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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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Optimization of Transformer Design Parameters of a 20kW SiC-Based Dual-Active Bridge Converter for Enhanced Efficiency

Optimization of Transformer Design Parameters of a 20kW SiC-...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Park, Gayoung Kim, Hwigon Cui, Shenghui Seoul National University Dept. of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350376067

This paper proposes a comprehensive optimization method to determine the optimal design parameters of a high-frequency (HF) transformer in a dual-active bridge (DAB) converter. To achieve high efficiency under heavy load conditions, it is essential to minimize the conduction loss of the DAB. Not only the modulation scheme but also the turns ratio and leakage inductance of the HF transformer are critical parameters that influence the operation and efficiency of the DAB. To address this issue, this paper presents a design method based on nonlinear optimization to derive the optimal combination of HF transformer and modulation parameters that minimizes conduction loss. By adopting a zero-voltage switching (ZVS)-enhanced triple-phase-shift (TPS) modulation scheme, both conduction loss and switching loss are minimized, while the optimal modulation parameters are determined simultaneously. Experimental demonstration of the optimized parameters is performed using a SiC-based 20 kW DAB converter hardware. © 2024 IEEE.

关键词： High frequency transformers

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Design of a Brain Controlled Robotic Arm: Initial Experimental Testing 21

Design of a Brain Controlled Robotic Arm: Initial Experiment...

引用

21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Aljedaani, Lama Taha Abdelhedi, Fatma Aldahasi, Renad Abdullah Batheeb, Amal Abdullah Engineering King Abdulaziz University Dept. of Electrical and Computer Jeddah Saudi Arabia

ISBN: (纸本)9798350374131

In recent years, a significant leap has been made in various techniques to control robotics to assist people with upper limb amputations. Individuals with limb amputations have been facing significant challenges in their daily lives, where the simple and daily tasks become much more complex and take longer time, or even impossible, without depending on other people. Furthermore, traditional assistive devices often require significant physical effort, and they may be unable to perform different tasks efficiently, thus restricting the amputee's independence. Consequently, there is a need for an advanced assistive device to provide greater control and functionality to improve upper limb amputee independence. The Brain-Controlled Robotic Arm project aims to develop an advanced assistive device that allows individuals with disabilities to control a robotic arm using their brain signals. The project applies brain-computer interface technology (BCI) that allows the human brain to interact with a digital computer through electroencephalogram (EEG) technology. In this project, the robotic arm utilizes an electroencephalogram (EEG) to capture brain signals and then process those signals to get the required signal in order to control the robotic arm. © 2024 IEEE.

关键词： Brain computer interface

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Neural Network Enabled Robotic Contact Measurement 3

Neural Network Enabled Robotic Contact Measurement

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Zhou, Davide Furlan, Valentina Giulietti, Nicola Carnevale, Marco Giberti, Hermes Università di Pavia Dept. Electrical Computer and Biomedical Engineering Pavia Italy

ISBN: (纸本)9798350378009

Following the ideas put forth by industry 4.0, flexible manufacturing systems that make use of robots, sensors and artificial intelligence are gaining more and more relevance. While vision systems are fundamental for the implementation of autonomous robotic systems, they are often lacking in accuracy when the conditions are not ideal. A robotic contact measurement system could be a possible solution to measure with more accuracy portions on the surface of a work-piece where the vision systems may struggle to acquire accurately. Using a robot for contact measurement introduces uncertainties due to the dynamics of the robot and the geometry of the probe. In this work a data-driven adaptive filter is developed exploiting a trained inferential model in order to enable robots equipped with a force sensor to perform contact measurements. The model is trained using data acquired from a 3D printed part with small features representing small surface defects to be measured, as small as 0.2 mm. The result shows that the profile of the defect can be effectively reproduced through the use of model reducing the mean absolute error from 0.382 mm to 0.034 mm. © 2024 IEEE.

关键词： Adaptive filtering

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A Hybrid approach for Deepfake Detection using CNN-RNN

A Hybrid approach for Deepfake Detection using CNN-RNN

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Antad, Sonali Arthamwar, Vaishnavi Vijay Deshmukh, Rohan Kishor Chame, Akshay Uttam Chhangani, Harshita Pawan Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350373783

Reliable deepfake detection of falsified videos generated by deep learning is still a pressing challenge with its growing popularity. Deep adversarial neural networks, which educate on film and target faces to ahead facial resources and facial expressions to targets, underneath DF can without difficulty idiot facial recognition structures the usage of revealed, 3-D photos mask, or video recordings from the valid consumer's face to sensors. We make significant contributions, including building adaptive datasets, comparing primitive and deep models for training, and tunable CNN-RNN tools. The overall evaluation model shows that the results are better than the findings of the most advanced methods. It was acknowledged that Deep faking today greatly impacts our environment;It is done by placing faces on original images/videos using deep neural networks. Together with the sharing of other misinformation via digital social networks, deepfake has formed digital fakery, which has become a real problem of negative social impact and as such there is a great need for successful measures to be taken to examine Deep Fakes [1]. The approach in this research generalizes by capturing physical cues in body shape and size. Our method combines CNN for spatial analysis, capturing facial features and skin texture, and LSTM and RNN for temporal analysis, monitoring expression progress. This method provides stable performance on complex deepfake videos. The modular design allows for future expansion, such as the integration of audio analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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A Second-Order Noise Shaping SAR ADC with 8x Fully Passive Gain Circuit Technique

A Second-Order Noise Shaping SAR ADC with 8x Fully Passive G...

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2024 IET International Conference on engineering Technologies and Applications, ICETA 2024

作者： Chueh, Chuang-Hsuan Chang, Yun-Chieh Chen, Hsin-Liang Chiang, Jen-Shiun Dept. of Electrical and Computer Engineering Tamkang University New Taipei City Taiwan

ISBN: (纸本)9781837242672

This paper presents a 14-bit second-order fully passive noise-shaping successive approximation register analog-to-digital converter using capacitors and switches to perform a passive integrator. It employs two prior circuit techniques: embedded passive gain and passive signal residue summation. A circuit technique of signal charge redistribution is proposed to reduce the noise and area costs of the comparator and capacitor. Therefore, the zero of the noise transfer function can be improved with an efficient solution. This work achieves SNDR of 87.3dB with a bandwidth of 260kHz at an oversampling ratio of 16. It consumes only 96.6μW of power under a supply voltage of 1V using the 90-nm CMOS technology. The Schreier FoM would reach 182 dB. © The Institution of engineering & technology 2024.

关键词： CMOS integrated circuits

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Optimized Guard Band Allocation for TN and NTN Coexistence Using Reinforcement Learning 15

Optimized Guard Band Allocation for TN and NTN Coexistence U...

引用

15th International Conference on Information and Communication technology Convergence, ICTC 2024

作者： Jang, Jiseok Jung, Soyi Ajou University Dept. Electrical and Computer Engineering Suwon16499 Korea Republic of

ISBN: (纸本)9798350364637

Efficient interference management is crucial for the coexistence of terrestrial networks and non-terrestrial networks. This paper proposes an approach to optimize frequency band allocation by determining the optimal guard bandwidth, reducing interference, and improving coexistence between terrestrial networks and non-terrestrial networks. Simulation results offer guidelines for setting appropriate guard bandwidth, ensuring effective terrestrial networks and non-terrestrial networks coexistence. © 2024 IEEE.

关键词： Deep reinforcement learning

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A Transformer-Based Model for Image Caption Generation with Memory Enhancement

A Transformer-Based Model for Image Caption Generation with ...

引用

2024 IEEE International Women in engineering (WIE) Conference on electrical and computer engineering, WIECON-ECE 2024

作者： Ome, Shadika Afroze Azhar, Tanvir Asaduzzaman East Delta University Dept. of Computer Science & Engineering Chattogram Bangladesh Chittagong University of Engineering & Technology Dept. of Computer Science & Engineering Chattogram Bangladesh

ISBN: (纸本)9798331535476

This study proposes a convolution-free transformer-based method for generating accurate descriptions of images. A Vision Transformer is utilized as the primary encoder, replacing traditional CNN, and a Meshed Memory Transformer is integrated for generating captions. The Vision Transformer splits images into fixed-size patches for extracting visual features, which are further processed by a memory-augmented encoder to integrate prior knowledge. It ensures key features are preserved and reused and the meshed decoder combines the text embedding with the outputs from each encoding layer to generate more contextually rich and accurate captions compared to conventional approaches. The model's performance is evaluated on the Flickr30K and the MSCOCO datasets, demonstrating that the Meshed Memory architecture in our approach improves both the image encoding and caption generation step. Our model outperforms the conventional 'CNN-LSTM' models and 'CNN-Transformer' methods, achieving a 1.02% improvement in METEOR and 1.31% in SPICE on the MSCOCO dataset. Additionally, on the Flickr30k dataset, our approach exhibits superior performance across most metrics, notably a 6.37% increase in the METEOR score. © 2024 IEEE.

关键词： Encoding (symbols)

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Traction Locomotive Incipient Fault Analysis using Wavelet based Particle Swarm Optimization 3

Traction Locomotive Incipient Fault Analysis using Wavelet b...

引用

3rd IEEE International Conference on Control, Instrumentation, Energy and Communication, CIEC 2024

作者： Ray, Debopoma Kar Roy, Tamal Chattopadhyay, Surajit Mckv Institute of Engineering Dept. of Electrical Engineering WB Howrah India Ghani Khan Chowdhury Institute of Engineering and Technology Dept. of Electrical Engineering WB Malda India

ISBN: (纸本)9798350313703

The present work aspires to pursue a qualitative research on the loosening of brushes in Indian induction motor locomotives, observing the traction transformer input current. At the initial step, the locomotive has been built in programming and the current input to the transformer has been documented at normal condition. Brushes have been gradually loosened and the transformer inputs have again been recorded. Both the currents have been assessed using a novel Wavelet Transform (WT) coefficient statistical analysis based Particle Swarm Optimization (PSO) Technique. From the computed Skewness, Kurtosis and RMS values, global bests have been disintegrated and optimization has been done in PSO plane to assess different conditions in the engine associated with an incipient brush fault. Here multi-objective PSO (MOPSO) has been used for the assessment and decision making. With the outcome of the refinement, a credo has been developed at the end to segregate the faulty traction engine from the healthy one. The technique used here has been validated in offline mode with satisfactory outcome. © 2024 IEEE.

关键词： Wavelet transforms

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Anti-Counterfeit Product Identification System: A Paradigm Shift in Supply Chain Integrity 8

Anti-Counterfeit Product Identification System: A Paradigm S...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Dhore, M.L. Loya, Riya Sasne, Rohan Rokade, Tejas Chandak, Rushikesh Vishwakarma Institute of Technology Dept. Of Computer Engineering Pune India

ISBN: (纸本)9798350391770

Counterfeiting is a continuous and global issue that afflicts sectors, undermines consumer trust, and causes economic harm. To effectively fight this issue, the Anti-Counterfeit Product Identification System Using Blockchain has developed as a novel solution. This article provides an in-dept. examination of this game-changing solution, which uses blockchain technology, QR codes, smart contracts, and the Ethereum network to provide a secure and transparent platform for certifying items across several industries. This study emphasizes the system's critical role in tackling complicated supply chain difficulties by providing a tamper-proof way for verifying product authenticity. The document describes the fundamental technologies used, significant features, and essential project setup steps. Finally, this approach has the potential to drastically reduce counterfeit goods while also creating transparency and confidence among supply chain players ranging from manufacturers to consumers, making it a critical tool in today's linked and fragile marketplaces. © 2024 IEEE.

关键词： Smart contract

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