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Deep Learning-Driven Cloud Intrusion Detection: Internet of Things and Cloud Networks 1

Deep Learning-Driven Cloud Intrusion Detection: Internet of ...

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

作者： Moon, Iffath Tanjim Islam Mondal, Md. Nazrul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331518578

In this paper, we have utilized deep learning approaches to detect cloud intrusion for the Internet of Things (IoT). The emerging growth of IoT and cloud environments has revolutionized many industries by allowing real-time data sharing and storage. Cloud platforms offer the essential infrastructure for managing the massive volumes of data produced by IoT devices. However, due to several flaws, including insufficient encryption, insecure APIs, and weak authentication, intruders can exploit the vulnerabilities to interrupt services and obtain unauthorized access. Therefore, deep learning can be an effective solution to detect cloud intrusion in IoT. In this research, we have developed individual convolutional neural network (CNN) and long short-term memory (LSTM) models, along with a hybrid CNN-LSTM model for multiclass classification and binary classification to detect cloud intrusion. In addition, we have examined which models perform the best for cloud intrusion detection for IoT among the individual and hybrid models. The proposed models are effective in detecting cloud intrusion, as CNN can extract spatial features, and LSTM can capture temporal dependencies from network traffic data. Furthermore, we have utilized one of the most recent and comprehensive datasets for IoT, the ToN-IoT dataset, containing real-world data. In multiclass classification, the hybrid CNN-LSTM has given the best performance, achieving an accuracy of 97.65%, precision of 97.72%, recall of 97.65%, f1 score of 97.67%, and AUC of 99.93%. In binary classification, all CNN, LSTM, and hybrid CNN-LSTM have demonstrated high performance, where LSTM has achieved the highest accuracy of 99.97%. Moreover, we have evaluated the performance of multiclass classification for detecting nine distinct kinds of attacks. In this instance, CNN has shown better performance in detecting backdoor attacks. Thus, this research can make the IoT platform more secure by efficiently detecting cloud intrusion. © 2025

关键词： Network intrusion

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Exploring Interactive Architecture in a Multidisciplinary Approach 2nd

Exploring Interactive Architecture in a Multidisciplinary Ap...

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2nd international conference on Trends in Architecture and Construction

作者： Chaturvedi, Divyanshi Satpal Nirmala Deenbandhu Chhotu Ram Univ Sci & Technol Dept Architecture Murthal Haryana India

ISBN: (纸本)9789819749904;9789819749881;9789819749874

Architecture has evolved continuously, aligning itself with the evolving needs and desires of society. The idea of interactive architecture emerged with the development of technologies and increment in environmental, functional, and human problems. Interactive architecture refers to the architecture which can change, adapt, and reconfigure to the needs of users and the environment in real time. A Space's Interaction, flexibility, and integration both with the environment and individuals can be done with the help of IoT, artificial intelligence, sensors, actuators, etc. This research paper aims to explore diverse ways and potential in interactive architecture in responding to user needs and environmental dynamics. In the Indian context, the application of interactive architecture is limited, and embracing it will ensure innovation, efficiency, and enhanced user experience. We shouldn't lag in the global evolution of design, adopting these advancements aligns with progress and enriches our built environment. To understand the user-centric perspective, a questionnaire survey (primary) was conducted involving 204 participants along with interviews (secondary). The study encompasses respondents of multiple disciplines, ranging from architects, architecture students, data science students, educators, and others to provide comprehensive insights. The finding of the paper presents that interactive architecture has the potential to revolutionize the way we live and work. Also, there are challenges like high cost, complex technology, and lack of understanding and awareness among the public. In conclusion, this research advances the existing theoretical discourse on interactive architecture and offers more practical insights for architects and other stakeholders in shaping the built environment.

关键词： Smart building Technological advancement User experience Human-building-computer interaction Sustainability

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NMT Framework for Verse Translation from Hindi to English with CDEC 1

NMT Framework for Verse Translation from Hindi to English wi...

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

作者： Chakrawarti, Rajesh Kumar Pathik, Nikhlesh Chauhan, P.S. Jaiswal, Lucky Sushila Devi Bansal College of Engineering Principal Investigator Department of CSE Indore India MANIT Department of CSE Bhopal India Sushila Devi Bansal College of Technology Department of ME Indore India Sushila Devi Bansal College of Engineering Department of CSE Indore India

ISBN: (纸本)9798331518578

In the modern era, AI and ML are highly demanding Technologies. AI and ML are used to train machines and computers to do types of human work or exactly like human beings. With the help of AI and ML people solve real-life problems. 'Chat-GPT' is one of the major examples. 'Chat-GPT' is an 'AI' tool to generate speech and text and it can be manipulated according to them Language translation is one of the important tasks. Because in this world there are uncountable languages is here. And people need to communicate with each other. So, there are many translators present in the market like Google translator, Microsoft Bing Translator, Yandex and many more translators. However, this type of translator translates only the normal text from the source language to the target language. So our research is based on verse(poetic) translation like poems, mythological text etc this type of verse translation is the most challenging task. So our researcher reached the NLP tool named CDEC. Which has already translated the Chinese poem into English and was developed by Cries Dyler this research motive is to run the CDEC and train the verse model from Hindi to the English language. After Completing this task people can translate verse text. All the translated text was analyzed by BLEU Bilingual Evaluation Understudy. This review aims to analyze how to translate verse text from Hindi to English Language with CDEC. © 2025 IEEE.

关键词： computer aided language translation

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Enhanced Multimodal Depression Detection With Emotion Prompts

Enhanced Multimodal Depression Detection With Emotion Prompt...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Teng, Shiyu Liu, Jiaqing Sun, Hao Chai, Shurong Tateyama, Tomoko Lin, Lanfen Chen, Yen-Wei College of Information Science and Engineering Ritsumeikan University Shiga Japan College of Computer Science and Technology Zhejiang University Hangzhou China Department of Intelligent Information Engineering Fujita Health University Japan

ISBN: (纸本)9798350368741

Depression is a pervasive mental health disorder that remains frequently undiagnosed and untreated due to societal barriers and the subjective nature of its symptoms. Leveraging recent advances in large language models (LLMs), we propose a novel depression detection pipeline that generates emotion prompts tailored to individual data, enhancing detection accuracy. Our approach integrates cross-modality fusion via cross attention mechanisms to combine depressive and emotional features, creating a comprehensive representation of depression indicators. Evaluated on the E-DAIC and EATD datasets, our method outperforms state-of-the-art techniques, demonstrating its potential for more precise emotion-based depression detection. © 2025 IEEE.

关键词： Depression detection Emotion prompt Large language model Multi-modal fusion

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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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AI Applications in Cybersecurity for Proactive Threat Management 1

AI Applications in Cybersecurity for Proactive Threat Manage...

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

作者： Mishra, Manas Kumar Grover, Varun Jain, Rahul Changalva, Ramsundernag Dhami, Aatishkumar Goel, Om G.L. Bajaj Insitute of Technology and Management Department of Computer Science and Engineering Greater Noida201306 India Carlson School of Management University of Minnesota MinneapolisMN55455 United States Texas A&M University College StationTX77840 United States Liverpool John Moores University Liverpool United Kingdom California State University Los AngelesCA90032 United States ABES Engineering College U.P. Ghaziabad India

ISBN: (纸本)9798331518578

AI in cybersecurity has been gaining ground for years now, and it is used to help manage threats beyond monolithic cyber-attacks. AI today plays a critical role in proactive threat management. It is being used in attempting to understand and mitigate threats before they can escalate and cause harm, as it acts as the first line of understanding and defense against them. Tools/Techniques Used for AI-powered Security Systems AI-powered security systems use advanced algorithms and machine learning techniques to monitor network activity continuously and detect anomalies and potential threats in real-time. These systems may also learn from past attacks and data breaches to evolve and refine their abilities to detect and prevent similar attacks in the future. Avoiding cyber threats from killing even half of any possible motive is possible. In cybersecurity, AI applications are capable of automating responses to detected threats, mitigating the repercussions of attacks and shortening the time needed to counteract security incidents. This is especially crucial in a time when the threat landscape changes every day, and attacks happen so regularly that traditional security controls and human intervention tend to fall short more often than not. This proactive approach enables organizations to identify potential threats before they escalate, providing a higher level of protection against a growing number of cyber threats that can compromise their data, assets, and reputation. Nevertheless, the way forward is not easy because the future of Cybercrime has arrived in the present which include Cyberattacks that violates AI tools to get an edge. © 2025 IEEE.

关键词： Cyber attacks

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A Review on the Role of Blockchain Technology for Decentralized Identity Management: A Future Without Passwords 4

A Review on the Role of Blockchain Technology for Decentrali...

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

作者： Raut, Rohini Gourshettiwar, Palash Thakre, Gayatri Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Advanced identity mechanisms are essential for secure client-identifiable proof in the unambiguously computerized world. Because the board frameworks rely on aggregated databases, fraud, data breaches, and unauthorized access are significant security risks. By providing a decentralized, immutable structure, blockchain technology advances security and protection. Through Self-Sovereign Identity, blockchain gives people authority over their own data. While also improving confirmation and check mechanisms. The potential of blockchain-based Identity, this article examines the board frameworks, focusing on five essential components: authentication, privacy, trust, integrity, and simplicity. It examines how interoperability, flexibility, and collaboration with outdated frameworks prevent decentralized IDMS being more widely used. The study also covers customer satisfaction, legal concerns, and potential future developments, such as Web 3.0, security enhancements, and integration with decentralized money. By addressing these problems and looking into new possibilities, blockchain-based IDMS may be able to reform advanced identity across the entire board. © 2025 IEEE.

关键词： Decentralized finance

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Research on the Next POI Recommendation System Based on Graph Neural Networks and Attention Mechanism 1

Research on the Next POI Recommendation System Based on Grap...

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1st international conference on Intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Li, Weilong School of Computer Science Guangdong University of Petrochemical Technology Guangdong Maoming525000 China

ISBN: (纸本)9781643685779

In order to enhance the accuracy of the POI recommendation system, a model that dynamically captures user behavior patterns was established by combining Graph Neural Networks (GNNs) and the Attention Mechanism. The experimental results show that the model improved the Top-1 accuracy by 15%, and the Top-5 and Top-10 accuracy by 13% and 15%, respectively. The conclusion indicates that the proposed method significantly improves recommendation performance and provides effective support for personalized recommendations. © 2025 The Authors.

关键词： Neural network models

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Application of OOSEM Method in Requirements Analysis of System Security engineering 5

Application of OOSEM Method in Requirements Analysis of Syst...

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5th international conference on advances in Electrical, Electronics and Computing Technology, EECT 2025

作者： Xin, Chenliang Zhou, Yanzhang Jiang, Ning Ding, Guohui Shenyang Aircraft Design and Research Institute Aviation Industry Corporation of China Shenyang China School of Computer Shenyang Aerospace University Shenyang China

ISBN: (纸本)9798331541545

OOSEM (Object-Oriented Systems engineering Method) is a MBSE(Model-Based Systems engineering) method. OOSEM is scenario-driven and it can analyze the requirements from system-level to component-level. As a selected MBSE method, OOSEM is applied in System security engineering. When focusing on requirement analysis, the OOSEM approach includes system requirements analysis. In OOSEM approach of this paper, SysML model is provided according to requirements analysis of Boundary Security System specially for concerning requirements analysis. In SysML model of Boundary Security System, package diagrams model, use case diagrams model, state machine diagram model and sequence diagram model respectively are applied in OOSEM process. These models provide strong design support for requirement analysis of system security engineering. © 2025 IEEE.

关键词： System of systems

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