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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 ...

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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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Efficient Selection of Optimal Sewage Treatment Method for Industries Using Fuzzy TOPSIS and Fuzzy AHP 3

Efficient Selection of Optimal Sewage Treatment Method for I...

引用

3rd IEEE Mysore Sub Section International Conference, MysuruCon 2023

作者： Prajwal, N. Rohini, B.R. Kushal, B.S. Gargi, N. Kodipalli, Ashwini Soma, Bhavani Global Academy of Technology Dept. of Artificial Intelligence and Data Science Karnataka Bangalore India Global Academy of Technology Dept. Artificial Intelligence and Data Science Karnataka Bangalore India

ISBN: (纸本)9798350340358

The management of wastewater from industrial processes is a substantial challenge when striving for sustainable industrial practices. The process of determining the most suitable method for treating wastewater in an industrial context necessitates a systematic and comprehensive approach due to the multifaceted nature of this decision-making endeavor. This research addresses the crucial challenge of selecting optimal sewage treatment methods for industrial applications. Employing a novel approach that integrates Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) and Analytic Hierarchy Process (AHP), our methodology systematically prioritizes criteria such as energy consumption, efficiency, cost, process duration, environmental impact, and reuse *** study not only contributes to sustainable industrial practices but also provides a practical decision support tool, empowering industries to make informed choices in wastewater management which safeguards public health by eliminating pollutants, preventing waterborne diseases, and ensures the ecological balance. Moreover, it fosters environmental sustainability through the reuse of treated water, conserving resources, and mitigating pollution impact. © 2023 IEEE.

关键词： Energy utilization

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Real-Time Network Monitoring: A Big data Approach 14

Real-Time Network Monitoring: A Big Data Approach

引用

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

作者： Jaswanth, Kunisetty Sruthi, S. Ramachandrula, Pranav Saravanan, S. Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India Amrita School of Computing Dept. of Computer Science Engineering Chennai India

ISBN: (纸本)9798350335095

Real-time network monitoring is a critical requirement for tracking user activities and ensuring optimal network performance. In this paper, we propose a big data approach to real-time network monitoring that leverages the capabilities of Apache Spark, MongoDB, and Apache Kafka with Python. Our approach utilizes distributed stream processing of network data, enabling real-time analytics for user activity monitoring and network performance optimization. Our approach demonstrates the effectiveness of our system through experiments with real network traffic data, showing how it can identify and track user activities in real-time. Our system can be used by network administrators and IT professionals to optimize network performance, improve user experience, and enhance security through real-time monitoring and analysis of user activity data. This approach can also be opted as a feature in SDNs. © 2023 IEEE.

关键词： Network performance

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Enacting Rural Empowerment: Implementing Cooperative Commerce in Communities using Machine Learning 2

Enacting Rural Empowerment: Implementing Cooperative Commerc...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Skandarsini, R.R. Sangavi, M.S. Harika, Ravitla Varun, C.M. RMK Engineering College Dept. of Computer Science and Business Systems Thiruvallur India

ISBN: (纸本)9798350365269

In order to revitalize rural economies through the empowerment of self-help organizations and the promotion of economic growth, this study offers a revolutionary cooperative commerce platform. The platform integrates cutting-edge features such as digital ledger and inventory management for sellers, demand forecasting, and dynamic pricing, thereby fostering optimized production and efficient marketplaces. Central to the platform's efficacy is the implementation of user segmentation, which categorizes users based on income and spending behaviors, enabling targeted marketing strategies and continuous application improvements to enhance user engagement and profitability. Leveraging machine learning algorithms, personalized user experiences are cultivated, elevating buyer satisfaction and overall engagement. Additionally, administrative tools provide valuable insights for informed decision-making. Preliminary results demonstrate improved financial management for self-help groups, heightened economic activities, and an enriched user-centric experience. Identified challenges and proposed future enhancements underscore the platform's commitment to adaptability and responsiveness to the evolving needs of rural communities, ensuring sustained relevance and impact. © 2024 IEEE.

关键词： Predictive analytics

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Deep Neural Network-Based Approach to Identify the Paddy Leaf Disease using ResNet50-V2

Deep Neural Network-Based Approach to Identify the Paddy Lea...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Islam, Md. Ashiqul Rahman, Md. Mizanur Shohan, Abdul Alim Pulok, Rishad Amin Akter, Sanzida Ahmed, Md. Tanvir Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh East-West University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350313413

South Asian countries especially Bangladesh, India, and Srilanka are very involved in paddy cultivation. In Bangladesh and India, one of the key producing crops is paddy. Worldwide more than 40 percent of the world's population derives most of its calories from rice and traditionally farmers are directly involved production of rice. But every year lot of farmers have to face a tremendous amount of loss due to paddy leaf disease. Major of the farmers do not know how to identify perfectly in the primary stage that's why they are facing loss. Due to the increase in population, it is very important to make paddy cultivation scientific and produce more. There are more than 30 types of paddy leaf disease and from there in Bangladesh, approximately 10 types of disease are quite similar. If it is possible to ensure the leaf disease in the very primary stage within a very short time, then every farmer can reduce their loss. Nowadays many researchers are interested in identifying paddy leaf disease using modern science. In this paper, we worked on paddy leaf disease detection and it has worked on 4 types of disease and 1 healthy and without any disease leaf class of paddy. This paper through an automated detection approach using deep learning techniques achieved the highest accuracy instead of the previous applicable working model and traditional manual disease detection process. In this paper, we have analyzed it using 4 different types of models. ResNet50-V2, Inception-V3, MobileNet-V2, and DensNet-201 algorithm models have been applied here and the finding of the research is, that all the models achieved more than 95% accuracy from these ResNet50-V2 has achieved the best accuracy and it is 99.06%. We hope that these deep learning models and techniques will detect the disease with high accuracy in the very beginning time and it will help reduce damage and loss of paddy cultivation. © 2023 IEEE.

关键词： Deep Neural Network DenseNet-201 MobileNet-V2 ResNet50-V2

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LiDAR-Based Elderly Fall Detection System for Indoor Environments Using Neural Network Algorithms 4

LiDAR-Based Elderly Fall Detection System for Indoor Environ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Vignesh, Puthucode Ganesh Nithin Kumar, S. Senthil Kumar, T. Jeyakumar, G. Amrita School of Computing Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798331523923

Fall detection systems are critical in elderly care and healthcare monitoring, but traditional camera-based solutions raise significant privacy concerns. This research presents a novel approach utilizing LiDAR (Light Detection and Ranging) sensors for fall detection in indoor environments, addressing privacy issues while maintaining high accuracy. We introduce LiFall, a new dataset captured using a Velodyne VLP 16 Puck LiDAR sensor, comprising 12 scenes and over 500 frames simulating various fall scenarios (including back falls, bed falls, and walking falls), contributing to the scarcity of LiDAR-based fall detection datasets. Our methodology employs two advanced deep learning architectures: Annularly Convolutional Neural Networks (ACNN) and Dynamic Graph Convolutional Neural Networks (DGCNN). Evaluating these models on both the ModelNet40 dataset and our custom LiFall dataset, results show DGCNN outperforms ACNN, achieving an accuracy of 84% on the ModelNet40 dataset with a 70:30 train-test split. On the LiFall dataset, despite challenges with dataset imbalance and achieving lower detection accuracy even after data augmentation, our approach demonstrates the potential of LiDAR-based fall detection systems. Compared to other fall detection methods, our LiDAR-based system offers significant advantages beyond privacy preservation, such as robust performance in low-light conditions and the ability to capture detailed 3D spatial information. While our current implementation is limited to laboratory settings, future work will focus on real-world deployments and further addressing dataset imbalance issues to enhance overall system performance. This research contributes to the field by providing a privacy-preserving, high-performance alternative to camera-based fall detection systems, paving the way for more acceptable monitoring solutions in sensitive indoor environments. © 2025 IEEE.

关键词： Fall detection

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Distinguish between Obese and Normal Body Types Through Gait Analysis Using Classification Models 6

Distinguish between Obese and Normal Body Types Through Gait...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Yi, Jae-Sik Han, Ji Hun Kim, Min Jeong Hong, Youn-Sik Incheon National University Dept. of Computer Science and Engineering Incheon Korea Republic of

ISBN: (纸本)9798350344349

For gait analysis, an IMU sensor was mounted on the knee and gait related data was collected. Various gait parameters such as gait time, stance swing ratio, heel strike, and toe off can be extracted from the dataset. To explore the relationship between gait parameters and individual gait characteristics, we analyzed the gait patterns of normal and obese people were analyzed based on BMI (Body Mass Index). To apply it to a classification model of machine learning, different gait cycles between subjects were normalized. Gait data was collected from eight subjects in their 20s. Using this dataset, we applied a logistic regression model, and obtained the classification accuracy of 92%. We also investigated the correlation between BMI and gait parameters and found that, the correlation between BMI and cadence was-0.66. © 2024 IEEE.

关键词： Gait analysis

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Churn Buster: Uncovering Patterns and Predicting Churn in OTT Platforms 6

Churn Buster: Uncovering Patterns and Predicting Churn in OT...

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6th Parul International Conference on Engineering and Technology, PICET 2024

作者： Anand, Akshat Sarika, D. Malviya, Harshit Vatsa, Dipti Yadwad, Sunita Adarsh Dholariya, Shreya M. Dept. Computer Science Engineering Parul Institute of Engineering and Technology Vadodara India

ISBN: (纸本)9798350369748

Churn, a common concern among business leaders, poses a significant challenge in today's fast-paced world where consumer attention spans are short. This research focuses on churn analysis within the OTT streaming platform sector, essential for retaining subscribers and ensuring business longevity. Utilising data preprocessing methods and machine learning algorithms, this study aims to identify factors influencing churn and provide insights for targeted retention strategies. The dataset includes 16 variables, with churn serving as the binary-dependent variable. data preprocessing involved addressing missing values and converting categorical data into numerical format. Four machine learning models-Random Forest, XGBoost, K-Nearest Neighbors, and Support Vector Machine-were applied, with Random Forest achieving the highest accuracy of 89%. Evaluation metrics like confusion matrices, ROC curves, and Precision-Recall curves were used to evaluate model performance, revealing Random Forest's effectiveness in churn prediction. Principal Component Analysis (PCA) was employed to handle the complexity of user data, aiding in the identification of influential churn factors. This study emphasises the importance of understanding customer behaviour and utilising machine learning to forecast subscription choices. The insights gained provide valuable guidance to OTT companies for enhancing customer satisfaction and retention strategies, ensuring competitiveness in a dynamic industry landscape. © 2024 IEEE.

关键词： Sales

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Quality-Aware Experience Exploitation in Model-Based Reinforcement Learning

Quality-Aware Experience Exploitation in Model-Based Reinfor...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Yang, Guang Li, Jiahe Geng, Ziye Luo, Changqing Virginia Commonwealth University Dept. of Computer Science RichmondVA United States

ISBN: (纸本)9798350362480

In model-based reinforcement learning (MBRL), the quality of simulated experiences is a critical bottleneck to effective policy learning. Existing research has primarily focused on reducing the generation errors of these simulated experiences but has largely ignored how the varying quality of these experiences impacts policy learning during their exploitation. To bridge this gap, we propose a novel quality-aware experience exploitation scheme, called QA2E, which dynamically exploits simulated experiences based on their assessed quality to enhance the effectiveness of model-based policy learning. Particularly, we develop a weighted Bellman backup approach to dynamically adjust the influence of simulated experiences on policy learning based on their assessed quality. Since directly measuring the quality is impractical, QA2E estimates it through the epistemic uncertainty derived from the prediction results of an ensemble of transition models. Experimental results demonstrate that QA2E significantly improves policy learning performance by more effectively exploiting simulated experiences. © 2024 IEEE.

关键词： Contrastive Learning

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Prediction of Alzheimer's Disease using Deep Learning Algorithms 2

Prediction of Alzheimer's Disease using Deep Learning Algori...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Praveen, P. Srilatha, Koleti Sathvika, Masabathini Nishitha, Edulapuram Nikhil, Madduri Sr University Dept. of Computer Science and Artificial Intelligence Warangal India Sr Engineering College Dept. of Computer Science Engineering Warangal India

ISBN: (纸本)9781665456302

Throughout the world, at least five crore individuals are thought to have the disease of Alzheimer. This Alzheimer's disease is the most well-known and prevalent kind of dementia (AD). Since the modest but significant advancements in Alzheimer's disease (AD) treatment, the diagnostic emphasis has progressively switched to the precise identification of the disease's earliest stage. In numerous attempts at clinical classification, the difficulty of differentiating pre-clinical AD from changes associated with normal aging or developed AD has been recognized. Alzheimer's disease is a neurological and brain disorder that eventually makes it difficult to do even the most fundamental tasks. It gradually destroys memory and thinking skills. It is a neurological degenerative disorder that causes both brain cell shrinkage and degeneration. The most typical reason of dementia is a gradual loss of cognitive ability, behavioral, and social abilities, limiting their capacity to operate independently. It causes both brain cell shrinkage and degeneration. As people live longer, there are more concerns about aging. Damage to brain cells can be avoided with an early diagnosis of this disease. Early detection can considerably slow or stop the growth of this disease because there is no cure for it. To stop the progression of Alzheimer's disease, early detection is essential. As a result, experts can initiate preventive care as soon as possible. They call for prompt and precise Alzheimer's disease detection in its initial and most elusive stages. The only accurate approach for diagnosis is magnetic resonance imaging (MRI) brain imaging, although exams like the Mini-Mental State Examination (Fol stein 1975), or MMSE, are routinely used for earlier diagnosis. The main objective of this research work is to devise a technique for accurately identifying and staging diseases in magnetic resonance imaging (MRI). Deep learning excels at analyzing images and predicting diseases. A growing body o

关键词： Magnetic resonance imaging

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