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An Optimized Hybrid Deep Learning Framework for Monitoring Botnet Attacks in IoT Networks 5

An Optimized Hybrid Deep Learning Framework for Monitoring B...

引用

5th international conference on IoT Based control Networks and intelligent systems, ICICNIS 2024

作者： Manimaran, A. Sathish Kumar, P.J. Kartheesan, L. Kumutha, D. Surendran, R. Karpaga Vinayaga College of Engineering and Technology Technical Sciences Department of Electronics and Communication Engg Chengalpattu India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr.Sangunthala R&D Department of Computer Science and Engineering Institute of Science and Technology Chennai India Jeppiaar Institute of Technology Department of Electronics and Communication Engg Sriperumudur India Saveetha School of Engineering Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518097

The serious threat of botnet attacks in the IOT world today can be effectively addressed with deep learning (DL). However, to train the model, large and complex data sets are required, which adds cost and necessitates expensive hardware to perform intricate mathematical operations. The IoT plays a significant challenge in memory constrained by implementing the DL. The hybrid Principal Component Analysis (PCA) and Autoencoder is proposed to measures the performance to lower the IoT network traffic data and consumes bulky information. Following that, reduced datasets are examined and trained using hybrid Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) algorithms. The hybrid deep learning algorithms attain the feature to enhance the precision, accuracy, recall, and F1 scores 96.51%, 100%, 96.51%, and 98.22%, which were higher than the conventional methods. The model's performance is analyzed using extensive experiments. © 2024 ieee.

关键词： Bot (Internet)

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Analysis of Post-Quantum Cryptography for Internet of Things 6

Analysis of Post-Quantum Cryptography for Internet of Things

引用

6th international conference on intelligent Computing and control systems, ICICCS 2022

作者： Sajimon, P.C. Jain, Kurunandan Krishnan, Prabhakar Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks India

ISBN: (纸本)9781665410359

Cryptographic algorithms are crucial for secure communication in the Internet of Things (IoT). Quantum computers threaten public key cryptography algorithms like RSA (Rivest, Shamir, Adleman) and Diffie-Hellman (DH) key exchange schemes, which are widely in use today. As cryptographic algorithms are the foundation of secure internet communication, the development of Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC) is essential to protect transactions over the internet. However, solutions like Quantum Key Distribution require heavy infrastructure. So, it is not feasible for the Internet of Things with fewer resources in computing power and form factor. Post-Quantum Cryptography is helpful on these devices. But, it needs further developments and improvements. This paper analyzes cryptography algorithms and protocols of IoT with different categories. Integrated PQC of National Institute of Standards and technology (NIST) third level finalists on Raspberry Pi 4 (RPI4) board with Linux operating system. The performance of Post-Quantum Cryptography schemes for IoT has been evaluated. Finally, a Post-Quantum Cryptography scheme has been suggested for the Internet of Things based on the performance analysis. © 2022 ieee.

关键词： Internet of things

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Using Algorithmic Modeling to control User Access Based on Functioning Table

Using Algorithmic Modeling to Control User Access Based on F...

引用

ieee international IOT, Electronics and Mechatronics conference (IEMTRONICS)

作者： Kabulov, Anvar Saymanov, Islambek Yarashov, Inomjon Karimov, Anvar Natl Univ Uzbekistan Fac Appl Math & Intelligent Technol Tashkent Uzbekistan

ISBN: (纸本)9781665486842

Today, the development of modern digital technologies requires them to solve existing cyber security problems. Thus, in this study, by building an algorithmic model for user access control based on a functional table, information security of existing systems can be used to create imitation of processes occurring in the system, which allows you to apply and analyze specific problems. This study attempted to focus on algorithmic issues. In this algorithmic model based on Functioning table, the user's access control process is active at all transitions in the Petri net, and cases can be reached and detected with an initial marking, a sequence of transitions leading to a particular state. The algorithmic model based on the Functioning table of user access control has a strictly formal description that allows you to analyze the process of interaction of three subjects of information relations. This model allows switching the algorithmic model of user access control to the system to software implementation.

关键词： algorithmic modeling functioning table security objective attribute datalogical model Petri net

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Model Predictive control of Self-Contained Electro-Hydraulic Actuator Driven by Permanent Magnet Synchronous Motor 3

Model Predictive Control of Self-Contained Electro-Hydraulic...

引用

3rd international conference on intelligent Power and systems, ICIPS 2023

作者： Wang, Qiwu Gao, Jianbo Li, Yu Dong, Guofeng Liu, Daojie Zhi, Wenhui Sun, Yuanxiang Wang, Yongdu Laser Institute Qilu University of Technology Shandong Academy of Sciences Jinan China Technical University of Munich Munich Germany

ISBN: (纸本)9798350328691

Self-contained electro-hydraulic actuators driven by servo motors is an established and successful concept which has higher energy-efficiency comparing to traditional valve-controlled devices. However, these novel actuators have specific operational constrains given by the components of motors and pumps, which cannot be systematically handled by the traditional Proportional-Integral (PI) controllers. This paper proposes a Model Predictive control (MPC) method to facilitate the control design and improve the performance of the self-contained electro-hydraulic actuators, detailed models of the electrical, mechanical and hydraulic subsystem are developed, and Finite control Set Model Predictive control (FCS-MPC) method is adopted as the torque controller of permanent magnet synchronous motor (PMSM). Furthermore, the hydraulic transmission dynamic is regulated by Continuous control Set Model Predictive control (CCS-MPC) algorithm. The system is simulated in MATLAB/Simulink which demonstrates the accuracy and effectiveness of the proposed method. ©2023 ieee.

关键词： Model predictive control

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Enhanced Detection of DoS/DDoS Attacks in SDN Using Ensemble and Hybrid CNN-LSTM Models

Enhanced Detection of DoS/DDoS Attacks in SDN Using Ensemble...

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2024 ieee international conference on Computing, Applications and systems, COMPAS 2024

作者： Afroj, Maharin Rifat, Khan Md Sadbin Rahman, Md. Saifur Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Software-Defined Networking (SDN) faces significant challenges in mitigating Attacks known as denial of service (DoS) and distributed DDoS due to its centralized control plane architecture. This centralization makes the control plane an attractive target for attackers, highlighting the need for effective detection and mitigation strategies. In this study, we propose three innovative methodologies to enhance the accuracy of detecting DoS/DDoS attacks in SDN environments. Our first methodology employs an ensemble model with a voting classifier, which achieves a detection accuracy of 97.4%. This approach leverages multiple weak learners to enhance the robustness of detection. The second methodology utilizes an ensemble with a stacking technique, attaining a superior accuracy of 99%, demonstrating the efficacy of integrating multiple base models in a hierarchical fashion. The third approach introduces a novel hybrid model that blends LSTM networks with CNN, achieving a competitive accuracy of 98.51%. A comparative analysis of these methods reveals that, while all three significantly enhance detection rates, the stacking ensemble technique outperforms the others. This finding underscores the potential of advanced ensemble techniques in effectively addressing the dynamic and evolving cyber threats within SDN environments. Our study offers valuable insights into strengthening SDN infrastructures against DoS/DDoS attacks, contributing to the ongoing efforts to secure modern network architectures. © 2024 ieee.

关键词： Denial-of-service attack

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Design of prediction model for multiple feature-type problems based on improved PSO-RBF neural network 6

Design of prediction model for multiple feature-type problem...

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2023 ieee 6th international conference on Information systems and Computer Aided Education, ICISCAE 2023

作者： Yang, Ruiqi Shang, Yujing Yao, Jiangxiu Su, Rui Zhou, Junjie Tang, Liulu Wuhan University of Science and Technology School of Mechanical Automation Wuhan China Hefei University of Technology School of Computer and Information Engineering Xuancheng China Wuxi Taihu University School of Intelligent Equipment Engineering Wuxi China Changzhou University School of Urban Construction Changzhou China Nanjing University Business School Nanjing China

ISBN: (纸本)9798350313444

Prediction problems for complex systems have been of wide interest. Since the input features of such problems often contain both qualitative and quantitative variables, it is not possible to preprocess the features by conventional methods. Moreover, due to the complexity of the problem, using only a single model for prediction may lead to insufficient accuracy. In this paper, a prediction model for multiple feature-type problems based on an improved PSO-RBF neural network is designed, in which qualitative and quantitative features are processed separately. The qualitative features are first screened using information gain and then the quantitative features are downscaled using factor analysis. After processing the features, they are predicted using improved PSO-RBF neural network. The results show that the resulting optimal SSE is 77136 and the prediction accuracy is high. © 2023 ieee.

关键词： Complex systems Factor analysis Improved PSO-RBF neural network Information gain Multiple feature-type SSE

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Prediction of Silicon Content in Molten Iron of Blast Furnace Based on Multi-dimensional Sequential Characteristic Reconstruction

Prediction of Silicon Content in Molten Iron of Blast Furnac...

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Industrial cyber-Physical systems (ICPS)

作者： Zhuofu Zhang Weihua Cao Jie Hu Qifu Chen Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

Molten iron is the primary output of blast furnace production. The content of silicon in molten iron clearly correlates with blast furnace temperature. However, due to the intricate conditions of blast furnace production, the silicon content in molten iron is nonlinear and unstable. Therefore, this paper adopts variational mode decomposition (VMD) to decompose and extract the feature information of the real silicon content data of LY Steel in March 2022, then uses Grey Wolf optimization (GWO) algorithm to optimize the parameters of the support vector regression (SVR) prediction model, and takes the decomposed data as model input for experimental verification. By comparing the predicted results with the real historical data of blast furnace production, it is found that the degree of fit is about 94.2%, which offers a new idea for the prediction of silicon content.

关键词： Support vector machines Production Predictive models Blast furnaces Prediction algorithms Feature extraction Silicon

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Optimized Transfer Learning Model for Lung Cancer Stage Classification Using Computed Tomography Images 5

Optimized Transfer Learning Model for Lung Cancer Stage Clas...

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5th international conference on IoT Based control Networks and intelligent systems, ICICNIS 2024

作者： Deepika, R. Shanmugam, P. Moorthi, K. Kumar, P. K. Manoj Swarna, S.L. Ramnath, K. B v Raju Institute of Technology Department of Artificial Intelligence and Data Science Telangana Narsapur India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Sri Sairam Engineering College Department of Electronics and Communication Engineering Chennai India School of Computational Intelligence Joy University Tirunelveli India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India K.L.N College of Information Technology Department of Computer Science and Engineering Tamilnadu Madurai India

ISBN: (纸本)9798331518097

Lung cancer has become one of the primary causes of death worldwide due to cancer, which indicates the necessity of early diagnosis. In this regard, the present work proposes an optimized model of transfer learning based on characterizing different stages of lung cancer by using computed tomography images acquired from the IQ-OTH/NCCD lung cancer database downloadable from Kaggle. It makes use of a two-step process: first, a U-Net model is used on the patient's CT images to reconstruct anatomical features and reduce noise;second, a ShuffleNet architecture is used to predict the patient's CT stage using the reconstructed image. In addition, to enhance classification, PSO was incorporated into the model so that it can optimize the hyperparameters. The accuracy of the model was at 97.85 percent, and it suggests that the model can become a very useful tool for clinicians to bring about earliest identification and staging for lung cancer. © 2024 ieee.

关键词： Lung cancer

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Detection and Recognition of Sino-Nom Characters on Woodblock-Printed Images 15

Detection and Recognition of Sino-Nom Characters on Woodbloc...

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15th international conference on Knowledge and systems Engineering, KSE 2023

作者： Dao, Trong Tuan Le, Cong Thuong Ngo, Thi Duyen Le, Thanh Ha Vnu University of Engineering and Technology Faculty of Information Technology Hanoi Viet Nam

ISBN: (纸本)9798350329742

There are many documents written in Sino-Nom capturing a wide range of historical, political, and literary facets of Vietnamese culture. To contribute to the process of preserving those documents, we focus on the task of building Optical Character Recognition models for Sino-Nom characters using a semi-supervised approach. Also, we propose a pipeline for collecting needed Sino-Nom document images which are used for training and evaluating our models. Being evaluated on our collected dataset, our OCR baseline achieves F1 scores of over 0.97 in detection and top-l and top-5 accuracies of 80.1% and 90.1% in recognition, respectively. © 2023 ieee.

关键词： Optical character recognition

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IoT Enabled Hybrid PV Powered PMSM Drive for E-Compressor systems 3

IoT Enabled Hybrid PV Powered PMSM Drive for E-Compressor Sy...

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3rd ieee international conference on intelligent Technologies, CONIT 2023

作者： Byri Manoj, S. Sanjay, K. Banerjee, Swapnil Dattatarya, K. Raja Singh, R. Vellore Institute of Technology Vellore Dept. of Electrical and Electronic Engg. Vellore India Cognizant Pune India Vellore Institute of Technology Vellore Dept. of Energy and Power Electronics Vellore India

ISBN: (纸本)9798350338607

Solar PV powered air conditioning system provides an effective means to minimize energy usage and foster sustainability. While also reducing their environmental impact by utilizing renewable energy sources. This paper presents a novel approach for designing an Internet of Things (IoT) enabled hybrid photovoltaic (PV) powered permanent magnet synchronous motor drive for E-compressor systems. The IoT-enabled system allows for real-time monitoring and control of the available solar energy. The system is designed to improve the efficiency and reliability of E-compressor systems. The work includes IoT integrated Matlab simulation for a hybrid PV powered E-compressor system. The system is analyzed with a 1.5-ton PMSM driven compressor system and their results are demonstrated in terms of performance and energy savings. © 2023 ieee.

关键词： Permanent magnets

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