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Detection of Cyberattacks on the Metaverse based on First-order logic 6

Detection of Cyberattacks on the Metaverse based on First-or...

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6th ieee international conference on Image Processing, Applications and systems, IPAS 2025

作者： Al Amro, Sulaiman Qassim University Department of Computer Science College of Computer Buraydah51452 Saudi Arabia

ISBN: (纸本)9798331506520

There are currently considerable challenges concerning data security and privacy, particularly in relation to modern technologies. This includes the virtual world known as the Metaverse, which consists of a virtual space that integrates various technologies, and therefore susceptible to cyber threats such as malware, phishing, and identity theft. This has led recent studies to propose the development of Metaverse forensic frameworks and the integration of advanced technologies, including machine learning for intrusion detection and security. In this context, the application of first-order logic offers a formal and systematic approach to defining the conditions of cyberattacks, thereby contributing to the development of effective detection mechanisms. In addition, formalizing the rules and patterns of cyber threats has the potential to enhance the overall security posture of the Metaverse and thus the integrity and safety of this virtual environment. The current paper focuses on the primary actions employed by avatars for potential attacks, including Interval Temporal Logic (ITL) and behaviour-based detection to detect an avatar's abnormal activities within the Metaverse. The research established that the proposed framework attained an accuracy of 92.307%, resulting in the experimental results demonstrating the efficacy of ITL, including its superior performance in addressing the threats posed by avatars within the Metaverse domain. © 2025 ieee.

关键词： Phishing

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Enhancing Product Bundling with Large Language Models 13

Enhancing Product Bundling with Large Language Models

引用

13th international conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Bayrak, Ahmet Tugrul Kaplan, Alper Data Science and Innovation Ata Technology Platforms Istanbul Turkey

ISBN: (纸本)9798331516147

Recommendation systems have become popular in recent years. Today, in a world where competition is increasing, recommendation systems are used in many different areas. When the number of products and customers is high, it is important to recommend the right products to the right people. For this reason, different methods are widely used in recommendation systems and various techniques have been explored and developed to improve recommendation accuracy. The study evaluates the potential use of large language models. ChatGPT is chosen to create and expand existing product bundles. Apriori and word embedding models such as Word2Vec and FastText have been prepared for recommendations based on existing and bundles. The results show that large language models can improve bundle creation processes in some scenarios and that tools such as ChatGPT have the potential to improve recommender systems. © 2025 ieee.

关键词： Embeddings

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Cloud-Supported Bayesian Networks for Predicting Plant Stress in Adaptive Agricultural Practices

Cloud-Supported Bayesian Networks for Predicting Plant Stres...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Pandey, Pramod Devi, R. Aiyshwariya Sree Southry, S. Balasubadra, K. Rajmohan, M. Muthulekshmi, M. Nagpur Campus Maharashtra Pune India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India R.M.D. Engineering College Department of Information Technology Tamil Nadu Chennai India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509828

To maximize yields and optimize resource allocation in modern agriculture, planning for and managing plant stress is crucial. An innovative approach to plant stress prediction in adaptive agriculture is presented in this paper, which makes use of cloud-supported Bayesian networks (BNs). It combines real-time data from agricultural sensors with past records and environmental variables to build probabilistic models that can predict possible stress factors affecting plant health. The continually evolving features of agricultural ecosystems may be accommodated by using cloud infrastructure to train models and process data scalable. This technique successfully predicts degrees of plant stress, as shown in a thorough case study. This framework makes it easier to apply adaptive agricultural techniques by providing farmers with practical information based on predictive analytics. Resource-efficient practices appropriate to crop varieties and environmental circumstances also help make agricultural operations more sustainable. To overcome the challenges of modern agriculture, make informed decisions, and enhance agricultural systems. © 2025 ieee.

关键词： Smart agriculture

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A Comparison of Deep Learning and Machine Learning Methods for Water Quality Prediction 3

A Comparison of Deep Learning and Machine Learning Methods f...

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Srinivasa Gupta, N. Valarmathi, B. Hiwanj, Ishaan Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798331527549

Predicting water quality is essential to preserving human health and environmental sustainability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accuracies of 62%, 72%, 83%, 69%, 63%, 66%, 71%, 63%, and 64%, respectively, the current techniques utilized were Logistic Regression, Decision Trees, Random Forest Regressor, Extreme Gradient Boosting, Naive Bayes, K-nearest neighbors, Support Vector Machine, AdaBoost, and Bagging [9]. This study addresses these challenges by leveraging Adaptive Synthetic Sampling (ADASYN) to balance the dataset and evaluating model performance on datasets of 5,000 and 10,000 entries per class. A robust dataset obtained from Kaggle was used, with five models – Long Short-Term Memory (LSTM), Feed Forward Neural Network (FFNN), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Random Forest – evaluated and compared. The proposed methods demonstrate significant improvements in accuracy, with XGBoost achieving the highest accuracy of 95.53%, followed by Random Forest at 93.98%. This work underscores the importance of advanced machine learning techniques in addressing the limitations of traditional methods, enhancing accuracy, scalability, and adaptability in water quality prediction. These findings contribute to advancing environmental monitoring and management practices with reliable, data-driven insights. © 2025 ieee.

关键词： Support vector machines

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A Robust Protocol Design to Perform data Communication over Wireless Sensor Networks with Enhanced Privacy Preserving Principles 4

A Robust Protocol Design to Perform Data Communication over ...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sathyavani, Bandela Kumar Yadav, Rakesh John Shiny, J. Revathi, R. Bulakh, Ashish Kumar Jain, Shilpa School of Engineering Sr University Department of Eee Telangana Warangal India Amity School of Engineering and Technology Amity University Bihar Patna India Velammal College of Engineering and Technology Madurai India Psgr Krishnammal College for Women Department of Computer Science-Information Technology Coimbatore India Cdgi Department of Computer Science and Engineering Indore India Ies College of Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798331523923

the growth of wireless sensor networks has transformed all automation and industrial systems. Nonetheless, guaranteeing safe and privacy-preserving connectivity in sensor networks continues to be a difficult task. Current authentication and routing techniques frequently jeopardize privacy, entail significant design intricacy, also vulnerable to malware assaults. To mitigate these restrictions, we present an innovative secure protocol, the Asymmetric Cryptography-based Modified Cuckoo Search Optimization Protocol (MCuSO) for wireless sensor networks. Our proposed technique impacts asymmetric credentials with belief supervision for the privacy preservation of WSN data. This method attains an equilibrium in the middle of security and efficiency. Through comprehensive simulations utilizing the MATLAB platform, we illustrate that MCuSO attains an average network throughput of 94% and a network residual energy of 89%, in comparison to leading other algorithms. Moreover, the protocol efficiently reduces malware attacks. These findings underscore MCuSO as a viable outcome of resource limited WSN devices, providing an advantageous equilibrium of information security, data integrity and data efficacy. © 2025 ieee.

关键词： Authentication Protocol

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Computer Aided Diagnosis Multi-model System using Late Fuison and Ensemble Learning

Computer Aided Diagnosis Multi-model System using Late Fuiso...

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2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Priya, M.Kani Aruru, Gunabhiram Vanam, Nikhil Suganiya, M. Salomi, M. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India

ISBN: (纸本)9798331508685

Lung Cancer and brain tumors are the well-known causes of cancer deaths universal. Therefore, appropriate and accurate diagnosis is an important issue that affects better and more reliable treatment and the patient's chance of survival. Present diagnostic strategies are mostly based on a single piece of information. This often leads to missing necessary information, which can lead to Misidentification of condition or delay in timely treatment. This study presents a novel Computer-Aided-Diagnosis system designed to enhance the detection of lung cancer and brain tumors by integrating multimodal data, including medical images (CT and MRI scans) and clinical information. The proposed approach employs late fusion and ensemble learning techniques, utilizing CNN for processing CT images, VGG19 for analyzing MRI scans, and Random Forest classifiers for evaluating clinical data. By separately training these models, the system capitalizes on the distinct characteristics of each data modality, overcoming the limitations of traditional CAD systems that typically rely on a single data source. This comprehensive analysis not only improves diagnostic accuracy but also provides reliable predictions, ultimately facilitating better clinical decision-making for lung cancer and brain tumor detection. © 2025 ieee.

关键词： Lung cancer

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Detection of Anomaly in Internet of Things using Gated Recurrent Unit with Artificial Bee Colony 1

Detection of Anomaly in Internet of Things using Gated Recur...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Karthik, M. Ganesh Divya, N. Sree Shnaeen, Amaar Haamid Alagarraja, K. Praveena, S. Gitam University Department of Computer Science and Engineering Bengaluru India Mahatma Gandhi Institute of Technology Department of Information Technology Hyderabad India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Electronics and Communication Engineering Mamallapuram India

ISBN: (纸本)9798331529246

Now a days, development in Internet of Things (IoT) networking generates large attack surface for cybercriminals, that impact the industry has resulted with exponential increase in cyber-attacks. To develop a robust anomaly detection system, a Gated Recurrent Unit Anomaly Detection (GRUAD) is proposed in this research. Initially, the data is collected from the Toyama IoT Network Traffic dataset (TON-IoT) dataset which includes samples of packet capture data (PCAP files) and network logs (text files). Then, the collected data is pre-processed using data cleaning technique which removes noisy and irrelevant data, then label encoding and data normalization assist to transform data into a suitable format while data splitting. Further, features are selected using Mutual Information (MI) in which features with high mutual information with the target variable are selected. Then, Pearson Coefficient Correlation (PCC) identifies features with strong linear correlations. At the end, K-Best selects the top-ranked features. Finally, the Multistep-Prediction is performed using GRUAD by hyperparameter tuning the Artificial Bee Colony (ABC). The proposed model gained better results in terms of precision with value 98.54%, f1 score of 98.74%, Accuracy of 98.62%, recall of 98.77% comparing with existing method Logistic Regression (LR) respectively. © 2025 ieee.

关键词： Logistic regression

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Artificial Intelligence for Automated Support in Educational systems

Artificial Intelligence for Automated Support in Educational...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Karthik, R. Maha Vishnu, S. Saran, R. Shashidharan, S. K.S.Rangasamy College of Technology Department of Computer Science and Business Systems Namakkal India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Educational institutions frequently suffer from inefficiencies in decision-making, interdepartmental communication, and administrative procedures. In order to optimize resource allocation, automate procurement workflows, and streamline helpdesk operations, this research study proposes an AI-powered platform that combines machine learning (ML) with Natural Language Processing (NLP). The platform acts as a central location for handling enquiries, automating repetitive processes, and enhancing decision-making by utilizing insights derived from data. The proposed system improves query response, fosters interdepartmental communication, and provides predictive analytics to predict resource requirements by continuously learning from user interactions. Through process automation, real-time support, and smooth departmental communication, this AI-driven system aims to improve institutional efficiency, reduce delays, and raise employee satisfaction. © 2025 ieee.

关键词： Efficiency

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The Usability of Teaching Practice Placement System Through Cloud Technology: PLMiSYS 22

The Usability of Teaching Practice Placement System Through ...

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22nd international Learning and Technology conference, L and T 2025

作者： Ibrahim, Mohd Mokhzani Abas, Ashardi Azmi, Noor Hidayah Ibrahim, Norshahila Universiti Pendidikan Sultan Idris Faculty of Science and Mathematics Perak Tanjung Malim Malaysia

ISBN: (纸本)9798331532215

Conventional teaching practice placement systems at universities often face inefficiencies, limited user-friendliness, and poor integration with modern technology, which restrict students from fully applying their theoretical knowledge in real teaching environments. To address these challenges, the PuLaMI Integrated System (PLMiSYS), a cloud-based placement management system, was developed by Universiti Pendidikan Sultan Idris (UPSI) to streamline the placement process. This study evaluates the usability of PLMiSYS, focusing on its effectiveness in meeting students' needs. A mixed-method approach was employed, comprising quantitative data from usability surveys involving 285 students and qualitative data from semi-structured interviews with 15 students. Results show that PLMiSYS enhances placement efficiency, improves user satisfaction, and provides a scalable model for educational management. Participants highlighted the system's intuitive design, though some suggested improvements for document upload capacity and integration with school systems. The study has practical implications, as PLMiSYS serves as a model for educational institutions aiming to digitise administrative processes, potentially reducing staff workload and increasing student satisfaction. Theoretically, this study adds to literature on technology adoption in education, demonstrating the importance of usability in cloud-based systems for effective educational management. Findings suggest that cloud technology adoption can improve operational efficiency and student placements, fostering a supportive learning environment. © 2025 ieee.

关键词： Cloud platforms

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A Comparative Analysis of Artificial Intelligence and Business Intelligence Using Big data Analytics 1

A Comparative Analysis of Artificial Intelligence and Busine...

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1st international conference on Advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Marella, Bhagath Chandra Chowdari Vegineni, Gopi Chand Addanki, Sireesha Ellahi, Ehsan Arun Kumar, K. Mandal, Rishabh Insights & Data Department of Financial Services Capgemini India Enrollment and Eligibility Team India System soft Technologies LLC India Cleveland State University Department of Information Systems OH United States Tula's Institute Department of Computer Science and Engineering UK Dehradun India India

ISBN: (纸本)9798331518578

Business intelligence (BI) encompasses the tools and uses to gather, combine, examine, and display data about a specific firm. Using historical and current efficiency comparisons, these structures assist in formulating plans and choices to achieve better outcomes in the future. Additionally, it helps firms determine which business issues and industry trends require attention. It has several benefits, such as increasing responsibility and efficiency, but it carries some disadvantages, such as being expensive, complicated, and time-consuming to adopt. Using big data-driven approaches to optimize talent recruitment is essential for improved decisions in the cutthroat world of BI. The efficacy of many machine learning algorithms to increase recruiting performance and accuracy is compared in this investigation. For optimizing, cross-validating, and hyperparameter modification were employed. The Random Forest (RF) algorithm has the greatest accuracy (92.9%), trailed by the Gradient Boosting Classifying system (92.6%) and the neural network (NNs) models (92.7%). These findings imply that sophisticated ML algorithms, in particular RF and NN, can greatly improve BI platforms' hiring procedures. Recruiters can benefit from this research's insightful recommendations for incorporating advanced ML methods into talent recruitment plans. © 2025 ieee.

关键词： data accuracy

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