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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Balakrishnan, D. Mariappanx, Umasree Karunya, T.Blessing Shri, M.Punitha Harini, S.Kesava Harishkrishna, R. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnankoil626126 India P.S.R. Engineering College Department of Information Technology Tamil Nadu Sivakasi626140 India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamil Nadu Krishnankoil626126 India Kalasalingam Academy of Research and Education Department of Information Technology Tamil Nadu Krishnankoil626126 India

ISBN: (纸本)9798350354171

Distributed Denial-of-Service (DDoS) attacks pose a significant threat to network security and service availability. Traditional detection methods often struggle to differentiate between legitimate traffic and malicious attacks. This work explores the potential of network traffic anomaly detection for accurate DDoS attack identification. We propose a novel approach utilizing a hybrid ARIMA-SWGARCH model. The ARIMA component effectively captures the inherent seasonality and trends within network traffic data, while the SWGARCH component models the volatility clustering often observed during DDoS attacks. By analyzing the residuals of the ARIMA model with the SWGARCH framework, we aim to identify significant deviations indicative of DDoS activity. This hybrid model offers several advantages over existing methods, including improved accuracy, adaptability to varying traffic patterns, and the ability to capture both level and volatility changes in traffic flow. The effectiveness of the proposed approach is evaluated using real-world network traffic datasets, demonstrating its potential as a powerful tool for DDoS attack identification. © 2024 IEEE.

关键词： Network security

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Effective 3D Video Streaming Using 2D Video Encoding System 2

Effective 3D Video Streaming Using 2D Video Encoding System

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2nd Asian Conference on Innovation in Technology, ASIANCON 2022

作者： Patil, Vishwas Manu, T.M. Anami, Basavaraj. S. Billur, Darshankumar K.L.E. Institute of Technology Dept. of Electronics and Communication Hubballi India K.L.E. Institute of Technology Dept. of Computer Science and Engineering Hubballi India K.L.E. College of Engineering & Technology Dept. of Electronics and Communication Hubballi India

ISBN: (数字)9781665468510

ISBN: (纸本)9781665468510

The amount of video traffic on the internet is expanding at an alarming rate, necessitating the use of a high compression technology. There are several 3D video compression techniques available, such as multi-view coding and depth-image-based rendering. To apply these techniques, an entirely new codec must be introduced to the present system. In this research, a 3D video compression approach is suggested that would provide better compression with decent video quality while leveraging an existing video streaming infrastructure. The proposed method is tested on two test videos using the existing HEVC video encoding technology. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are used to evaluate the performance of the proposed 3D video compression technique. These values are then compared to the traditional depth-image-based rendering technique. There is a decrease of around 50% in video size with a 2% reduction in PSNR, and a penalty of around 7% drop in SSIM. This signifies that we benefit in terms of video size reduction while compromising a small quality level. © 2022 IEEE.

关键词： Image compression

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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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Traffic Classification Using CNN-LSTM Based on Multimedia communication System

Traffic Classification Using CNN-LSTM Based on Multimedia Co...

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Integrated Circuits and communication Systems (ICICACS), IEEE International Conference on

作者： Srinivasan Jagannathan Montater MuhsnHasan Panjagari Kavitha K. S. Vigneshwaran Neethu C Francis Department of Computer Applications Madanapalle Institute of Technology & Science Madanapalle India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Al Diwaniyah Iraq Department of Electronics and Communication Engineering CVR College of Engineering Hyderabad India Department of Mechanical Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Electronics and Communication Engineering Dhanalakshmi Srinivasan College of Engineering Technology Mamallapuram India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Network traffic classification is the process of determining and categorizing network data by identifying various kinds of services, applications, and threats. However, the challenge lies in recognizing and classifying the various class of network traffic among high volume of network traffic data. The classification with high network traffic data struggles from imbalanced data and noise. This research proposes an effective Convolution Neural Network - Long Short-Term Memory (CNN-LSTM) model to classify the network traffic based on multimedia communication system. Initially, the proposed CNN-LSTM is evaluated using Information Security centre for Excellence (ISCX) Virtual Private Network (VPN) and non-VPN traffic dataset to determine the model performance. After obtaining data, the min-max normalization is applied to transform numerical values in a standardized range followed by feature extraction using CNN to capture spatial relationships in network patterns. Finally, the proposed CNN-LSTM is performed to classify the network traffic effectively by utilizing CNN for spatial feature extraction and LSTM to learn sequential dependencies of data effectively in traffic flow. While compared to existing methods like Advanced Density-Based Spatial Clustering of Applications with Noise (A-DBSCAN), the proposed method obtains a high accuracy of 0.9876 using ISCX VPN-non-VPN traffic dataset.

关键词： Noise Telecommunication traffic Transforms Feature extraction Virtual private networks Numerical models Convolutional neural networks Multimedia communication Integrated circuit modeling Long short term memory

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Health Care Assistive System in Hospital for Doctor to View the Patient's Parameter in a Cloud Database using Light Fidelity 6

Health Care Assistive System in Hospital for Doctor to View ...

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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Ramadevi, R. Kowsalya, T. Sathish Kumar, D. Nagarajan, P. Manikandan, J. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Simats Department of Bio Medical Engineering Tamil Nadu Chennai India Muthayammal Engineering College Department of Electronics and Communication Tamil Nadu Rasipuram India Sri Sairam Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Computer and Communication Engineering Tamil Nadu Chennai India Hindusthan College of Engineering and Technology Department of Mechanical Engineering Tamil Nadu Coimbatore India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

In hospitals, conventional or wireless fidelity (Wi-Fi)-based equipment continually monitor patients' health state. Because of the rising number of approaches used, the Wi-Fi-based strategy is sluggish. Light fidelity (Li-Fi) identifies the places where Wi-Fi is applicable in this case, with increased high-speed data wireless modems. Aside from the pace aspect, Li-Fi is better suited in hospital operations for tracking the state of individuals despite human body periodic involvement. In this suggested system, we have constructed a LiFi-based patient health monitoring system with a high data-receiving speed. Whereas under the current system, the sensors may be physically checked by physicians or other specialists, this takes too long. This article demonstrates the usage of a healthcare Li-Fi system to record clinical variables such as temperature, pressure, pulse, hypoglycemia, and breathing issues using appropriate sensors. The sensor data is sent to the sink and evaluated using the microcontroller before being shown on the monitor screen in the form of tables and graphs. © 2022 IEEE.

关键词： Hospitals

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Securing unmanned aerial vehicle—heterogeneous networks using functional encryption scheme

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Multimedia Tools and Applications 2025年 1-19页

作者： Gupta, Sachin Kumar Gupta, Parul Singh, Pawan Department of Electronics and Communication Engineering Central University of Jammu UT of Jammu and Kashmir Jammu181143 India Computer Science of Engineering J.B. Institute of Technology Uttarakhand Dehradun248197 India Department of Computer Science School of Mathematics Statistics and Computational Sciences Central University of Rajasthan Ajmer- 305817 Rajasthan India

In today's scenario, as the internet expands at a faster pace, the number of data consumers also increases at an exponential rate. As the range and number of users increased, a change from traditional homogeneous networks to heterogeneous networks is in demand. However, heterogeneous networks' primary focus is to give a higher quality of service criteria to users. Furthermore, in a heterogeneous network enabled by unmanned aerial vehicles, a large number of users and devices are active. In such a diverse environment, intruder nodes may efficiently operate and transmit numerous harmful actions throughout the network. As a result, the network's entire communication is open to many types of security threats. Therefore, to secure the communication, we used the functional encryption scheme, which allows us to protect the data against intrusion attacks. The whole procedure of establishing a functional encryption scheme in unmanned aerial vehicles integrated heterogeneous network is proposed in this study in two phases: the first phase between user equipment and micro base station, and the second phase between micro base station and unmanned aerial vehicles. The proposed approach is validated using the simulation tool namely Automated Validation of Internet Security Protocols and Applications. The outcome of the security validation ensures that the proposed methodology provides the desired security and robustness to the network. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Network intrusion

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A Novel Hybrid, BERT and Deep Learning Model Network Intrusion Detection System for Healthcare electronics

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IEEE Transactions on Consumer electronics 2024年 1-1页

作者： Alferaidi, Ali Yadav, Kusum Alharbi, Yasser Alreshidi, Eissa Jaber Alreshidi, Abdulrahman Aboshosha, Bassam W. Sharma, Rohit Alkhayyat, Ahmed Aray, Daniel Gavilanes College of Computer Science and Engineering University of Ha’il Ha’il Saudi Arabia Department of Communication and Computer Engineering Higher Institute of Engineering El-Shorouk Academy El-Shorouk City Egypt Department of Electronics and Communication Engineering ABES Engineering College Ghaziabad Uttar Pradesh India Ahmed Alkhayyat College of technical engineering The Islamic University Najaf Iraq Engineering Research and Innovation Group Universidad Europea del Atlántico Santander Spain

IoMT has become an attractive playground for cybercriminals due to its market value and rapid growth. As the amount of sensitive data transmitted across IT infrastructures grows, healthcare organizations and companies that generate wearable data become targets for attackers. Recently, government agencies and healthcare organizations have prioritized collecting this data and using machine learning to protect users’ privacy. In all test cases, the proposed method performed better than any other method, including the IoMT intrusion dataset, with an accuracy increase of 2.9%. It can also monitor IoMT networks within the healthcare and medical environment to protect IoMT devices and networks from attackers. Based on the ECU-IoHT dataset, it achieves 99% performance improvement in accuracy, precision, recall, and F1-score compared with existing anomaly detection models. The proposed model shows higher detection accuracy than the existing latest state-of-arts. IEEE

关键词： Wearable technology

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Machine Learning-Based Device Modeling and Performance Optimization for OTFT

Machine Learning-Based Device Modeling and Performance Optim...

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Information Technology (OCIT), OITS International Conference on

作者： Prasanthi Lingala Bethalam Venkata Siva Sai Greeshma Patta Supraja Sanjay Kumar Matta Durga Prakash Department of Electronics and Communication Engineering SRM University- AP Guntur India Department of Computer Science and Engineering Visual Information Processing Lab SRM University- AP Guntur India

ISBN: (数字)9798331510404

ISBN: (纸本)9798331510411

In the huge growth of semiconductor industry, it is noticed that the device simulation is a very sluggish process. It is very promising to use Machine Learning (ML) techniques in device modeling as their combination will create great results in semiconductor industry and reduce the computational time. Organic Thin Film Transistor (OTFT) is a promising alternative to amorphous silicon devices due to its flexibility, low cost, and can be manufactured at reduced temperatures. In traditional TCAD simulation, at once only a single simulation of OTFT for fixed length, width and dielectric thickness can be done, for change in any of the input parameter again simulation has to be done. To avoid this ML is used to predict drain current for simultaneous changes in input parameters. This introduces a machine learning based structure to model OTFT integrated with ML algorithm named Random Forest Regressor (RFR). ML based device model for p-type OTFT takes length, width and thickness of dielectric layer as input parameters and drain current as output parameter. Experimental results has shown that our ML-based model can predict drain current accurately. $\mathbf{R}^{2}$ -value is found be around 0.997253. ML based performance optimization is a promising alternative to traditional technology computer aided design (TCAD) tools. The highest $\mathbf{I}_{\text{ON}}/\mathbf{I}_{\text{OFF}}$ ratio, very high ON current (I ON ), very low OFF current (I OFF ) is achieved for OTFT. $\mathbf{I}_{\text{ON}}/\mathbf{I}_{\text{OFF}}$ ratio is obtained to be 10 11 . The trained RFR models can accelerate the optimization in terms of performance and serves as promising alternative.

关键词： Machine learning algorithms Computational modeling Semiconductor devices electronics industry Prediction algorithms Organic thin film transistors Dielectrics Optimization Tuning Random forests

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GCN-Integrated Multi-Agent Reinforcement Learning (MARL) with Modified Firefly Algorithm for Cross-Layer Optimization in Cognitive Sensor Networks 3

GCN-Integrated Multi-Agent Reinforcement Learning (MARL) wit...

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3rd International Conference on Integrated Circuits and communication Systems, ICICACS 2025

作者： Jakranpally, Karthik Babu, Nagabathula Ramesh Babu, Doddavarapu Sreenu Rakesh, K. Barve, Amit Shelke, Atmaram Valiant It Services 4000 Stanwick ave SacramentoCA95835 United States J.B. Institute of Engineering & Technology Department of Electronics and Communication Engineering Hyderabad India Bharatratna Indira Gandhi College of Engineering Department of Electronics and Communication Engineering Solapur India Malla Reddy Engineering College for Women Department of Electronics and Communication Engineering Hyderabad India Parul University Parul Institute of Technology Faculty of Engineering and Technology Department of Computer Science and Engineering P.O.Limda Gujarat India Pune India

ISBN: (纸本)9798331508456

CSN s are integrating cognitive abilities, making it challenging to achieve optimized cross-layer performance with limited resources. Non-coordinated techniques typically demonstrate lower proficiency in dynamic spectrum management and network synchronization. We introduce Graph Convolutional Networks (GCN) and Multi-Agent Reinforcement Learning (MARL) in our architectural solution, with the help of the specially designed Firefly Algorithm. The framework implements a distributed learning process, utilizing GCN for local topology perception and MARL for cooperative spectrum and optimal routing decisions. The results of experimental analysis show large gains compared to traditional methods. The proposed model provided a 94.2% recognition rate in spectrum hole identification, an 89.7% enhancement in network throughput, and a 78.5% diminution in end-to-end delay. The energy efficiency surpasses baseline techniques by 67.3%, while the raw packet delivery reliability remains at 92.8%. Integrating GCN with MARL and the improved Firefly Algorithm can significantly improve cross-layer optimization, offering a promising solution for the next generation of sensor networks. © 2025 IEEE.

关键词： Reinforcement learning

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Optimizing Cloud Security with CNN and XGBoost Models for Intrusion Detection Systems

Optimizing Cloud Security with CNN and XGBoost Models for In...

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Integrated Circuits and communication Systems (ICICACS), IEEE International Conference on

作者： Amit Karbhari Mogal V. Anitha Preeti Nitin Bhatt Kriti Srivasatava S. Rukmani Devi Anvesh Perada Department of Computer Science and Application MVP Samaj's CMCS College Nashik India Department of Computer Science and Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Trichy India Department of Electronics & Communication Engineering IIMT College of Engineering Greater Noida India Department of Computer Science and Engineering (Data Science) Dwarkadas J Sanghvi College of Engineering Vile Parle India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai India Department of Electrical and Computer Engineering Drexel University Philadelphia United States of America

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Cloud computing and Internet growth have simplified certain formerly difficult activities. This advancement has also revealed many security weaknesses. Due to cyberattacks, organizations need Intrusion Detection Systems (IDS) to protect their data and networks. Preprocessing, feature selection, and model training comprise the suggested strategy. Preprocessing includes data cleansing, standardization, and labeling. Information gain, chi-square, and PSO are used to choose features. CNN-XGBoost trains the model. The CNN-XGBoost model outperforms solo CNN and XGBoost with an average accuracy of 92.08%. This accuracy proves the hybrid method's cloud computing breach detection usefulness. The study underlines the importance of enhanced IDS models in cyber risk reduction. CNN's feature extraction and XGBoost's classification power provide a powerful, efficient, and reliable cloud intrusion detection model. Modern organizations benefit from this network security method.

关键词： Training Cloud computing Accuracy Computational modeling Standards organizations Intrusion detection Standardization Feature extraction Reliability Integrated circuit modeling

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