800Egypt Jadara Research Center Jadara UniversityIrbid21110Jordan

Segmenting a breast ultrasound image is still challenging due to the presence of speckle noise,dependency on the operator,and the variation of image *** paper presents the UltraSegNet architecture that addresses these challenges through three key technical innovations:This work adds three things:(1)a changed ResNet-50 backbone with sequential 3×3 convolutions to keep fine anatomical details that are needed for finding lesion boundaries;(2)a computationally efficient regional attention mechanism that works on high-resolution features without using a transformer’s extra memory;and(3)an adaptive feature fusion strategy that changes local and global featuresbasedonhowthe image isbeing *** evaluation on two distinct datasets demonstrates UltraSegNet’s superior performance:On the BUSI dataset,it obtains a precision of 0.915,a recall of 0.908,and an F1 score of *** the UDAIT dataset,it achieves robust performance across the board,with a precision of 0.901 and recall of ***,these improvements are achieved at clinically feasible computation times,taking 235 ms per image on standard GPU ***,UltraSegNet does amazingly well on difficult small lesions(less than 10 mm),achieving a detection accuracy of *** is a huge improvement over traditional methods that have a hard time with small-scale features,as standard models can only achieve 0.63–0.71 *** improvement in small lesion detection is particularly crucial for early-stage breast cancer *** from this work demonstrate that UltraSegNet can be practically deployable in clinical workflows to improve breast cancer screening accuracy.

关键词： Breast cancer ultrasound image segmentation classification deep learning

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Hybrid Global Optimization Algorithm for Feature Selection

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computers, Materials & Continua 2023年第1期74卷 2021-2037页

作者： Ahmad Taher Azar Zafar Iqbal Khan Syed Umar Amin Khaled M.Fouad Faculty of Computers and Artificial Intelligence Benha UniversityBenha13511Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Technology and Computer Science Nile UniversitySheikh ZaidEgypt

This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing into the combined power of TVAC(Time-Variant Acceleration Coefficients)and IW(Inertial Weight).Proposed algorithm has been tested against linear,non-linear,traditional,andmultiswarmbased optimization *** experimental study is performed in two stages to assess the proposed *** I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIWPSO *** based Particle Swarm Optimization(PSO)algorithms,TVAC based PSO algorithms,traditional PSO,Genetic algorithms(GA),Differential evolution(DE),and,finally,Flower Pollination(FP)*** phase II,the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT(BA)and Multi-Swarm BAT *** phase III,the proposed PLTVACIW-PSO is employed to augment the feature selection problem formedical *** experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable *** from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features.

关键词： Particle swarm optimization(PSO) time-variant acceleration coefficients(TVAC) genetic algorithms differential evolution feature selection medical data

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Exploring Knowledge Landscapes: Clustering and Topic Modeling of Sebha University Scientific Publications 4

Exploring Knowledge Landscapes: Clustering and Topic Modelin...

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4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Abdelhamed, Roqyia Essgaer, Mansour Sebha University Faculty of Science Department of Computer Sciences Sebha Libya Sebha University Faculty of Information Technology Department of Information Technology Sebha Libya

ISBN: (纸本)9798350372632

The continuous and exponential growth in scientific publications poses a challenge in manually sorting and searching for specific topics within a university's publications. Recent advancements in artificial intelligence, natural language processing, and clustering methods, particularly the K-Means, have shown satisfactory performance in handling large volumes of textual data. Furthermore, topic modeling methods, notably praised for their effectiveness in organizing textual documents. This study focuses on evaluating the performance of the K-Means and Latent Dirichlet Allocation in clustering Sebha University scientific publications. Different term weighting is employed namely term frequency inverse document frequency, and Word2Vec to represent the tokens. K-Means demonstrated notable performance when coupled with Word2vec, as indicated by metrics such as the Davies-Bouldin Index, Silhouette Score, Calinski-Harabasz Index, and Inertia. The best performance was observed when using term frequency inverse document frequency with the Entropy metric. The evaluation metrics for K-Means did not unanimously agree on the optimal number of clusters. In contrast, Latent Dirichlet Allocation consistently performed well, with all measurements agreeing on the best results with the smallest number of clusters. Among the various combinations tested, Latent Dirichlet Allocation paired with Word2vec produced the best results across metrics, demonstrating superior cohesion strength through the topic coherence metric and minimizing noise aggregates based on the entropy metric. © 2024 IEEE.

关键词： Statistics

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3D Palmprint MCI Synthesis for 2D-3D Heterogeneous Palmprint Recognition 18th

3D Palmprint MCI Synthesis for 2D-3D Heterogeneous Palmprin...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Su, Le Fei, Lunke Zhao, Shuping Li, Shuyi Wei, Jia School of Computer Science and Technology Guangdong University of Technology Guangzhou China Faculty of Information Technology Beijing University of Technology Beijing China School of Computer and Information Hefei University of Technology Hefei China

ISBN: (纸本)9789819610679

Cross-2D-to-3D (2D-3D) heterogeneous palmprint recognition aims to matching a 2D palmprint probe with the 3D palmprint galleries, which shows great potential for biometric applications due to rich information of 3D images and the low-cost of 2D image acquisition. However, the large structural discrepancy between 2D and 3D palmprint images makes it hard to directly conduct matching between 2D-3D heterogeneous palmprint images. In this paper, we propose a palmprint image generative adversarial network (PIGAN) to convert 2D palmprint images into 3D domain for 2D-3D heterogeneous palmprint recognition. We first calculate the mean curvature images (MCIs) to present the 3D palm surface measurements of 3D palmprint images. Then, we employ an image-to-image generation backbone to transform 2D palmprint images into 3D MCI representations. To make the fake MCIs realistic, we impose both adversarial and identity-aware learning losses to protect the discriminative information of MCIs, and further introduce a visual-recovery loss to restore the visual-specific texture characteristics of palmprints. By this way, high-quality 3D palmprint MCI can be synthesized with high similarity as the real ones at both feature and visual levels, such that the pixel-level gap between 2D and 3D palmprint images can be effectively reduced for 2D-3D heterogeneous palmprint recognition. Extensive experimental results on the widely-used PolyU 2D-3D palmprint database clearly show the effectiveness of the proposed PIGAN in improving the performance of 2D-3D heterogeneous palmprint recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Palmprint recognition

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YOLOCSP-PEST for Crops Pest Localization and Classification

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computers, Materials & Continua 2025年第2期82卷 2373-2388页

作者： Farooq Ali Huma Qayyum Kashif Saleem Iftikhar Ahmad Muhammad Javed Iqbal Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Science&Engineering College of Applied Studies&Community ServiceKing Saud UniversityRiyadh11362Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

关键词： Deep learning classification of pests YOLOCSP-PEST pest detection

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Thermal Model of a Soldering Iron 33rd

Thermal Model of a Soldering Iron

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33rd International Conference on Organization and technology of Maintenance, OTO 2024

作者： Barić, Tomislav Glavaš, Hrvoje Dubravac, Anto Faculty of Electrical Engineering Computer Science and Information Technology Osijek University of Osijek Osijek Croatia

ISBN: (纸本)9783031805967

Soldering irons are a hand tool that is indispensable in the process of making small series of electronic devices. Soldering irons have evolved from very simple devices without temperature control to devices with complex temperature control. The implementation of temperature control is based on the knowledge of the thermal models of the soldering iron. Thermal models of soldering irons are based either on the theory of thermal fields or on the theory of equivalent Kirchhoff thermal circuits. The theory of thermal fields is applied for very accurate modeling. In doing so, the finite element method (FEM) is used. For the above, it is necessary to use software packages based on FEM. Unlike FEM-based modeling, thermal circuit modeling does not require specialized software tools. It is also possible to derive analytical expressions that describe the thermal process of the soldering iron. In this paper, a thermal model of a soldering iron based on equivalent Kirchhoff thermal circuits is presented. Analytical expressions were derived to describe the thermal transient. The validity of the theoretical model is supported by measurement. Measurements were carried out on two soldering irons from different manufacturers. The measurement results were analyzed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Hand tools

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Centralized smart energy monitoring system for legacy home appliances

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Energy Informatics 2024年第1期7卷 29页

作者： Ahmad, Shahed S. Almasalha, Fadi Qutqut, Mahmoud H. Hijjawi, Mohammad Faculty of Information Technology Applied Science Private University Amman11937 Jordan Faculty of Computer Science University of New Brunswick FrederictonNBE3B 5A3 Canada

The increasing global population and reliance on electrical devices for daily life resulted in sharply rising energy consumption. Also, this leads to higher household electricity bills. As a result, there is a growing demand for energy monitoring systems that can accurately estimate energy usage to help save power, especially for older home appliances that are difficult or expensive to update with monitoring sensors. However, current energy monitoring systems have some drawbacks, such as the inability to detect different types of appliances and the deployment complexity. Moreover, such systems are too costly to use in older power infrastructures. To address this issue, we proposed a centralized smart energy monitoring system designed for legacy home appliances, aiming to address the limitations of current energy monitoring systems by avoiding costly infrastructure upgrades to calculate the power consumption of legacy home appliances. The proposed system employs a two-layered architecture comprising hardware (Emontx device, Analog-to-Digital Converters (ADC), and Current Transformer (CT) sensors) and a software layer that includes Artificial Intelligence (AI) predictors using a pre-defined set of rules and K Nearest Neighbours (KNN) algorithms. We conducted three experiments on real home appliances to evaluate the proposed work. The accuracy of the proposed system showed positive results after several modifications and hard tuning of several parameters in devices, specifically for Jordanian power plants. © The Author(s) 2024.

关键词： Electric power utilization

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Enabling IoT Service Classification: A Machine Learning-Based Approach for Handling Classification Issues in Heterogeneous IoT Services

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IEEE Access 2023年 11卷 89024-89037页

作者： Abbasi, Mohammad Asad Chen, Yen-Lin Khan, Abdullah Ayub Memon, Zulfiqar A. Durrani, Nouman M. Yang, Jing Ku, Chin Soon Por, Lip Yee Benazir Bhutto Shaheed University Lyari Department of Computer Science and Information Technology Karachi75660 Pakistan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Sindh Madressatul Islam University Department of Computer Science Karachi74000 Pakistan -FAST Department of Computer Science Islamabad44000 Pakistan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The Internet of Things (IoT) is a form of Internet-based distributed computing that allows devices and their services to interact and execute tasks for each other. Consequently, the footprint of the IoT is increasing and becoming more complex to the highest degree. This has also given birth to new IoT-enabled applications and services. Efficient service interaction and management also call for understanding and analyzing the nature of IoT services. Further, IoT services must be characterized into various classes, and different service-related attributes must be considered for the classification. This article assesses the requirements of heterogeneous IoT services by examining their interactions. Principally, heterogeneous IoT and their service interactions are targeted. The research work performs classification of IoT services into various classes. Services are classified on the basis of various attributes. The attributes reflect different characteristics of the services. This research enables improved utilization of IoT services through efficient classification of available resources using machine learning methods. To demonstrate service classification applicability, the SVM, voting classifier, and decision tree have been applied in a service-oriented environment along with different types of services. All the services in the data set were analyzed and divided into five classes. Moreover, the decision tree performed well and achieved higher accuracy values in all classes. However, the overall prediction and classification of the decision tree model were observed to be good and satisfactorily high. © 2013 IEEE.

关键词： Decision trees

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Deployment of Polar Codes for Mission-Critical Machine-Type Communication Over Wireless Networks

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computers, Materials & Continua 2022年第4期71卷 573-592页

作者： Najib Ahmed Mohammed Ali Mohammed Mansoor Rodina Binti Ahmad Saaidal Razalli Bin Azzuhri Department of Software Engineering Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala Lumpur Malaysia 2Department of Computer System&TechnologyFaculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia

Mission critical Machine-type Communication(mcMTC),also referred to as Ultra-reliable Low Latency Communication(URLLC),has become a research *** is primarily characterized by communication that provides ultra-high reliability and very low latency to concurrently transmit short commands to a massive number of connected *** the reduction in physical(PHY)layer overhead and improvement in channel coding techniques are pivotal in reducing latency and improving reliability,the current wireless standards dedicated to support mcMTC rely heavily on adopting the bottom layers of general-purpose wireless standards and customizing only the upper *** mcMTC has a significant technical impact on the design of all layers of the communication protocol *** this paper,an innovative bottom-up approach has been proposed for mcMTC applications through PHY layer targeted at improving the transmission reliability by implementing ultra-reliable channel coding scheme in the PHY layer of IEEE 802.11a standard bearing in mind short packet transmission *** achieve this aim,we analyzed and compared the channel coding performance of convolutional codes(CCs),low-density parity-check(LDPC)codes,and polar codes in wireless network on the condition of short data packet *** Viterbi decoding algorithm(VA),logarithmic belief propagation(Log-BP)algorithm,and cyclic redundancy check(CRC)successive cancellation list(SCL)(CRC-SCL)decoding algorithm were adopted to CC,LDPC codes,and polar codes,***,a new PHY layer for mcMTC has been *** reliability of the proposed approach has been validated by simulation in terms of Bit error rate(BER)and packet error rate(PER)***-to-noise ratio(SNR).The simulation results demonstrate that the reliability of IEEE 802.11a standard has been significantly improved to be at PER=10−5 or even better with the implementation of polar *** results also show that the general-purpose wireless net

关键词： Machine-type communication mission-critical applications MTC URLLC M2M wireless networks polar codes

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