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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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Wildfire Spread Prediction using Machine Learning Algorithms 14

Wildfire Spread Prediction using Machine Learning Algorithms

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Singh, Aman Yadav, Rakesh Sudhamshu, Gadug Basnet, Aryan Ali, Rahmat University Faculty of Engineering and Technology Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335095

Wildfires are one of the most destructive natural disasters that cause significant harm to both humans and the environment. Predicting their spread is critical for disaster management and preparedness. In this study, we have utilized machine learning algorithms, including Decision Tree Regression, XG Boost Regression, and Artificial Neural Networks, to predict the spread of wildfires using the Next Day Wildfire dataset. The dataset includes satellite images, weather, and geography conditions aggregated across the United States from 2012 to 2020. We preprocessed and engineered the dataset which includes the features such as elevation, wind direction and speed, temperature, humidity, precipitation, drought index, vegetation index, energy release component, and population density. We evaluated the models using the Root Mean Squared Error (RMSE) metric and found that the Decision Tree Regression algorithm performed the best with the lowest RMSE score. Our study highlights the potential of machine learning algorithms in predicting the spread of wildfires, which can aid in better disaster management and preparedness efforts. © 2023 IEEE.

关键词： Forecasting

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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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***: A Japanese Token Level Pattern Matching and Processing Language with Dependency Analysis 23

***: A Japanese Token Level Pattern Matching and Processing ...

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12th International Conference on Software and computer Applications, ICSCA 2023

作者： Nakanishi, Tsuneo Ototake, Hokuto Tanabe, Toshifumi Yoshimura, Kenji Department of Electronics Engineering and Computer Science Faculty of Engineering Fukuoka University Fukuoka Japan

ISBN: (纸本)9781450398589

The quality of development documents written in natural language is basically ensured by developer’s review with quite a few man-hours. Natural language processing (NLP) tools can partially automate the time-consuming review. The authors have developed morpheme level pattern matching and processing tools morfgrep and morfawk for Japanese texts for this purpose;moreover, in this paper, extends them to perform pattern matching on dependency among phrases. Example applications of the tools, proofreading of development documents and domain-specific term extraction, are also presented. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Pattern matching

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Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data

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computers, Materials & Continua 2023年第10期77卷 681-697页

作者： Uzair Aslam Bhatti Sibghat Ullah Bazai Shumaila Hussain Shariqa Fakhar Chin Soon Ku Shah Marjan Por Lip Yee Liu Jing College of Information and Communication Engineering Hainan UniversityHaikou570228China Department of Computer Engineering Balochistan University of Information TechnologyEngineeringand Management Sciences(BUITEMS)QuettaPakistan Department of Computer Science Sardar Bahadur Khan Women’s UniversityQuettaPakistan Department of Computer Science Universiti Tunku Abdul RahmanKampar31900Malaysia Department of Software Engineering Balochistan University of Information TechnologyEngineeringand Management Sciences(BUITEMS)QuettaPakistan Faculty of Computer Science and Information Technology University of MalayaKuala Lumpur50603Malaysia

Crop diseases have a significant impact on plant growth and can lead to reduced *** methods of disease detection rely on the expertise of plant protection experts,which can be subjective and dependent on individual experience and *** address this,the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease *** this paper,we propose a novel approach that utilizes a convolutional neural network(CNN)model in conjunction with Inception v3 to identify plant leaf *** research focuses on developing a mobile application that leverages this mechanism to identify diseases in plants and provide recommendations for overcoming specific *** models were trained using a dataset consisting of 80,848 images representing 21 different plant leaves categorized into 60 distinct *** rigorous training and evaluation,the proposed system achieved an impressive accuracy rate of 99%.This mobile application serves as a convenient and valuable advisory tool,providing early detection and guidance in real agricultural *** significance of this research lies in its potential to revolutionize plant disease detection and management *** automating the identification process through deep learning algorithms,the proposed system eliminates the subjective nature of expert-based diagnosis and reduces dependence on individual *** integration of mobile technology further enhances accessibility and enables farmers and agricultural practitioners to swiftly and accurately identify diseases in their crops.

关键词： Plant disease Inception v3 CNN crop diseases

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Identification of HIV Inhibitors Using Graph Neural Networks 47

Identification of HIV Inhibitors Using Graph Neural Networks

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47th ICT and Electronics Convention, MIPRO 2024

作者： Georgiev, Dimitar Toshevska, Martina Gievska, Sonja University Of Ss. Cyril And Methodius Faculty Of Computer Science And Engineering Skopje Macedonia

ISBN: (纸本)9798350382495

Graph neural networks (GNN), primed to extract knowledge and discover patterns in graph-structured data, have received particularly increased attention in biomedical research. By integrating information from a variety of biomedical knowledge repositories they offer a fast and efficient computational alternative approach to the costly and time-consuming process of drug development and research. The core contributions of this paper include the design and empirical evaluation of several GNN-based models for the identification of potential HIV (Human Immunodeficiency Virus) inhibitors. In particular, the predictive power of model variants based on Graph Attention Network (GAT), Graph Isomorphism Network (GIN), and Continuous Kernel-Based Graph Convolutional Network, specifically developed to handle molecular data, have been investigated. To assess the effectiveness of the proposed models, the Stanford open graph benchmark dataset for molecular data ogbg-molhiv was used. Furthermore, two types of molecular fingerprints have been proposed to augment the molecular representation in the proposed graph neural models, leading to better performance standing compared to the original models. The paper provides a detailed description of the proposed models for identifying HIV inhibitors, followed by a comparative analysis of the experimental results focusing on a discussion of the challenges we face and future research directions that could be investigated. © 2024 IEEE.

关键词： Diseases

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R3 - Rescale, Rotate, and Randomize: A Novel Image Cryptosystem Utilizing Chaotic and Hyper-Chaotic Systems

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IEEE Access 2023年 11卷 119284-119312页

作者： Gabr, Mohamed Korayem, Yousef Chen, Yen-Lin Yee, Por Lip Ku, Chin Soon Alexan, Wassim Faculty of Media Engineering and Technology Computer Science Department Cairo11835 Egypt National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan 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 Faculty of Information Engineering and Technology Communications Department Cairo11835 Egypt New Administrative Capital Mathematics Department Cairo13507 Egypt

This work proposes a novel image encryption algorithm that integrates unique image transformation techniques with the principles of chaotic and hyper-chaotic systems. By harnessing the unpredictable behavior of the Chua system and the hyper-chaotic nature of the Chen system, the algorithm carries out rescaling, rotation, and randomization on the target image. The intrinsic unpredictability and sensitivity to initial conditions of these chaotic systems endow the encryption algorithm with an expansive key space of 25208. This feature not only bolsters its resilience against brute-force attacks but also magnifies its overall security profile. The algorithm's efficiency is evident in its rapid computational speed and lean resource consumption, making it suitable for real-time applications. To gauge its robustness, a battery of rigorous tests and analyses, spanning differential attacks, statistical attacks, and brute-force assaults, were employed. The results validate its ability to resist a diverse spectrum of threats. With its expansive key space, exceptional efficiency, and sturdy defenses, the proposed algorithm emerges as a potential cornerstone for safeguarding digital images in arenas like secure communication, data storage, and multimedia transmission. In sum, this research pushes the boundaries of high-security image encryption methodologies, catering to the burgeoning demands of the digital age. © 2013 IEEE.

关键词： Cryptography

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Speeding up Dense Optical Flow Estimation with CUDA 32

Speeding up Dense Optical Flow Estimation with CUDA

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32nd Telecommunications Forum, TELFOR 2024

作者： Stameski, Kristijan Gusev, Marjan Ss. Cyril and Methodius University in Skopje Faculty of Computer Science and Engineering Macedonia

ISBN: (纸本)9798350391053

Optical flow is the perceived movement of a pixel within the video. It is inherently helpful for motion tracking and may also be used as a preprocessing for other computer vision algorithms or machine learning. Algorithmic optical flow estimation is slow and resource-intensive, but real-time performance can be achieved by using GPUs. This paper discusses implementing and optimizing a pyramidal Lucas-Kanade optical flow algorithm in CUDA. © 2024 IEEE.

关键词： Graphics processing unit

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A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

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computers, Materials & Continua 2023年第12期77卷 3969-3992页

作者： Naeem Ullah Javed Ali Khan Sultan Almakdi Mohammed S.Alshehri Mimonah Al Qathrady Eman Abdullah Aldakheel Doaa Sami Khafaga Department of Software Engineering University of Engineering and TechnologyTaxila4400Pakistan Department of Computer Science Faculty of PhysicsEngineeringand Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Departments of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL methods often require substantial computational resources,hindering their application on resource-constrained *** propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome *** Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for *** proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet *** specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato *** model could be used on mobile platforms because it is lightweight and designed with fewer *** farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.

关键词： CNN deep learning DTomatoDNet tomato leaf disease classification smart agriculture

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A Hybrid Cybersecurity Algorithm for Digital Image Transmission over Advanced Communication Channel Models

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computers, Materials & Continua 2024年第4期79卷 201-241页

作者： Naglaa F.Soliman Fatma E.Fadl-Allah Walid El-Shafai Mahmoud I.Aly Maali Alabdulhafith Fathi E.Abd El-Samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

The efficient transmission of images,which plays a large role inwireless communication systems,poses a significant challenge in the growth of multimedia ***-quality images require well-tuned communication *** Single Carrier Frequency Division Multiple Access(SC-FDMA)is adopted for broadband wireless communications,because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio(PAPR).Data transmission through open-channel networks requires much concentration on security,reliability,and *** data need a space away fromunauthorized access,modification,or *** requirements are to be fulfilled by digital image watermarking and *** paper ismainly concerned with secure image communication over the wireless SC-FDMA systemas an adopted communication *** introduces a robust image communication framework over SC-FDMA that comprises digital image watermarking and encryption to improve image security,while maintaining a high-quality reconstruction of images at the receiver *** proposed framework allows image watermarking based on the Discrete Cosine Transform(DCT)merged with the Singular Value Decomposition(SVD)in the so-called DCT-SVD *** addition,image encryption is implemented based on chaos and DNA *** encrypted watermarked images are then transmitted through the wireless SC-FDMA *** linearMinimumMean Square Error(MMSE)equalizer is investigated in this paper to mitigate the effect of channel fading and noise on the transmitted *** subcarrier mapping schemes,namely localized and interleaved schemes,are compared in this *** study depends on different channelmodels,namely PedestrianAandVehicularA,with a modulation technique namedQuadratureAmplitude Modulation(QAM).Extensive simulation experiments are conducted and introduced in this paper for efficient transmission of encrypted watermarked *** addition,different variants of SC-FDMA bas

关键词： Cybersecurity applications image transmission channel models modulation techniques watermarking and encryption

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