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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Khan, Mohammad Ahmar Verma, Pushpendra Kumar Ghaldiyal, Shivangi Balaji, N.V. Francis, G. Arun Patil, Swati Bula Department of Management Information System College of Commerce & Business Administration Dhofar University Country Sultanate of Oman Oman School of Computer Science and Applications IIMT University Meerut India Department of Computer Applications Invertis University Uttar Pradesh Bareilly India Department of Computer Science Karpagam Academy of Higher Education Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Karpagam College of Engineering Tamil Nadu Coimbatore India IT Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350370249

Research on fake news has gained significant traction in a number of fields, We describe how the issue is tackled from a natural language processing standpoint in this research with the aim of developing identify false material in news. Collecting high-quality data, such as examples of false and actual news stories on a diverse range of subjects, is the key obstacle in this field of study. We examine the available before introducing the site as our lab's gift to the . We provide the whole text of the news that have already been applied based on human evaluation of the articles' authenticity. In order to clarify the gaps and causes of imbalance in the datasets that are now accessible and to direct ongoing attempts, we also conduct an appearance-based experiment. We urge the public to gather more information and make it accessible for study. ©2024 IEEE.

关键词： Data collection

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An Extensive Examination of Different Phases Available in Enhancing the Power of WSN 4

An Extensive Examination of Different Phases Available in En...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Gill, Rupali Kumar, Rakesh Pant, Shivani Deepika, A. Al-Fatlawy, Ramy Riad Angadi, Sanjeevkumar Chitkara University Centre of Research Impact and Outreach Punjab Rajpura140401 India Gla University Department of Computer Engineering & Applications Mathura India Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science Engineering Dehradun248007 India India The Islamic University Najaf Iraq Computer Science and Engineering Nutan College of Engineering and Research Pune Maharashtra Talegaon Dabhade India

ISBN: (纸本)9798350360165

In this whole test, we delve into the evolution and transformative ability of Wireless Sensor Networks (WSNs), emphasizing the pivotal position of graphical statistics in elucidating the technological improvements and stressful conditions interior this region. By harnessing the energy of the IEEE 802.15.4 general, this research provides an in-intensity analysis of WSNs' operational efficiencies, energy consumption styles, and network resilience through meticulously designed graphs and information visualizations. These graphical representations now not only offer a easy and concise overview of WSN universal performance metrics but additionally spotlight the giant enhancements completed thru the implementation of a singular WSN framework. The examiner's findings underscore the vital importance of empirical statistics visualization in advancing our knowledge of WSN technologies, offering valuable insights into the optimization strategies that may result in extra power-inexperienced, reliable, and scalable sensor networks. This research contributes to the continued communicate in the subject, paving the manner for future enhancements and programs of WSNs across several sectors. © 2024 IEEE.

关键词： Energy utilization

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Analysing of diverse Stress pattern in ECG using Machine Learning Techniques 2

Analysing of diverse Stress pattern in ECG using Machine Lea...

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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Anbarasi, S. Dhanalakshmi, B. Department of Computer Applications BS Abdur Rahman Crescent Institute of Science and Technology GST Road vandalur Tamil Nadu Chennai India Department of Computer Science and Engineering BS Abdur Rahman Crescent Institute of Science and Technology GST Road vandalur Tamil Nadu Chennai India

ISBN: (数字)9781837242535

ISBN: (纸本)9781837242672

ECG signals are commonly used to diagnose various cardiac conditions, and the classification of these signals is an important task in Stress identification. With the advancement of ML techniques, several ML models have been developed to classify ECG signals with high accuracy. This paper aims to review recent studies that use ML models for ECG classification, equivalence to the working nature of different prototype, and discuss the challenges and limitations of these models. The study found that deep learning models, such as CNNs and RNNs, outperformed traditional ML models in terms of closeness and validity. The results also showed that the combination of multiple models could improve the classification performance. However, the study also highlighted the need for large annotated ECG datasets and the requirement of domain expertise for feature engineering. Overall, this review provides a complete analysis of the current state of ECG classification using ML models and identifies areas for future research and improvement. Even though the accuracy of ML models in ECG classification is not always perfect, they can still provide valuable information and support to healthcare professionals. In some cases, even a lower accuracy can still be useful, especially in the context of large-scale screening and monitoring programs, where the goal is to identify individuals who require further evaluation. In these cases, the high computational speed and scalability of ML models make them an attractive option, as they can quickly process large amounts of data and provide preliminary results that can be followed up with more comprehensive and accurate tests. Additionally, the results from ML models can also be used as a starting point for further analysis by healthcare professionals, who can use their domain knowledge to interpret the results and make informed decisions. Therefore, despite their limitations, ML models can still play an vital role in the field of Electrocardiogram cla

关键词： Convolutional neural networks

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An Optimized DDoS Attack Detection Using Deep Convolutional Generative Adversarial Networks 5

An Optimized DDoS Attack Detection Using Deep Convolutional ...

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5th International Conference on Inventive Research in Computing applications, ICIRCA 2023

作者： Akana, Chandra Mouli Venkata Srinivas Kumar, Arvind Tiwari, Mohit Yunus, Aslanov Zabit Vijayakumar, E. Singh, Mukesh Bonam Venkata Chalamayya Engineering College Odalarevu India School of Computer Applications Manav Rachna International Institute of Research and Studies India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India Azerbaijan State University of Economics - Unec Baku Azerbaijan KIT-Kalaignar Karunanidhi Institute of Technology Department of Computer Applications Tamilnadu Coimbatore India Graphic Era University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350321425

Attacks known as Distributed Denial-of-Service (DDoS) are rising as a result of recent, dramatic increase in demand for Internet access. When the amount and characteristics of network traffic, which may include harmful DDoS contents, expand dramatically, traditional basic algorithms using machine learning for classifying DDoS attacks frequently fail because it cannot automatically extract high-value characteristics. In order to effectively identify and classify DDoS attacks, a hybrid technique called DFNN-SAE-DCGAN that combines three deep learning-based models is suggested. The Deep Feed-Forward Neural Network (DFNN) and Stacked Auto encoder offers an efficient method for extracting features that identifies the most pertinent feature sets without the assistance of a person. To avoid the operational overhead and presumption associated with processing massive set of features with distortion and redundant characteristic values, the Deep Convolutional Generative Adversarial Networks (DCGAN) component of the proposed model classifies the attacks into various DDoS attack types using the restricted and minimized characteristic sets generated by the DFNN-SAE as inputs. The experimental results show a very high and resilient accuracy rate and an F1-score of 98.5%, which is higher than the performance of many similar approaches. These results were acquired by thorough and extensive trials on various performance aspects on the CICDDoS2019 dataset. This demonstrates that the suggested methodology may be utilized to defend against the increasing amount of DDoS attacks. © 2023 IEEE.

关键词： Denial-of-service attack

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Security Challenges in Multi-Cloud Environments: Solutions and Best Practices 7

Security Challenges in Multi-Cloud Environments: Solutions a...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Madanan, Mukesh Patel, Pratik Agrawal, Prateek Mudholkar, Pankaj Mudholkar, Megha Jaganraja, V. Dhofar University Department of Computer Science Oman National Forensic Sciences University India India Marwadi University Faculty of Computer Applications Gujarat Rajkot360003 India Marwadi University Department of Computer Engineering Gujarat Rajkot360003 India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai600062 India

ISBN: (纸本)9798350350067

The aim of this research paper is to investigate how machine learning can be incorporated to improve security around multi-clouds, with specific reference to the challenges. Based on the datasets obtained from Kaggle, this study uses both unsupervised learning, more specifically the Isolation Forest technique and supervised learning, such as regression-based forecast for identifying possible security threats. The evaluation shows that through the use of ML techniques risk that originates from multi-cloud systems may be well managed while, at the same time, the utilization of cloud resources could be enhanced to produce the best results. The results make it possible to conclude that machine learning still holds a huge potential in the sphere of cloud security and provides a range of recommendations and recommendations for organizations that have to manage their multi-cloud environment. Possible future studies are devoted to the investigation of deep learning models and the incorporation of blockchain technology for more security and effectiveness of the system. © 2024 IEEE.

关键词： Cloud security

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Dynamic forest of random subsets-based one-time signature-based capability enhancing security architecture for named data networking

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International Journal of Information Technology (Singapore) 2023年第2期15卷 773-788页

作者： Babu, Varghese Jensy Jose, M. Victor Department of CSE IES College of Engineering Chittilappilly Kerala Thrissur 680551 India Department of Computer Science and Engineering and Computer Applications Noorul Islam Centre For Higher Education Kumaracoil Thuckalay Kanyakumari District Tamil Nadu 629 180 India

Network caching in named data networks (NDN) is essential for improving the potentialities of the conventional IP networking. The concept of network caching is necessary for achieving optimal bandwidth utilization and location independent data access during multipath data dissemination. However, network caching in NDN makes it highly vulnerable to security breaches such as access content packets violation, flooding or malicious injection of packets and content cache poisoning. In this paper, a dynamic forest of random subsets-based one-time signature-based capability enhancing security architecture (DFORS-CSA) is proposed for attaining distributed data authentication. This DFORS-CSA security architecture leverages the potential in exploring the access privileges of the packets disseminated in the network. It includes the capability through which the routes can perform authentication of packets forwarded in NDN. It supports a significant verification strategy through which the routers can ensure the packet timeliness for resolving the problems that get introduced through unsolicited packets exchanged during flooding-based denial of service attacks. The simulation experiments of the proposed DFORS-CSA is conducted using the open source CCNs platform and Planetlab simulator. The results of the proposed DFORS-CSA confirmed its predominance in minimizing overall delay and time incurred in the bit vector generation by 16.74 and 15.63%, excellent to the baseline approaches. The results of the proposed DFORS-CSA also conformed a mean improvement in the precision rate by 10.21%, true positive rate by 8.94% and F-measure by 7.62% with decreased false positive rate of 8.56%, during the process of detecting content cache poisoning attack. © 2021, Bharati Vidyapeeth's Institute of computer applications and Management.

关键词： Content cache poisoning attack Dynamic forest of random subsets Named data networks (NDN) Network caching One-time signature

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure 3

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. Ramya, R. Kanimozhi, R. Lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

Data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 IEEE.

关键词： Data integrity

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Design and Analysis of High-Speed Communication Systems for 5G and Beyond 9

Design and Analysis of High-Speed Communication Systems for ...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Jegadeesan, R. Srinivas, Dava Deivakani, M. Joshi, Sunita Dinesh, P.M. Kumar, M. Jogendra Jyothishmathi Institute of Technology And Science Department of Computer Science And Engineering Telangana Karimnagar505481 India Psna College of Engineering And Technology Department of Electronics And Communication Engineering Tamil Nadu Dindigul624622 India Manav Rachna International Institute of Research And Studies Department of Faculty of Computer Applications Haryana Faridabad121004 India Sona College of Technology Department of Electronics And Communication Engineering Tamil Nadu Salem636005 India KoneruLakshmaiah Education Foundation Department of Computer Science And Engineering Andhra Pradesh Vaddeswaram522302 India

ISBN: (纸本)9798350365092

Fifth Era Innovation (5G) is the late portable network in wireless communications. This article presents an assessment of the advancements in portable communications. The different issues that arise at each stage are reported in our guide to state-of-the-art wearable networks. The fifth era (5G) of portable networks offers everybody admittance to high-speed web at whatever point they need, any place they are. Since to its inventive qualities, such connecting individuals and controlling contraptions, machines, and items, 5G is somewhat unique. Various levels of execution and capacities presented by the 5G versatile innovation will make new client encounters and connection new organizations. It is pivotal to comprehend where the business can utilize5G's benefits. This work examines and models a microstrip fix antenna working at 29 GHz for impending 5G communication innovation. The result of this reproduction is better than past work that was finished. It can then be used as a significant source of strength for 5G wireless technologies. The ongoing logic diary demonstrates that the proposed antenna performs better than those currently in use. This antenna may therefore be adequate for his 5G wireless connection architecture. © 2024 IEEE.

关键词： 5G mobile communication systems

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A Genetic-Muted Leader Scheme with CNN-Based IDS for Industrial Internet of Things Networks 1

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9th International Conference on Cyber Security, Privacy in Communication Networks, ICCS 2023

作者： Reddy, Boyella Mala Konda Abdul Azeez Khan, A. Sathick, K. Javubar Raj, L. Arun Department of Computer Science and Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Applications B. S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (数字)9789819739738

ISBN: (纸本)9789819739721

The industrial industry has adopted web and cloud-based technologies widely, which has accelerated the IoT ecosystem’s expansion. Industrial Internet of Things (IIoT) has established a large network due to the sheer volume of information and linked equipment. IIoT networks are made to be vulnerable in terms of cybersecurity. So as to ensure the security of the IIoT networks, intrusion detection systems (IDS) must be developed. This study suggests an IDS-based on a Genetic-Muted Leader Scheme (GMLS)-CNN for usage in IIoT networks. The data collection and preprocessing steps improve the detection accuracy. The next step is to classify data using convolutional neural networks (CNNs), with GMLS used to fine-tune the parameters for the most accurate results. The two datasets such as UNSW-NB15 and X-IIoTID and named as TD were used to distinguish normal and problematic data, which allowed the study to evaluate several categorization techniques. Among the other schemes, the GMLS-CNN model had the maximum accuracy rate for IDSis 98.7% (X-IIoTID) and 95.21% (UNSW-NB15). Additionally, the model’s success in correctly detecting attacks based on the various types of attacks reported in the datasets was assessed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network intrusion

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Recognition of Human Activity using Signal Processing & Deep Neural Networks 8

Recognition of Human Activity using Signal Processing & Deep...

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Moharana, Bhimasen Biswal, Bhramara Bar Samant, Tapaswini Behera, Trupti Mayee Mishra, Subhashree Banerjee, Shobhan Lovely Professional University Department of Computer Science & Engineering Punjab India College of Engineering P.G. Department of Computer Science & Applications Bhubaneswar India Kalinga Institute of Industrial Technology School of Electronics Engineering Bhubaneswar India Birla Institute of Technology and Science - Pilani Department of Engineering Technology Rajasthan India

ISBN: (纸本)9798350334012

The task of tracking the activities being performed by humans is of immense importance as it finds application in healthcare, marketing, surveillance, etc. To keep track of this, they need to be recognized and classified into various activities. The data can be collected through smart devices such as smartwatches or smartphones which internally use various sensors to measure the physical movements of the person using it. We have used Human Activity Recognition Dataset from the UCI ML Repository, which consists of the data being collected from the smartphones of 30 volunteers. After choosing the appropriate features, we trained an Artificial Neural Network and a Long Short Term Memory Neural Network to classify the activities into various categories and performed a comparative analysis of these neural networks among themselves & also with respect to the previously leveraged techniques & algorithms on the dataset. © 2023 IEEE.

关键词： Long short-term memory

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