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SentinelScan: Advanced Network Scanner and Packet Detection Suite 8

SentinelScan: Advanced Network Scanner and Packet Detection ...

引用

8th international conference on Inventive systems and control, ICISC 202

作者： Seshapriyan, T. Dinesh, S.M. Godwin Ponsam, J. Srm Institute of Science and Technology School of Computing Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798350386578

In today's interconnected digital landscape, safeguarding networked systems and preserving data integrity needs more attention. With the evolution of cybersecurity threats, the emergence of SentinelScan signifies a proactive response to these challenges. SentinelScan employs advanced packet manipulation techniques and realtime packet sniffing capabilities to offer a comprehensive defence against a wide spectrum of cyber threats. It integrates pivotal concepts in network security, including cryptographic algorithms, authentication mechanisms, secure communication protocols, and adherence to security standards. SentinelScan's efficacy depends on the use of Scapy, a robust packet manipulation tool for enabling realtime packet sniffing. Customized Python scripts enable Scapy to intercept and analyze network packets, providing valuable insights into traffic patterns, protocol utilization, and potential security vulnerabilities. The proposed model comprises of various layers of the networking stack, including application layer protocols (client-server architecture, web, and email protocols) and transport layer protocols (UDP and TCP), detailing the connection establishment, reliable data transfer, and congestion control mechanisms. The proposed research methodology emphasizes precision, dependability, and reproducibility, ensuring rigorous environment configuration and documentation. These measures enhance the robustness of the findings, making SentinelScan an efficient tool for safeguarding digital assets. © 2024 ieee.

关键词： Data transfer

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intelligent Verbal Interaction Methods with Non-Player Characters in Metaverse Applications 5

Intelligent Verbal Interaction Methods with Non-Player Chara...

引用

5th ieee international conference on Advanced Information and Communication Technologies, AICT 2023

作者： Pariy, Vladislav Lipianina-Honcharenko, Khrystyna Brukhanskyi, Ruslan Sachenko, Anatoliy Tkachyk, Fedir Lendiuk, Dmytro West Ukrainian National University Department for Information Computer Systems and Control Ternopil Ukraine West Ukrainian National University Department of Business Analytics and Innovative Engineering Ternopil Ukraine Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine Kazimierz Pulaski University of Technology and Humanities in Radom Department of Informatics and Teleinformatics Radom Poland West Ukrainian National University Department of Taxes and Fiscal Policy Ternopil Ukraine

ISBN: (纸本)9798350372571

This article is devoted to the development of intelligent Verbal Interaction Methods with Non-Player Characters in Metaverse Applications. The creation of a character model using MetaHuman Creator and the implementation of dialogue animation are considered. The construction of answers based on text analysis and their submission to the chat window is described. The system has been tested and found to be stable and have a satisfactory response time of approximately 2 seconds. The results show the successful implementation of methods of verbal interaction with non-game characters in metaverse, which can be useful for further research and development in this area. © 2023 ieee.

关键词： Animation

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Comparison of LDPC codes based on CCSDS standard and 5G standard in underwater channels

Comparison of LDPC codes based on CCSDS standard and 5G stan...

引用

2023 international conference on cyber-Physical Social Intelligence, ICCSI 2023

作者： He, Chong Ma, Junyan Lv, Hongfei Song, Yingjie Ding, Jie Chen, Min Hainan University The College of Information and Communication Engineering Haikou China

ISBN: (纸本)9798350312492

In the process of information transmission, in order to provide the system with certain error correction and anti-interference capabilities, the participation of channel coding is required. Low Density Parity Check (LDPC) codes, as a member of linear block codes, have been increasingly used in recent years. In order to adapt to different scenarios and address the main impacts during transmission, LDPC codes based on multiple standards have emerged, but no standards suitable for underwater channel transmission have emerged. In this article, in order to find more suitable LDPC codes for underwater channel transmission, LDPC codes based on two standards were selected. The underwater channel was simulated using a generalized gamma distribution model, and the same normalized minimum sum decoding method was used. Starting from two aspects of iteration times and normalization factors, this paper analyzes the error rate of LDPC codes based on the Consultative Committee for Space Data systems (CCSDS) and the 5th Generation mobile communication systems (5G) standards in underwater channel transmission to determine the performance of these two LDPC codes. It was found that LDPC codes based on the CCSDS standard have significant advantages in low signal-to-noise ratio situations and have better performance in underwater channel transmission. © 2023 ieee.

关键词： 5G mobile communication systems

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Carrier Frequency Offset Estimation and Compensation Technique Based on Pilot Tones for OFDM Wireless Communication systems 15

Carrier Frequency Offset Estimation and Compensation Techniq...

引用

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

作者： Van Linh, Dinh Anh, Nguyen Tuan Phuong Thao, Hoang Thi Van Yem, Vu Hanoi University of Science and Technology Hanoi Viet Nam Academy of Cryptography Techniques Hanoi Viet Nam Electric Power University Hanoi Viet Nam

ISBN: (纸本)9798350329742

Orthogonal Frequency Division Multiplexing (OFDM) technology provides many benefits to wireless communication systems. However, the orthogonality of OFDM's subcarriers may be broken due to Carrier Frequency Offset (CFO) which causes a reduction in system efficiency. In this paper, a pilot-based CFO estimation and compensation algorithm is proposed to address the CFO issue in OFDM wireless communication systems. Pilot tones are inserted into the OFDM subcarriers on transmitters and then removed to estimate the CFO on receivers. The estimated CFO will be compensated in the time domain. The OFDM systems are simulated over the Rayleigh channel with different modulation levels and a different number of pilot tones. We find that the bit error rate (BER) of the OFDM systems applying our proposed method is dramatically improved by low modulation order and is not affected by the number of inserted pilots. As a result, our technique can save the resources for the OFDM systems. Moreover, the proposed solution outperforms the existing ones for CFO estimation and compensation. © 2023 ieee.

关键词： Orthogonal frequency division multiplexing

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Coarse Tree Algorithm-Based Detection of Unstructured cyber-Attacks in Oil and Gas Process control Networks 16

Coarse Tree Algorithm-Based Detection of Unstructured Cyber-...

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16th ieee AFRICON, AFRICON 2023

作者： Obonna, Ugochukwu Onyekachi Opara, Felix Kelechi Mbaocha, Christian Chidiebere Obichere, Jude-Kennedy Chibuzo Nwakanma, Cosmas Ifeanyi Ahakonye, Love Allen Chijioke Kim, Dong-Seong Federal University of Technology Electrical/Electronic Engineering Owerri Nigeria Federal University of Technology Mechatronics Engineering Owerri Nigeria Kumoh National Institute of Technology Ict Convergence Research Center Gumi Korea Republic of Kumoh National Institute of Technology IT-Convergence Engineering Gumi Korea Republic of

ISBN: (纸本)9798350336214

Recently, there have been malicious cyber-attacks on oil and gas installations, which have increased the rate of catastrophic damages leading to loss of containment, loss of life, and destruction of the environment. cyber-attacks on process control networks could be in different forms, and the inability to detect such unplanned attacks poses a massive problem to this critical infrastructure supplying energy to the world. This paper reviews the different attack methods in SCADA systems of oil and gas installations, proposing the Coarse Tree Machine Learning algorithm to enhance network surveillance and monitoring while protecting the Process control Network (PCN) data exchange between the sensors, controllers, processes, and the final control element. The experimental results demonstrate the significance, reliability, and applicability of the proposed Coarse Tree algorithm on two different SCADA datasets. The quality and precision of attack detection further validate the proposed model's performance in reducing false alarm rates recorded for the evaluated large SCADA dataset over an extended period. © 2023 ieee.

关键词： Machine learning

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Denoising Diffusion Model-Based Channel Estimation in IRS-Assisted ISAC systems 16

Denoising Diffusion Model-Based Channel Estimation in IRS-As...

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16th international conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Yi, Lan Miao, Jiansong Zhang, Tao Yuan, Zhenhui Kang, Jiawen Niyato, Dusit Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks Beijing China School of Software Engineering Beijing Jiaotong University Beijing China School of Engineering University of Warwick CV4 7AL United Kingdom School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798350390643

In Integrated Sensing and Communications (ISAC) systems assisted by intelligent Reflecting Surfaces (IRS), precise channel estimation is crucial for optimal system performance and maximizing the potential benefits. The estimation problem, however, becomes intricate due to complex channel coupling and specific environmental conditions. This paper addresses these challenges by formulating channel estimation as a denoising problem and proposing a novel diffusion-based framework. This approach mitigates the impact of ill-posedness, which is typically problematic for general neural network (NN)-based techniques. By modeling the joint distribution and sampling from the learned posterior distribution, we accurately recover the channel coefficients from noisy pilot-based observations. Simulation results demonstrate significant performance improvements of the proposed method compared to both least-squares and NN-based benchmarks. Moreover, thanks to the carefully designed architecture and residual prediction strategy, the number of sampling steps in the diffusion reverse process is greatly reduced, enabling real-time CSI acquisition. © 2024 ieee.

关键词： Least squares approximations

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Experimental Analysis of Plant Disease Identification Methodology Based on Improved Classification and Deep Learning Principle

Experimental Analysis of Plant Disease Identification Method...

引用

2024 international conference on Innovative Computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Shibu, S. Priyadharshini, S. Madhini, M. Radha, K. Giri, Pallavi Samhan, Ahmad A. Ali Panimalar Engineering College Department of Ece Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Robotics and Automation Tamil Nadu Chengalpattu India S.A. Engineering College Department of Ece Chennai India Dr. Mahalingam College of Engineering and Technology Department of Cse Pollachi Coimbatore India Laxminarayan Innovation Technological University Department of Civil engineering Nagpur India Zarqa University Faculty of Information Technology Deoartment of Software Engineering Zarqa Jordan Universityof Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The agriculture industry is essential for providing excellent food and significantly contributes to the growth of economies and people. Plant diseases can result in substantial reductions in food output and diminish species diversity. The prompt identification of plant diseases by precise or automated detection methods can improve food production quality and save economic losses. In recent years, deep learning has significantly enhanced the identification accuracy of image categorization and object detection systems. In this paper, a novel method termed Learning Enabled Disease Detection Logic (LDDL) is introduced for the efficient identification of plant diseases, which is cross-validated against the traditional Convolutional Neural Network (CNN) to assess the efficacy of the proposed scheme. The report delineates the specific crop illnesses addressed, the models utilized, the data sources referenced, and the overall efficacy as measured by the performance criteria applied in each study for plant disease diagnosis. The findings demonstrate that the proposed model, LDDL, achieves superior accuracy compared to prevalent illness detection methods and identifies the primary parameters influencing the efficacy of deep learning-based tools. The research offers significant insights for researchers, practitioners, and industry professionals in plant disease detection by addressing challenges and limitations, presenting a thorough understanding of the current research landscape, elucidating the advantages and drawbacks of various methods, and suggesting potential solutions to facilitate their implementation. © 2024 ieee.

关键词： Convolutional neural networks

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Predicting Smart Grid Stability with Multi-Directional LSTM

Predicting Smart Grid Stability with Multi-Directional LSTM

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2023 international conference on Power Energy, Environment and intelligent control, PEEIC 2023

作者： Vishnoi, Manish Srivastav, Anamika Thiruvenkadam, T. Vivekananda Global University Department of Civil Engineering Jaipur India Noida Institute of Engineering & Technology Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Computer Science Engineering Department Bangalore India

ISBN: (纸本)9798350357769

The expression relates to the national electric arrangement, used to deliver electricity via the electrical plants to the customers. It is made up of converters, distributors, monitoring stops and lines of communication. Large electricity generation plants that produce hundreds of thousands of watts of electricity and redistribute it across many demographic areas now make up the electrical system. Efficient management of the electricity delivered to many customer domain names including households, smart cities, companies, and organizations, is vital. To meet the changing needs for electricity, an electricity network with advanced technology is being implemented. Its CPS, which stands for concept, which integrates data centres with mechanical structures, is what the idea of smart grids is based on. The Computer Intelligence (also known as module represents the IT component in the smart energy system scenario integrated with CPS, while the energy consumption units represent the physical components. This study proposes a unique Broad Long and Short-Term Memory tackle to forecast the smart energy system reliability. The outcomes are compared with different well-liked artificial intelligence techniques, including neural networks with recurrent neurons (an RNN), gates recurrent units (the Government Relations Unit), and conventional the LSTM algorithm The research results demonstrate how the strategy works better than any other Learning methods. © 2023 ieee.

关键词： Artificial intelligence techniques Conventional LSTM cyber-Physical systems (CPS) Electricity distribution Energy system reliability and Machine learning methodologies Gated recurrent units (GRU) Multidirectional LSTM Recurrent neural networks (RNN) Smart grids

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ML and DL Classifications of Route Conditions Using Accelerometers and Gyroscope Sensors 3

ML and DL Classifications of Route Conditions Using Accelero...

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3rd ieee international conference on Artificial Intelligence, ICAI 2023

作者： Khan, Ibrahim Ahmed, Zahid School of Mechanical and Manufacturing Engineering National University of Sciences and Technology Islamabad Pakistan

ISBN: (纸本)9798350322125

The Modern Era has undergone vast transformations in terms of automation in a wide array of industrial applications. The Artificial Intelligence platform has revolutionized our daily lives with the advent of intelligent systems. The usefulness of AI in Road Mapping and Route Classification is demonstrated in our study where an intelligent Transport System (ITS) is proposed which enables monitoring and classification of road conditions by implementing Machine Learning (ML) and Deep Learning (DL) algorithms on data recorded by cellular accelerometer, gyroscope, and GPS sensors. Field Data was recorded in two different scenarios on different vehicles. The route mapping was performed by plotting latitude and longitudes on Google Earth. The labelling of different classes of road was done manually with correlation done via video camera recording. Road Terrain was classified into Bumps. Potholes, Rough and Smooth Roads. Six classical Supervised Machine Learning models (K Neighbors Classifier, Decision Tree Classifier, Random Forest Classifier, Support Vector Classifier, Gaussian Naive Bayes Model and Logistic Regression Model) were implemented. Furthermore, Ensembler classifier was used on all six classifiers. The selection of an Optimum Classification Model is done via Soft Voting Algorithm. Finally, K-Fold cross validation was performed to determine the accuracy of our trained model. © 2023 ieee.

关键词： intelligent systems

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Assistance for Healthcare Professionals using Hand Gesture Recognition

Assistance for Healthcare Professionals using Hand Gesture R...

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2023 international conference on Innovative Computing, intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Sethuraman, K. Jeffrey Delaperle, S. Bethanney Janney, J. Mohammed Thoufeeq, A. Aadhitya, S. Solomon, Ezekiel Jebaraj Sathyabama Institute of Science and Technology Department of Biomedical Engineering Chennai India University of Madras Cyber Forensics and Information Security Chennai India

ISBN: (纸本)9798350319200

Bedridden and paralyzed individuals need to be continuously taken care of since they find it difficult to convey their requirements due to loss of movement management in their brain. This results in communication disabilities. The proposed system aims to reduce the burden on healthcare providers with a computer-aided system that interprets the hand gestures shown by the patient and displays the needs of the patient on a monitor. Hand gesture recognition involves three processes namely: hand detection, finger detection, and gesture detection. Firstly, Hand gestures are captured by a webcam and are recognized by a Deep learning model called Computational Neural Networks (CNN), followed by background subtraction, image recognition, and image processing. CNN is used as it provides accurate and efficient output. The output/need is displayed by connecting the webcams to any desk for the ease of healthcare providers. © 2023 ieee.

关键词： Computational Neural Networks Deep Learning Hand gesture Hospital management Output transmission Patient monitoring

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