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Enhancing Malicious URL Detection with advanced Machine Learning Techniques 1

Enhancing Malicious URL Detection with Advanced Machine Lear...

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

作者： Alavala, Hemanth Reddy Singh, Shambhavi Joshi, Priyam Basavaraju, Sagar Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798331518578

Malicious URLs pose a significant threat to online security. The can steal our personal information and can even hack out system. Refraining ourselves from using such malicious websites is the need of the hour. This paper presents a comprehensive evaluation of various machine learning algorithms which are used to effectively detect and mitigate these threats. This research explores the performance of traditional machine learning techniques including Random Forests, Decision Trees, Logistic Regression, Support Vector Machines, as well as advanced techniques like XGBoost, LightGBM, and CatBoost while the previous works focused on only traditional classification techniques. Additionally, this work investigates the potential of deep learning models, including LSTM and CNN, to capture intricate patterns within URL sequences. Machine learning algorithms are evaluated using the evaluations metrics including Accuracy, Recall, Precision and F1 score. Experimental Results and analysis have shown that advanced models outperform traditional classification techniques. These results valuable insights into the most effective approaches for safeguarding online users from malicious attacks. © 2025 IEEE.

关键词： Phishing

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Application of Wireless Sensor Technology in Urban Energy Management 24

Application of Wireless Sensor Technology in Urban Energy Ma...

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8th international conference on Electronic information Technology and computer engineering, EITCE 2024

作者： Zhang, Yajuan Chen, Jie Gong, Zhijia College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

The rapid growth of urbanization has led to an increasing need for efficient energy management systems to optimize energy consumption and reduce environmental impacts. Wireless sensor technology emerges as a pivotal solution, providing advanced capabilities in real-time monitoring, control, and management of urban energy systems. This paper explores the practical applications of wireless sensor networks (WSN) in urban energy management, highlighting their role in smart grids, intelligent building systems, smart street lighting, public transportation, water management, and renewable energy integration. Additionally, it addresses the prominent challenges such as data security, sensor durability, network scalability, data management, energy consumption, and infrastructure integration. [1-2] The paper further discusses optimization pathways to enhance the effectiveness and sustainability of WSNs in urban energy management. By overcoming these challenges and leveraging the potential of wireless sensor technology, cities can achieve significant energy savings, operational efficiencies, and contribute to broader environmental sustainability goals. © 2024 Copyright held by the owner/author(s).

关键词： Green buildings

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Energy-Efficient Cloud Computing Through Reinforcement Learning-Based Workload Scheduling

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international Journal of advanced computer science and Applications 2025年第4期16卷 645-656页

作者： Malipatil, Ashwini R Paramasivam, M.E. Gulyamova, Dilfuza Saravanan, Aanandha Ramesh, Janjhyam Venkata Naga Muniyandy, Elangovan Ghodhbani, Refka Department of Computer Science and Engineering BNM Institute of Technology Bangalore India Department of ECE Sona College of Technology Salem India Computer Engineering Department University of Information Technologies in Tashkent Tashkent Uzbekistan Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia

The basis for current digital infrastructure is cloud computing, which allows for scalable, on-demand computational resource access. Data center power consumption, however, has skyrocketed because of demand increases, raising operating costs and their footprint. Traditional workload scheduling algorithms often assign performance and cost priority over energy efficiency. This paper proposes a workload scheduling method utilizing deep reinforcement learning (DRL) that adjusts dynamically according to present cloud situations to ensure optimal energy efficiency without compromising performance. The proposed method utilizes Deep Q-Networks (DQN) to perform feature engineering to identify key workload parameters such as execution time, CPU and memory consumption, and subsequently schedules tasks smartly based on these results. Based on evaluation output, the model brings down the latency to 15 ms and throughput up to 500 tasks/sec with 92% efficiency in load balancing, 95% resource usage, and 97% QoS. The proposed approach yields improved performance in terms of key parameters compared to conventional approaches such as Round Robin, FCFS, and heuristic methods. These findings show how reinforcement learning can significantly enhance the scalability, reliability, and sustainability of cloud environments. Future work will focus on enhancing fault tolerance, incorporating federated learning for decentralized optimization, and testing the model on real-world multi-cloud infrastructures. © (2025), (science and information Organization). All Rights Reserved.

关键词： Deep reinforcement learning

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research on Teaching IoT Courses in the New engineering Education Framework for Postgraduate Students 24

Research on Teaching IoT Courses in the New Engineering Educ...

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8th international conference on Electronic information Technology and computer engineering, EITCE 2024

作者： Yang, Yi Zhang, Jie Jiang, Xue-Qin College of Information Science and Technology Donghua University Shanghai China

ISBN: (纸本)9798400710094

Internet of Things (IoT) is a crucial component of the new engineering. This paper addresses the need of new engineering education by exploring a three-stage teaching methodology focused on fundamental knowledge, cutting-edge knowledge, and practical engineering skills for postgraduate students. The results indicate that this teaching methodology is applicable to postgraduate students with diverse knowledge backgrounds and varying research interests. These explorations and practices will serve as valuable references for the advancement of new engineering education. We anticipate that the explorations will contribute significantly to curriculum framework design of new engineering education about IoT. © 2024 Copyright held by the owner/author(s).

关键词： Students

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State observer and slow and fast time-scale design for permanent magnet synchronous motors.* 27

State observer and slow and fast time-scale design for perma...

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27th international conference on advanced Communications Technology, ICACT 2025

作者： Yoo, Heonjong Choi, Seonggon Computer information and communication research institute Chungbuk National University Cheongju-city Korea Republic of

ISBN: (纸本)9791188428137

In the presentation, the state observer is applied to the PMSM(permanent magnet synchronous motors) to estimate torque. The observer is commonly used for sensorless control using the estimation of the torque in the PMSM(permanent magnet synchronous motor). © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Permanent magnets

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Question Pair Similarity Detection and Development of Question Answering Chatbot

Question Pair Similarity Detection and Development of Questi...

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2025 IEEE international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Gupta, Abhinay Tyagi, Praveen Kumar Siddiqui, Ebtasam Ahmad Poornima Institute of Engineering and Technology Rajasthan Jaipur302022 India Vit Bhopal University School of Computing Science and Engineering MP 466114 India

ISBN: (纸本)9798331529833

The development of a question-answer chatbot involves leveraging advanced embedding techniques to optimize language understanding and response generation. The process begins with curating a diverse dataset of questions and answers, followed by preprocessing steps such as tokenization, stemming, and stop-word removal. Various embedding techniques, including traditional methods like BoW and TF-IDF, as well as modern approaches such as Word2Vec, GloVe, and BERT, are explored to determine the most effective technique for chatbot optimization. Performance metrics such as accuracy, responsiveness, and scalability guide the evaluation process. The approach encompasses data collection, preprocessing, embedding, model training, and user interaction, aiming to enhance human-computer interactions and improve information retrieval. © 2025 IEEE.

关键词： Embeddings

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Application of Improved Ant Colony Algorithm in the Path Planning Problem of Mobile Robot 24

Application of Improved Ant Colony Algorithm in the Path Pla...

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8th international conference on Electronic information Technology and computer engineering, EITCE 2024

作者： Chen, Huiyun Zhuo, Shulong Shi, Jinmei School of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

In this study, we present an advanced approach to global path planning for mobile robots, grounded in the refinement of the ant colony algorithm. The conventional implementation of this algorithm often encounters the "stalemate" issue and struggles to attain the globally optimal solution. By strategically modifying the transition probabilities of ants within the traditional framework, we mitigate the frequency of "stalemate" occurrences. Furthermore, we incorporate the roulette wheel selection methodology from genetic algorithms, thereby circumventing the tendency towards local optimization inherent in the ant colony algorithm. Through rigorous simulation experiments, we have optimized and identified the most suitable parameter combination for the refined ant colony algorithm, enhancing its applicability and performance. Finally, it proves that the improved ant colony algorithm can greatly improve the path planning efficiency through Matlab2016b. © 2024 Copyright held by the owner/author(s).

关键词： Ant colony optimization

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Hyperspectral Images for Crop Prediction using Machine and Deep Learning Models 3

Hyperspectral Images for Crop Prediction using Machine and D...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mahalakshmi, R. Kumutha, K. Sakthivanitha, M. Lakshmi, R. Bagavathi Sirajudeen, Mohamed Padmanabhan, Sankar Vels Institute of Science Technology & Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Application Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Tamil Nadu Chennai India Nilgiri College of Arts and Science Pg and Research Department of Computer Science Tami Nadu The Nilgiris India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. Machine Learning (ML) and Deep Learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector Machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data. © 2025 IEEE.

关键词： Classification Deep Learning(DL) Hyperspectral Images(HSI) Machine Learning(ML)

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NL2Code: A Hybrid NLP and Model-Driven Framework for Automated Code Generation from Natural Language and UML

NL2Code: A Hybrid NLP and Model-Driven Framework for Automat...

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2025 IEEE international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Nair, Reshma P Thushara, M.G. Amrita School of Computing Amrita Department of Computer Science & Engineering Kerala Vishwa Vidyapeetham Amritapuri Kollam India Amrita School of Computing Amrita Department of Computer Science & Applications Kerala Vishwa Vidyapeetham Amritapuri Kollam India

ISBN: (纸本)9798331529833

The process of converting natural language requirements and visual models into executable software code remains an ongoing challenge in software engineering. We developed an intelligent system that adopts Natural Language Processing (NLP) and Model-Driven engineering (MDE) to generate code from natural language requirements and UML diagrams. It then applies advanced NLP techniques for natural language specification, such as tokenization, POS tagging, semantic analysis, and information extraction. This information can be transformed into computer language construct and design patterns with the help of MDE concepts. It generates code templates, skeleton code, and real code and incorporates key principles of software engineering and technological specializations. Case studies explain how the system can improve the software development lifecycle for a development company by automatically translating the natural language and the visual models into code. The system synthesized code with 92-95 % accuracy and 92-93 % completeness in three particular cases of use, proving the efficiency of the translation process of natural language requirements and UML diagrams into executable code. The present work enriches software engineering, NLP, as well as the integration of MDE, and code generation, reducing time consumption, manual efforts, and fast communication in the Software Development Process. © 2025 IEEE.

关键词： Semantics

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Securing the Digital Frontier: An Analysis of Cybersecurity Strategies and Obstacles

Securing the Digital Frontier: An Analysis of Cybersecurity ...

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2025 IEEE international conference on Computational, Communication and information Technology, ICCCIT 2025

作者： Saravanan, V. Indumathi, G.S. James, Sathish Kumar Pandi, V. Samuthira Shakila, J. Banumathi, S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Nano Electronics Materials and Sensors Tamil Nadu Chennai India St Joseph's College of Engineering OMR Department of MBA Chennai India BNY Mellon Center PittsburghPA15219 United States Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Jeppiaar Engineering College Rajiv Gandhi Salai Department of CSE Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science India

ISBN: (纸本)9798331512965

In an era where digital connectivity is ubiquitous, the imperative to safeguard cyberspace has never been more pressing. This research delves into the multifaceted landscape of cybersecurity, examining the strategies employed by governments, businesses, and individuals to protect sensitive information and critical infrastructure from cyber threats. By leveraging a mixed-methods approach, which includes a review of existing literature, case studies, and expert interviews, the research provides a nuanced understanding of the cybersecurity ecosystem. Key findings reveal that while technological advancements have bolstered defense mechanisms, including sophisticated malware, advanced persistent threats (APTs), and insider threats, continues to outpace traditional security measures. Moreover, the study underscores the critical role of human factors, such as user error and inadequate training, as significant vulnerabilities. The research concludes with a set of recommendations aimed at enhancing cybersecurity posture, including the adoption of a proactive, adaptive defense strategy, increased investment in cybersecurity education and awareness, and the fostering of international cooperation to combat transnational cybercrime. The insights gleaned from this analysis are intended to inform policymakers, industry leaders, and cybersecurity professionals, equipping them with the knowledge to fortify the digital frontier against evolving threats. © 2025 IEEE.

关键词： Cyber attacks

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