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Interference Mitigation in D2D Communication Underlying Cellular Networks:Towards Green Energy

computers, Materials & Continua 2021年第7期68卷 45-58页

作者： Rana Zeeshan Ahamad Abdul Rehman Javed Shakir Mehmood Mohammad Zubair Khan Abdulfattah Noorwali Muhammad Rizwan Department of Computer Science Xian Jiaotong UniversityXi’anChina Department of Cyber Security Air UniversityIslamabadPakistan Department of Information and Technology Northwest UniversityXianChina Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinahSaudi Arabia Department of Electrical Engineering Umm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science Kinnaird College for WomenLahore54000Pakistan

Device to Device(D2D)communication is emerging as a new participant promising technology in 5G cellular networks to promote green energy networks.D2D communication can improve communication delays,spectral efficiency,system capacity,data off-loading,and many other fruitful scenarios where D2D can be ***,induction of D2D communication in reuse mode with the conventional cellular network can cause severe interference issues,which can significantly degrade network *** reap all the benefits of induction of D2D communication with conventional cellular communication,it is imperative to minimize interference’s detrimental *** power control can minimize the negative effects of interference and get benefits promised by D2D *** this work,we propose two power control schemes,Power Control Scheme 1(PCS 1)and Power Control Scheme 2(PCS 2),to minimize the interference and provide performance *** results observe improvements with PCS 1 and PCS 2 as compared to without using any power control scheme in terms of data rate in both uplink and downlink communication modes of Cellular User Equipment(CUE).

关键词： Interference mitigation green IoT green networks D2D power control spectral efficiency reuse mode

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An Improved Clone Node Detection System Based on Enhanced Neural Optimization Strategy Over Wireless Sensor Network Environment

An Improved Clone Node Detection System Based on Enhanced Ne...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Sheela, D. Sagar, M. Vidya Rao, S Kondala Ranjith, S. Shahare, D.Y. Al Sherideh, A. Dept of Ece Chennai India Qis College of Engineering and Technology Department of Mba Pondur Road Vengamukkapalem Ongole Prakasam AndhraPradesh PrakasamDistrict523272 India Qis College of Engineering and Technology Department of S&h Pondur Road Vengamukkapalem Ongole Prakasam AndhraPradesh PrakasamDistrict523272 India Jeppiaar Engineering College Department of Ece Rajiv Gandhi salai Chennai119 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Wireless Sensor Networks (WSNs) are widely used in diverse applications, including environmental monitoring and military surveillance. However, their open and distributed nature makes them vulnerable to clone node attacks, where an adversary replicates legitimate nodes to compromise network integrity. This paper presents an improved clone node detection system based on enhanced neural optimization strategies, utilizing various detection protocols such as Energy-efficient Ring-based Clone Detection (ERCD), Distributed Hash Table (DHT), Randomly Directed Exploration (RDE), Random Multicast (RM), and Randomized, Efficient, and Distributed (RED) methods. The performance of these protocols was evaluated based on detection accuracy, energy consumption, communication overhead, and memory usage across different network sizes. Results showed that the RED method achieved the highest detection accuracy of 99.3% in large networks (1000 nodes), followed closely by ERCD with 96.7%. The DHT and RDE protocols demonstrated lower energy consumption, making them suitable for large-scale, resource-constrained WSNs. The RM method had the lowest detection accuracy (94.3%) and the highest communication overhead. Overall, the proposed neural optimization strategy effectively balances accuracy and resource efficiency, enhancing the robustness of WSNs against clone node attacks. Future work will focus on integrating machine learning models to further optimize detection performance and minimize resource consumption. © 2024 IEEE.

关键词： Network security

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A Deepfake Image Classifier System for real and Doctored Image Differentiation

A Deepfake Image Classifier System for real and Doctored Ima...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Juanith Mathew Thomas V. Ebenezer Rohan Paul Richard Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

The sudden proliferation of deepfake technology has raised concerns about the authenticity and integrity of digital media. In response to these concerns, this paper presents a Deepfake image classifier system designed to differentiate between real and doctored images using "MesoNet" architecture which utilizes convolutional layers and builds a suitable neural network for classification. Making efficient use of Deep Learning strategies, this system aims to detect subtle manipulations indicative of deepfake alterations. The proposed model is trained on two distinct datasets: Dataset I comprises authentic images while Dataset II consists of images subjected to various levels of manipulation, making them deepfakes. Through extensive training using these images and their labels, we’ll train the neural network to adequately predict the authenticity of an image. The model has been trained on exactly 7104 images and judging from the results, the predictions made by the model are fairly accurate. Results yield an accuracy of 88.81% and a precision of 87.93%.

关键词： Deep learning Training computers Deepfakes Computational modeling Neural networks Medical services

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Comparative analysis of different scene for face mask detection based on YOLOv4 3

Comparative analysis of different scene for face mask detect...

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3rd International Conference on Intelligent Computing and Human-computer Interaction, ICHCI 2022

作者： Cheng, Zuozheng Zhang, Chaoran Zhu, Yudong Department of Computer Science and Technology Zhengzhou University Zhengzhou450001 China Department of Information Security China University of Mining and Technology Xuzhou221116 China Department of Electronic Engineering Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9781510661301

The 2019-nCoV can be transmitted through respiratory droplets and other methods, which greatly endangers public health security. Wearing masks correctly has been proven to be one of the effective means to prevent virus infection, but limited by the complexity of practical application scenarios, the wearing of masks still relies heavily on manual supervision. Therefore, a fast and accurate face mask wearing detection method is urgently needed. In this paper, a mask detection algorithm based on improved YOLO-v4 is proposed as a solution to the problems of low accuracy, poor real-time performance, and poor robustness caused by complicated environments. In addition, a number of different training approaches, such as mosaic data augment, CIOU, label smoothing, cosine annealing, etc., are introduced. These techniques help to increase the training speed of the model as well as the accuracy of its detection. With a fast-training model, the model will be able to detect and compare the results of samples from different scenarios. The experiment will compare front and side faces, different colored masks, scenes of varying complexity and other perspectives in a systematic way. The experiment's result was able to reach 99.38 % accuracy after the model was trained using data from a variety of face masks being worn. Experiment results, both quantitative and qualitative, indicate that the method can be adapted to most scenarios and offers effective ideas for improvement. © 2023 SPIE.

关键词： Deep learning

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Diagnosis of Plant Leaf Disease using Vision Transformer

Diagnosis of Plant Leaf Disease using Vision Transformer

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International Conference on Communications and Signal Processing

作者： Ashik Sundaraj Deva Priya Isravel Julia Punitha Malar Dhas Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

In precision agriculture, it is crucial to have a reliable system for identifying diseases and suggesting measures to maintain crop health and enhance yield performance. Addressing the persistent challenge of accurate and timely identification of plant illnesses, this study introduces a new understanding of the approach utilizing the Vision Transformer (ViT) architecture. Leveraging a diverse dataset encompassing both diseased and healthy plant images, the ViT model demonstrates efficacy by achieving an precision of 94% in recognizing and categorizing diseases based on plant leaves. The success of this transformer-based methodology underscores its potential to significantly enhance the field of agricultural diagnostics, offering a promising solution to improve crop management practices and mitigate potential losses in yield.

关键词： Training Plant diseases Plants (biology) Crops Signal processing algorithms Signal processing Transformers

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Evolutionary Algorithms for Deep Learning in Secure Network Environments

Evolutionary Algorithms for Deep Learning in Secure Network ...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Seva, Banothu Jain, Ratnesh Khare, Neha Choudhury, Hussain Ahmed Chakravarthi, M Ayyappa Tomer, Nitin MLR Institute of Technology Dept. of CSE Telangana Hyderabad India GGITS Dept of Computer Science & Design M.P Jabalpur India IES Institute of Technology and Management IES University Department of Computer Science & Engineering M.P Bhopal462044 India Barak Valley Engineering College Department of CSE Assam Karimganj India KKR & KSR Institute of Technology and Sciences Dept. of CSE - Data Science AP Guntur India GGITS Cyber Security & Blockchain Dept of IOT M.P. Jabalpur India

ISBN: (纸本)9798331539948

Hackers are getting better at breaking into protected networks, making network security an arms race. Today, combining EAs with DL to combat these risks is cuttingedge. This research illustrates how EAs and DL may enhance network security in a shifting threat environment. This blend outperforms traditional techniques in many critical security measures, according to our study. Network safety in deep learning involves improving model architecture, hyperparameters, and adaptive strategies. These variables affect how successfully a model detects unexpected activity while limiting false positives. We propose rethinking how these settings are fine-tuned and modified to create a larger digital asset security strategy. This technique integrates EAs with DL. Evolutionary algorithms, based on natural selection and genetic variety, solve large problem areas well. With DL, these approaches speed up the construction of cutting-edge safety tools. These approaches allow us to find model designs, modify hyperparameters, and update trained models, which we like. Our experiment illuminated the topic. The recommended method routinely outperforms established methods in terms of discovery rate. In network security, the detection rate indicates how successfully a system can identify and react to malicious conduct. Our approach can protect digital products due to their greater detection rate. security specialists celebrate any modest finding in their never-ending battle against nefarious actors. Network security professionals must monitor false positives to avoid too many false alarms. New dangers may be stopped before they do harm. Evolutionary algorithms and deep learning in secure networks give new perspectives. Empirical evidence reveals that our method is more efficient, versatile, and finds things better. Because digital threats change, our strategy strengthens our defenses to protect critical networks and digital assets. © 2024 IEEE.

关键词： Adversarial machine learning

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Using Cycling 3D Chaotic Map and DNA Sequences for Introducing a Novel Algorithm for Color Image Encryption *

Using Cycling 3D Chaotic Map and DNA Sequences for Introduci...

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2022 International Conference on cyber Resilience, ICCR 2022

作者： Sahlabadi, Mahdi Saberikamarposhti, Morteza Muniyandi, Ravie Chandren Shukur, Zarina University Kebangsaan Malaysia Research Centre for Cyber Security Faculty of Information Science and Technology Selangor Malaysia Islamic Azad University South Tehran Branch Department of Computer Engineering Tehran Iran Faculty of Information Science and Technology Selangor Malaysia

ISBN: (纸本)9781665461221

Today, social communication through the Internet has become more popular and has become a crucial part of our daily life. Naturally, sending and receiving various data through the Internet has also grown a lot. Keeping important data secure in transit has become a challenge for individuals and even organizations. Therefore, the trinity of confidentiality, integrity, and availability will be essential, and encryption will definitely be one of the best solutions to this problem. Of course, for image data, it will not be possible to use conventional encryption methods for various reasons, such as the redundancy of image data, the strong correlation of adj acent pixels, and the large volume of image data. Therefore, special methods were developed for image encryption. Among the prevalent methods for image encryption is the use of DNA sequences as well as chaos signals. In this paper, a cycling 3D chaotic map and DNA sequences are used to present a new method for color image encryption. Several experimental analyses were performed on the proposed method, and the results proved that the presented method is secure and efficient. © 2022 IEEE.

关键词： Cryptography

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Power amplifier circuit defect detection based on improved Patch SVDD

Power amplifier circuit defect detection based on improved P...

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作者： Xie, Zhonghui Kong, Wa Zhou, Xinyu Zhang, Wence Zhong, Yujie Xia, Jing School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Jiangsu University Zhenjiang China Department of Electronic Information Engineering Hong Kong Polytechnic University Hong Kong

In the manufacturing process of power amplifier (PA), various defects on the circuit surface will seriously affect the circuit performance and its operation. To solve the above problems, this article proposes a circuit defect detection method based on improved patch-based support vector data description algorithm (Patch SVDD), which uses contrastive learning to enhance the feature extraction ability of neural network. An image calibration algorithm is used for calibration preprocessing to enhance the robustness of the model. The Euclidean distance between a query image patch and its nearest normal patch is defined to be an anomaly score. For verification, three defects in the PA circuit, including components missing, solder contamination and component orientation dislocation, were detected. The experimental results show that, compared with conventional method, the area under receiver operating characteristic (AUROC) of the improved anomaly detection model increased from 90.1% to 95.1%, which improves the detection accuracy effectively. © 2023 John Wiley & Sons Ltd.

关键词： Anomaly detection

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Look One and More: Distilling Hybrid Order Relational Knowledge for Cross-Resolution Image Recognition

arXiv

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arXiv 2024年

作者： Ge, Shiming Zhang, Kangkai Liu, Haolin Hua, Yingying Zhao, Shengwei Jin, Xin Wen, Hao Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China Department of Cyber Security Beijing Electronic Science and Technology Institute China CloudWalk Technology Co. Ltd China

In spite of great success in many image recognition tasks achieved by recent deep models, directly applying them to recognize low-resolution images may suffer from low accuracy due to the missing of informative details during resolution degradation. However, these images are still recognizable for subjects who are familiar with the corresponding high-resolution ones. Inspired by that, we propose a teacher-student learning approach to facilitate low-resolution image recognition via hybrid order relational knowledge distillation. The approach refers to three streams: the teacher stream is pretrained to recognize high-resolution images in high accuracy, the student stream is learned to identify low-resolution images by mimicking the teacher’s behaviors, and the extra assistant stream is introduced as bridge to help knowledge transfer across the teacher to the student. To extract sufficient knowledge for reducing the loss in accuracy, the learning of student is supervised with multiple losses, which preserves the similarities in various order relational structures. In this way, the capability of recovering missing details of familiar low-resolution images can be effectively enhanced, leading to a better knowledge transfer. Extensive experiments on metric learning, low-resolution image classification and low-resolution face recognition tasks show the effectiveness of our approach, while taking reduced models. © 2024, CC BY-NC-ND.

关键词： Students

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A Symmetric TDMA Mechanism to Optimize the Performance of the Body Sensor Network for Sports Application 5

A Symmetric TDMA Mechanism to Optimize the Performance of th...

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5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Mustafa, Mohammed M. Khalifa, Ahmed A. Cengiz, Korhan Sri Krishna College of Engineering & Technology Department of Information Technology Coimbatore India College of Information Technology University of Fujairah Department of Networking and Security Fujairah United Arab Emirates National Telecommunication Institute Switching and Traffic Department Cairo Egypt Istinye University Department of Computer Engineering Zeytinburnu Istanbul Turkey

ISBN: (纸本)9798350337525

The body sensor network plays a vital role in the analysis of the gait analysis of human, sensing the various events happening in the human body. The communication that takes place between the sensed device and the processing system is very important in which the nodes worn on the human body communicate with the sink node placed in the center of the human body. A wireless communication mechanism TDMA was used and the results gave around 60 percent reliability among the nodes. The dynamic TDMA gave a reliability of 90 percent and retransmission mechanism of 95 percent. Our research work focused on to improve the reliability between the nodes and to develop an application for the users to retrieve the data from the sink node. A Symmetric TDMA algorithm was used in which the reliability was increased up to 2 percent resulted in 97 percent. Also, a Bayesian model was developed to identify the probability of nodes initiating to transmit at the same time. The model proved that the chances of nodes transmitting at the same time are 8 percent when comparing to all other techniques. The reliability of the network was also increased. Further, the work will be developed to bring the security concepts into the transmission of data packets, so that the loss can be minimized. © 2023 IEEE.

关键词： Reliability

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