K. Naresh Kumar Thapa

Sathyabama Institute of Science and Technology

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N. Duraipandian

Saveetha University

Deepika Yadav

RIKEN Center for Advanced Photonics

Manisha Guduri

University of Louisiana at Lafayette

S. Kalaivani

Government Medical College

G. Vishnupriya

Arunai Engineering College

Omar S. Saleh

Ministry of Higher Education and Scientific Research

Lakshmi Kantham

Central Council for Research in Siddha

Samuthira Pandi

Trichy SRM Medical College Hospital and Research Centre

S Sivapriya

A. Sivakumar

Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

Cooperative Institutions

Saveetha University

R.M.D. Engineering College

Anna University, Chennai

Velammal Medical College Hospital and Research Institute

Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

Dr. M.G.R. Educational and Research Institute

GS Engineering (United States)

SRM Institute of Science and Technology

Vellore Institute of Technology University

Sathyabama Institute of Science and Technology

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A novel authenticated and storage constrained qos routing protocol using onion routing in manets

International Journal of Current Advanced Research (2017)

S Sivapriya K. Naresh Kumar Thapa

Zone Routing Protocol

10.24327/ijcar.2017.3558.0313

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A novel enhanced security architecture for sixth generation (6G) cellular networks using authentication and acknowledgement (AA) approach

Results in Engineering (2023)

Samuthira Pandi Anitha Juliette Albert K. Naresh Kumar Thapa R. Krishnaprasanna

The sixth generation (6G) of cellular transmission is extremely adaptable. 6G will equip everyone with pervasive wireless connectivity and meet the needs of a fully connected world. It is anticipated that revolutionary ideas will increase in support for a fast-expanding variety of smart devices and applications. The sophisticated characteristics of 5G portable cellular network infrastructures produce novel threats and requirements. Comparing 6G cellular network technologies to 5G cellular networks, this paper discusses various security and privacy issues in 6G cellular networks based numerous security services. Proposes AA approach to evaluate the effectiveness of the methodology. Propose a new 6G wireless security architecture based on the analysis of secret key authentication and flexible position based identification, which serves as the foundation for an examination of identity management and flexible authentication. Demonstrates the benefits of the proposed architecture, analyzes BER against SINR and Measure Throughput Against various SINR values. Finally, Limitations and future recommendation for 6G cellular network security is presented.

Acknowledgement

10.1016/j.rineng.2023.101669

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A Comprehensive Analysis for Implementing IoT on LTE Systems

2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC) (2022)

K. Naresh Kumar Thapa A. Hema Malini A Kalaimani Sathiyandrakumar Srinivasan

Rapid increase in usage of devices being used by Internet of Things (IoT), it has become compulsory to connect all devices to an architecture. This kind of interconnection allows services on either end to be seamlessly integrated From the approximate count of 10 billion internet users in 2015, it has now risen to an extent of 35 billion user devices by 2021.There is a progressive rise in IoT signaling overhead as the number of IoT devices grows. Increased LTE signaling overhead results from this. The present protocol cannot handle the increasing traffic produced by IoT devices due to the large number of exchanges between transmitter and receiver.. In order to handle the signaling traffic in future LTE systems, an effective protocol stack is developed in this proposed paper. The proposed system is developed mainly to concentrate on real time services and all these techniques are evaluated to operate better than the conventional systems. This proposed protocol provides better Quality of Service (QoS) thereby enhancing better video and voice streaming and also improves the overall throughput of the system.

Protocol stack

10.1109/icaaic53929.2022.9793086

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Block Chain Based Identity Authentication System for a Wireless Sensor Networks

2022 International Conference on Computer Communication and Informatics (ICCCI) (2023)

K. Naresh Kumar Thapa G. Vishnupriya Manisha Guduri Omar S. Saleh T. Lakshmibai

In the present IoT context, typical focused security validation approaches are unable to offer security verification due to the restricted performance and flexibility of the devices. These issues are addressed by IoT-Chain, an Internet of Things (IoT) security validation system built on the Hyperledger Fabric block chain architecture, which offers superior security confirmation. These three forms of chain codes are all included in the architecture. It is the entry code that is responsible for running the test to determine whether or not a client is healthy. An asset information URL may be retrieved via a device code, whereas a method code indicates the access control mechanism for a particular client. IoT-Chain offers dynamic security validation of the Internet of Things by integrating access control with block chain technologies. IoT-Chain can sustain high throughput and really achieve consensus in a distributed system, according to preliminary studies.

10.1109/iccci56745.2023.10128299

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Malicious Traffic classification Using Long Short-Term Memory (LSTM) Model

Wireless Personal Communications (2021)

K. Naresh Kumar Thapa N. Duraipandian

Traffic classification

10.1007/s11277-021-08359-6

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Plant Disease Identifer Using K-Means and GLSM in Convolution Neural Network

European Journal of Molecular & Clinical Medicine (2020)

S.P. Vijaya Vardan Reddy T. Suresh K. Naresh Kumar Thapa V. Ramkumar S. Mahabhoob Basha

Convolution (computer science)

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Digital signature using stationary wavelet transform based watermarking for robots

K. Naresh Kumar Thapa Ponnraja Kumari Lakshmi Kantham

Researchers in the field of robotics have targeted mostly on the design automation and improvements of Robot's intelligence. The present paper emphasises on providing individuality and unique digital signature for robots using Watermarking In this technique we have used RGB uncompressed AVI video sequence in stationary wavelet transform (SWT) domain using singular value decomposition (SVD). The data acquired by the robots are thus watermarked by digital signature which holds rightful ownership of the data being sent. The resulting video can exhibit highly desired features like good quality, robustness and also secures the data from being tampered by any external sources.

Robustness

RGB color model

10.1109/iccic.2014.7238560

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Malicious Traffic classification using Long Short-Term Memory (LSTM) model

Research Square (Research Square) (2021)

K. Naresh Kumar Thapa N. Duraipandian

Abstract Malicious traffic classification is the initial and primary step for any network-based security systems. This traffic classification systems include behavior-based anomaly detection system and Intrusion Detection System. Existing methods always relies on the conventional techniques and process the data in the fixed sequence, which may leads to performance issues. Furthermore, conventional techniques require proper annotation to process the volumetric data. Relying on the data annotation for efficient traffic classification may leads to network loops and bandwidth issues within the network. To address the above-mentioned issues, this paper presents a novel solution based on artificial intelligence perspective. The key idea of this paper is to propose a novel malicious classification system using Long Short-Term Memory (LSTM) model. To validate the efficiency of the proposed model, an experimental setup along with experimental validation is carried out. From the experimental results, it is proven that the proposed model is better in terms of accuracy, throughput when compared to the state-of-the-art models. Further, the accuracy of the proposed model outperforms the existing state of the art models with increase in 5% and overall 99.5% in accuracy.

10.21203/rs.3.rs-159180/v1

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Differential Amplifier Based Speed Monitoring Circuit for Airport and Production Industry

2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) (2021)

K. Naresh Kumar Thapa S. Kalaivani S. Vanaja J. Joselin Jeya Sheela Deepika Yadav

The most common conveyer belt systems are designed to transport large volumes of goods at lower cost. In plants such as the medical industry, food industry, printed circuit board (PCB) industry, and language moving services at airport, boam detecting entrance system, both inflated and motorized conveyors are commonly used. The mechanical conveyor is a broad term that refers to a wide range of devices. The term "mechanical conveyor" refers to a range of machines that are commonly used to transport goods and materials through the industrialized process in an automatic manner, ranging from belts to trays to buckets. The design and implementation of a differential amplifier and functional amplifier-based mechanical conveyor speed control system is system design is developed using ORCAD simulation software. A buzzer system can monitor and signal the conveyor's low and high speed movement.

Buzzer

Conveyor belt

SIGNAL (programming language)

10.1109/i-smac52330.2021.9640939

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An Efficient Approach to Predict COVID-19 Infected Patient Applying Deep learning Algorithm on Chest X-ray Images with Analyzing the Patient Symptoms (Preprint)

Mohammad Helal Uddin Mohammad Nahid Hossain K. Naresh Kumar Thapa S.-H Yang

BACKGROUND COVID-19 is a life-threatening infectious disease that has become a pandemic for the time being. The virus grows within the lower respiratory tract where early-stage symptoms(like- cough, fever, sore throat, etc.) develop and then it causes lung infection(pneumonia) OBJECTIVE This paper proposed a new methodology of artificial testing whether a patient has been infected by COVID-19 or not METHODS We have presented a prediction model based on, Convolutional Neural Networks(CNN) and our own developed mathematical equation based algorithm named SymptomNet. The CNN algorithm classifies the lung infection(pneumonia) from frontal chest X-ray images, while the symptoms analyzing algorithm(SymptomNet) predicts the possibility of COVID-19 infection from developed symptoms in a patient RESULTS The model has the accuracy of 96% while predicting COVID-19 patients. In this Model, the CNN classifier has the accuracy of around 96% and the SymptomNet algorithm has the accuracy of 97%. CONCLUSIONS This research work obtained a promising accuracy while predicting COVID-19 infected patients. The proposed model can be ubiquitously used at a low cost with high accuracy.

Preprint

10.2196/preprints.21420

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