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Modelling the dynamics of information warfare: an attacker-defender scenario using Lotka-Volterra equations

International Journal of Information technology (Singapore) 2025年第3期17卷 1407-1418页

作者： Pandey, Rashmikiran Pandey, Mrinal Nazarov, Alexey Nikolaevich Faculty of Radio and Computer Technology Moscow Institute of Physics & Technology Moscow 141701 Russian Federation Federal Research Center “Computer Science and Control” Russian Academy of Science Moscow 119333 Russian Federation

The dynamics of information warfare in an attacker-defender scenario pose significant challenges in today’s digital age. To address these challenges, this research models the dynamics of information warfare using modified Lotka-Volterra equations, adapted from predator-prey interactions, to simulate the interactions between attackers and defenders. The model captures the growth, interaction, and feedback loops of both populations, offering insights into their dynamics over time. By incorporating the attacker’s growth rate and the defender’s natural decline rate, and discretizing time for real-time simulation, the study reveals how attacker populations initially grow but decline as defenders enhance their countermeasures. The defender population stabilizes as their defensive capabilities improve, demonstrating the model’s effectiveness in representing the complex dynamics of information warfare. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Attacker-defender Cyber security Information warfare Lotka-Volterra equations Population dynamics

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Advanced Feature Extraction and Machine Learning Techniques for Classifying Steam Game Feedback

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第1期19卷 107-125页

作者： Al Tawil, Arar Fathi, Hanaa Alzoubi, Sharaf Shaban, Amneh Almazaydeh, Laiali H. Faculty of Information Technology Applied Science Private University Amman Jordan College of Computer Science and Informatics Amman Arab University Amman Jordan College of Engineering AbuDhabi University Abu Dhabi United Arab Emirates

The gaming industry produces vast amounts of user-generated feedback, making it challenging for developers to efficiently analyze and respond to real-time reviews. This study addresses the problem of classifying large-scale unstructured user feedback from Steam reviews. In this paper an approach that integrates traditional machine learning models and deep learning models is proposed. XGBoost is used to manage feature-rich datasets, reducing overfitting. Long-short-term memory (LSTM) and Bi-directional LSTM are used to enhance the accuracy and robustness of classification. Feature extraction techniques, such as sentiment analysis and topic modeling, are employed to enrich the dataset and improve model performance. The experimental results show that the XGBoost model achieved the highest performance with an accuracy of 0.9499 and a ROC-AUC score of 0.6113, demonstrating superior performance in distinguishing between positive and negative feedback. In comparison, deep learning models such as LSTM and Bi-directional LSTM showed lower ROC-AUC scores, indicating less effectiveness in handling the classification task. This approach offers game developers a reliable and scalable solution for classifying user sentiment, leading to better game improvements based on user reviews. © 2025 by the authors of this article.

关键词： Contrastive Learning

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Effect of energy intensity on component quality through laser metal deposition (LMD) process

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Welding and Cutting 2022年第2期2022卷 120-127页

作者： Sheikhi, Shahram Mayer, Eduard Bronstein, Konstantin Faculty of Engineering and Computer Science at Institute of Materials Science and Joining Technology University of Applied Science Hamburg Germany Institute of Materials Science and Joining Technology University of Applied Science Hamburg Germany

The ability to quickly produce complex structures of a unique design and with reduced waste are the main advantages of the additive manufacturing (AM) of metal-based components. Various AM processes are used in the industry and have been subject of re-search. Nevertheless, the correlation of energy input, heat balance and quality of components is still a point of research. This study will introduce an affordable method to control the height of each layer as a mech-anism to assure the reproducibility of AM products. Moreover, the fundamentals of energy input and heat balance will be discussed in order to demonstrate that the general requirement for constant parameters as demanded by certification authorities actually in-creased the production time and reduced the quality of the parts. The research has been carried out by means of laser metal deposition with powder, which is one process of additive manufacturing for met-al-based components. © 2022, DVS Media GmbH. All rights reserved.

关键词： 3D printers

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An Adaptive Framework for Classification and Detection of Android Malware

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第21期18卷 59-73页

作者： Al Sharah, Ashraf Alrub, Yousef Abu Owida, Hamza Abu Elsoud, Esraa Abu Alshdaifat, Nawaf Khtatnaha, Hamzah Department of Electrical Engineering College of Engineering Technology Al-Balqa Applied University Amman Jordan Department of Computer Information Systems Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Medical Engineering Faculty of Engineering Al-Ahliyya Amman University Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Faculty of Information Technology Applied Science Private University Amman Jordan

The hardware and software of a computer are controlled by its operating system (OS), which performs essential tasks such as input and output processing, file and memory management, and the management of peripheral devices such as disk drives and printers. Application software refers to programs designed for specific purposes, these applications, often freely available and open source, contribute to the rising number of downloads. In the third quarter of 2022, combined downloads from the Apple App Store and Google Play Reached an estimated 35.3 billion. However, the prevalence of insecurity in these applications and technologies heightens the potential for cybercrimes. Protection against unauthorized intruders is crucial in identifying malicious applications. Machine learning (ML) serves as a promising avenue for detecting malware attacks, offering potential solutions to bolster cybersecurity measures. We propose a novel approach utilizing ML to enhance malware detection accuracy by segmenting datasets into distinct groups. Our research employs supervised ML techniques on the CICMaldroid2020 dataset, which includes comprehensive information such as intent actions, permissions, and sensitive APIs. The dataset was partitioned into four groups, each containing 150 features, and analyzed across four experiments to distinguish between attack and benign classes. Our proposed model demonstrated exceptional performance, with the random forest algorithm achieving an accuracy of 98.6% and a precision of 98.75%. These results highlight the effectiveness of our segmentation approach and its significant contribution to advancing malware detection in Android applications, offering a promising direction for future cybersecurity solutions. © 2024 by the authors of this article.

关键词： Android malware

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Explainable AI-Based DDoS Attacks Classification Using Deep Transfer Learning

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computers, Materials & Continua 2024年第9期80卷 3785-3802页

作者： Ahmad Alzu’bi Amjad Albashayreh Abdelrahman Abuarqoub Mai A.M.Alfawair Department of Computer Science Jordan University of Science and TechnologyIrbid22110Jordan Department of Computer Science The University of JordanAmman11942Jordan Cardiff School of Technologies Cardiff Metropolitan UniversityCardiffCF52YBUK Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied UniversitySalt19117Jordan

In the era of the Internet of Things(IoT),the proliferation of connected devices has raised security concerns,increasing the risk of intrusions into diverse *** the convenience and efficiency offered by IoT technology,the growing number of IoT devices escalates the likelihood of attacks,emphasizing the need for robust security tools to automatically detect and explain *** paper introduces a deep learning methodology for detecting and classifying distributed denial of service(DDoS)attacks,addressing a significant security concern within IoT *** effective procedure of deep transfer learning is applied to utilize deep learning backbones,which is then evaluated on two benchmarking datasets of DDoS attacks in terms of accuracy and time *** leveraging several deep architectures,the study conducts thorough binary and multiclass experiments,each varying in the complexity of classifying attack types and demonstrating real-world ***,this study employs an explainable artificial intelligence(XAI)AI technique to elucidate the contribution of extracted features in the process of attack *** experimental results demonstrate the effectiveness of the proposed method,achieving a recall of 99.39%by the XAI bidirectional long short-term memory(XAI-BiLSTM)model.

关键词： DDoS attack classification deep learning explainable AI cybersecurity

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Enhanced gas sensing characteristics of rGO-TiO₂ nanocomposites: Synthesis, structural, optical characterization and sensitivity analysis for environmental monitoring

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Ceramics International 2025年第15期51卷 19923-19941页

作者： Rajguru, Girish Murlidhar Mishra, Rakesh Kumar Raykar, Vijay S. Khirade, Pankaj P. Faculty of Applied Sciences Parul Institute of Applied Sciences Parul University Gujarat Vadodara391760 India Department of Applied Sciences and Humanities Faculty of Engineering & Technology Parul University Gujarat Vadodara391760 India Department of Physics G.M. Vedak College of Science Tala MS Raigad402111 India Department of Physics Shi Shivaji Science College MS Amravati444603 India

The modified Hummers' method was employed to synthesize graphene oxide (GO), while titanium dioxide (TiO2) nanoparticles were produced using the sol-gel process. These nanoparticles were then hydrothermally blended to create rGO-TiO2 nanocomposites, which contain rutile, anatase, and brookite phases of TiO2 alongside varying oxidation levels of reduced graphene oxide (rGO). The synthesis successfully yielded rGO-TiO2 nanocomposites with uniformly dispersed TiO2 nanoparticles on rGO sheets, using with varying concentration ratios of rGO:TiO2 (10:0, 7.5:2.5, 5:5, 2.5:7.5, and 0:10). The average crystallite size increased from approximately 15.48 to 35.08 nm with higher TiO2 concentrations, as supported by Williamson-Hall extrapolation. FTIR analysis revealed functional groups associated with both the hexagonal and tetragonal phases of the nanocomposite. HR-TEM analysis indicated a uniform TiO2 particle size distribution, peaking at 204–210 nm. The Raman spectra confirmed the structural integrity of rGO and the presence of anatase TiO2, with an energy band gap of ∼3.67 eV. The gas sensing characteristics highlighted enhanced sensitivity to NO2 compared to other gases such as H2S, CO, and NH3. The results demonstrate that higher rGO content significantly improves the sensor's responsiveness and recovery dynamics, making these composites promising candidates for hazardous NO2 gas detection in environmental monitoring and industrial safety applications. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Sol-gel process

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Rice Bacterial Infection Detection Using Ensemble Technique on Unmanned Aerial Vehicles Images

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computer Systems science & Engineering 2023年第2期44卷 991-1007页

作者： Sathit Prasomphan Department of Computer and Information Science Faculty of Applied ScienceKing Mongkut’s University of Technology North Bangkok1518 Pracharat 1 Rd.WongsawangBangsueBangkok10800Thailand

Establishing a system for measuring plant health and bacterial infection is critical in ***,the farmers themselves,who observed them with their eyes and relied on their experience in analysis,which could have been *** inspection can determine which plants reﬂect the quantity of green light and near-infrared using infrared light,both visible and eye using a *** goal of this study was to create algorithms for assessing bacterial infections in rice using images from unmanned aerial vehicles(UAVs)with an ensemble classiﬁcation *** neural networks in unmanned aerial vehi-cles image were *** convey this interest,the rice’s health and bacterial infec-tion inside the photo were *** project entailed using pictures to identify bacterial illnesses in *** shape and distinct characteristics of each infection were *** symptoms were deﬁned using machine learning and image processing *** steps of a convolution neural network based on an image from a UAV were used in this study to determine whether this area will be affected by *** proposed algorithms can be utilized to classify the types of rice deceases with an accuracy rate of 89.84 percent.

关键词： Bacterial infection detection adaptive deep learning unmanned aerial vehicles image retrieval

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Toward Fine-grained Image Retrieval with Adaptive Deep Learning for Cultural Heritage Image

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computer Systems science & Engineering 2023年第2期44卷 1295-1307页

Fine-grained image classiﬁcation is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a speciﬁc category caused by different poses and scales.A cul-tural heritage image is one of theﬁne-grained images because each image has the same similarity in most *** the classiﬁcation technique,distinguishing cultural heritage architecture may be difﬁ*** study proposes a cultural heri-tage content retrieval method using adaptive deep learning forﬁne-grained image *** key contribution of this research was the creation of a retrieval mod-el that could handle incremental streams of new categories while maintaining its past performance in old categories and not losing the old categorization of a cul-tural heritage *** goal of the proposed method is to perform a retrieval task for *** learning for new classes was conducted to reduce the re-training *** this step,the original class is not necessary for re-train-ing which we call an adaptive deep learning *** heritage in the case of Thai archaeological site architecture was retrieved through machine learn-ing and image *** analyze the experimental results of incremental learning forﬁne-grained images with images of Thai archaeological site architec-ture from world heritage provinces in Thailand,which have a similar *** aﬁne-grained image retrieval technique for this group of cultural heritage images in a database can solve the problem of a high degree of similarity among categories and a high degree of dissimilarity for a speciﬁc *** proposed method for retrieving the correct image from a database can deliver an average accuracy of 85 *** deep learning forﬁne-grained image retrieval was used to retrieve cultural heritage content,and it outperformed state-of-the-art methods inﬁne-grained image retrieval.

关键词： Fine-grained image adaptive deep learning cultural heritage image retrieval

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An electron-transfer-tuning strategy at the graphene/metal interface for improving acidic water electrolysis in proton exchange membrane electrolyzers

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Journal of Energy Chemistry 2025年第4期103卷 344-352页

作者： Yue Xu Shuaidong Li Yingjian He Fumiya Shiokawa Samuel Jeong Aimi Asilah Haji Tajuddin Zeyu Xi Yoshikazu Ito Jingzi Zhang Zeyun Cai Xi Lin Kailong Hu School of Materials Science and Engineering and Institute of Materials Genome&Big DataHarbin Institute of TechnologyShenzhen 518055GuangdongChina Institute of Special Environments Physical Sciences Harbin Institute of TechnologyShenzhen 518055GuangdongChina School of Computer Science and Technology Harbin Institute of TechnologyShenzhen 518055GuangdongChina Institute of Applied Physics Graduate School of Pure and Applied SciencesUniversity of TsukubaTsukuba 305-8573Japan

Graphene encapsulation has been shown to be an effective technique for improving the corrosion resistance of non-noble metal catalysts for the acidic water *** key challenge lies in enhancing the electrocatalytic activity of graphene-encapsulated metals while maintaining their durability in acidic ***,an electron-transfer-tuning strategy is investigated at the graphene/NiMo interface,aiming to improve the hydrogen evolution reaction(HER)performance of graphene-encapsulated NiMo *** doping of Ti,a low electronegativity element,into NiMo substrate was confirmed to increase electron transfer from the metal core toward the *** electron-rich state on graphene facilitates the adsorption of positively charged protons on graphene,thereby enabling a Pt/C-comparable performance in 0.5 M H_(2)SO_(4),with only a 3.8%degradation in performance over a 120-h continuous *** proton exchange membrane(PEM)water electrolyzer assembled by the N-doped grapheneencapsulated Ti-doped NiMo exhibits a smaller cell voltage to achieve a current density of 2.0 A cm^(-2),in comparison to the Pt/C based *** study proposes a novel electron-transfer-tuning strategy to improve the HER activity of graphene-encapsulated non-noble metal catalysts without sacrificing durability in acidic electrolytes.

关键词： Hydrogen evolution Acidic media Graphene encapsulation Interface Proton exchange membrane

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Finite Difference Method for Neutrosophic Fuzzy Second Order Differential Equation under Generalized Hukuhara Differentiability

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Neutrosophic Sets and Systems 2023年 58卷 311-329页

作者： Kamal, Bassem Khalid, Huda E. Salama, A.A. El-Baghdady, Galal I. Department of Mathematics and Engineering Physics Faculty of Engineering Delta University for Science and Technology Egypt Telafer University Telafer Iraq Department of Mathematics and Computer Science Faculty of Science Port Saied University Egypt Department of Mathematics and Engineering Physics Faculty of Engineering Mansoura University Egypt

This paper solves the boundary value problem of the second-order differential equation under the neutrosophic fuzzy boundary condition. The proposed solution is approximated using the finite difference method but deneutrosophication utilizing strongly generalized Hukuhara differentiability. The error calculation is then processed by comparing the proposed numerical solution with the analytical solution presented in our earlier work [20]. © (2023). All Rights Reserved.

关键词： Boundary conditions

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