Pub Date : 2023-04-05DOI: 10.1109/ICNWC57852.2023.10127271
D. Ramya, C. Lakshmi
Segmenting tumor in the brain is a challenging process undertaken by the surgeon to assess and locate the tumor location in the MRI images. To overcome this constraint, an improved U-Net architecture for use in the BraTS20 and BraTS21 challenge’s brain tumor segmentation problem is proposed. The accuracy has been improved by modifying the loss function. Comprehensive ablation research to investigate Deep Supervision loss, Cross-Entropy, Decoder Attention, and Residual Connections to determine the best model architecture and learning schedule is performed. Multiple convolutional channels have been experimented with, and post-processing techniques to find the ideal spot for the U-Net encoder’s depth have also been undertaken. The proposed technique outperforms every U-Net variant and produces superior outcomes while incurring a minimal loss.
{"title":"Brain Tumor Segmentation using MRI Images by Optimized U-Net","authors":"D. Ramya, C. Lakshmi","doi":"10.1109/ICNWC57852.2023.10127271","DOIUrl":"https://doi.org/10.1109/ICNWC57852.2023.10127271","url":null,"abstract":"Segmenting tumor in the brain is a challenging process undertaken by the surgeon to assess and locate the tumor location in the MRI images. To overcome this constraint, an improved U-Net architecture for use in the BraTS20 and BraTS21 challenge’s brain tumor segmentation problem is proposed. The accuracy has been improved by modifying the loss function. Comprehensive ablation research to investigate Deep Supervision loss, Cross-Entropy, Decoder Attention, and Residual Connections to determine the best model architecture and learning schedule is performed. Multiple convolutional channels have been experimented with, and post-processing techniques to find the ideal spot for the U-Net encoder’s depth have also been undertaken. The proposed technique outperforms every U-Net variant and produces superior outcomes while incurring a minimal loss.","PeriodicalId":197525,"journal":{"name":"2023 International Conference on Networking and Communications (ICNWC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130255453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-05DOI: 10.1109/ICNWC57852.2023.10127293
Sunil Varma, Nitika Kapoor
In this paper, we propose a novel approach to generate image captions using deep learning techniques. Our model employs a pre-trained visual language model and matches picture label information to generate familiar picture captions that can depict novel articles. We also use a range of pre-training techniques for learning cross-modal representations on picture text sets, which contribute to the model’s ability to predict picture text semantic arrangements. We demonstrate that our model outperforms state-of-the-art models on the Flicker 8K dataset. We also employ a combination of long short-term memory (LSTM) and Convolutional Neural Networks (CNNs) layers to extract image features, which help the model understand and highlight the relationship between image features and caption semantics. Our results suggest that our approach can provide a more effective and resource-efficient solution for generating image captions. Overall, this paper presents a comprehensive investigation into the use of deep learning techniques for image caption generation.
{"title":"Image Caption: Explaining Pictures by Text using Deep Learning","authors":"Sunil Varma, Nitika Kapoor","doi":"10.1109/ICNWC57852.2023.10127293","DOIUrl":"https://doi.org/10.1109/ICNWC57852.2023.10127293","url":null,"abstract":"In this paper, we propose a novel approach to generate image captions using deep learning techniques. Our model employs a pre-trained visual language model and matches picture label information to generate familiar picture captions that can depict novel articles. We also use a range of pre-training techniques for learning cross-modal representations on picture text sets, which contribute to the model’s ability to predict picture text semantic arrangements. We demonstrate that our model outperforms state-of-the-art models on the Flicker 8K dataset. We also employ a combination of long short-term memory (LSTM) and Convolutional Neural Networks (CNNs) layers to extract image features, which help the model understand and highlight the relationship between image features and caption semantics. Our results suggest that our approach can provide a more effective and resource-efficient solution for generating image captions. Overall, this paper presents a comprehensive investigation into the use of deep learning techniques for image caption generation.","PeriodicalId":197525,"journal":{"name":"2023 International Conference on Networking and Communications (ICNWC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134428494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-05DOI: 10.1109/ICNWC57852.2023.10127299
S. Sivasundarapandian, R. Sakthiprabha, V. Vedanarayanan, A. Aranganathan, T. Gomathi, E. Rajinikanth
Cognitive radio plays a vital role and can be taken as a feasible alternative in future for mobile communication networks. Spectrum allocation will become a serious issue in cognitive radio networks if it is not addressed. In this research we are proposing an enhanced structural mobility model for cognitive radio network. We express a distinctive spectrum reallocation algorithm based on mobility model that incorporates secondary user’s (SU) mobility. In part, simulation findings confirm that spectrum reallocation algorithm has a good system communication overhead performance.
{"title":"Spectrum Reallocation Algorithm in Cognitive radio Networks Based on Secondary User Mobility Model","authors":"S. Sivasundarapandian, R. Sakthiprabha, V. Vedanarayanan, A. Aranganathan, T. Gomathi, E. Rajinikanth","doi":"10.1109/ICNWC57852.2023.10127299","DOIUrl":"https://doi.org/10.1109/ICNWC57852.2023.10127299","url":null,"abstract":"Cognitive radio plays a vital role and can be taken as a feasible alternative in future for mobile communication networks. Spectrum allocation will become a serious issue in cognitive radio networks if it is not addressed. In this research we are proposing an enhanced structural mobility model for cognitive radio network. We express a distinctive spectrum reallocation algorithm based on mobility model that incorporates secondary user’s (SU) mobility. In part, simulation findings confirm that spectrum reallocation algorithm has a good system communication overhead performance.","PeriodicalId":197525,"journal":{"name":"2023 International Conference on Networking and Communications (ICNWC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130762046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-05DOI: 10.1109/ICNWC57852.2023.10127560
M. Kanchana, Vel Murugesh Kumar, T. Anish, P. Gopirajan
The newscast system has shifted from conventional print to online media platforms in the current computing era. As a result, online media platforms enable us to absorb information more quickly and with fewer editorial constraints, and false information is disseminated at an extraordinary rate and on a massive scale. Many practical algorithms for identifying fake News have recently been created, which use unidirectional text sequence analysis. News and social context-level information were encoded using sequential neural networks. As a result, a bidirectional training strategy is capable of enhancing classification. This paper proposed Deep Fake BERT, a new model for identifying bogus News in online media. The model uses a BERT-based deep learning technique by integrating multiple simultaneous modules into a single-layer DCNN with various kernel filter sizes and strides. This combination can handle ambiguity, the most challenging aspect of natural language comprehension. This approach used classification methods such as Naive Bayes, Feed Forward Neural Networks, and LSTM, and prediction results were compared. Based on the comparison, the proposed model yields a classification accuracy is 99.25% to the existing methods.
{"title":"Deep Fake BERT: Efficient Online Fake News Detection System","authors":"M. Kanchana, Vel Murugesh Kumar, T. Anish, P. Gopirajan","doi":"10.1109/ICNWC57852.2023.10127560","DOIUrl":"https://doi.org/10.1109/ICNWC57852.2023.10127560","url":null,"abstract":"The newscast system has shifted from conventional print to online media platforms in the current computing era. As a result, online media platforms enable us to absorb information more quickly and with fewer editorial constraints, and false information is disseminated at an extraordinary rate and on a massive scale. Many practical algorithms for identifying fake News have recently been created, which use unidirectional text sequence analysis. News and social context-level information were encoded using sequential neural networks. As a result, a bidirectional training strategy is capable of enhancing classification. This paper proposed Deep Fake BERT, a new model for identifying bogus News in online media. The model uses a BERT-based deep learning technique by integrating multiple simultaneous modules into a single-layer DCNN with various kernel filter sizes and strides. This combination can handle ambiguity, the most challenging aspect of natural language comprehension. This approach used classification methods such as Naive Bayes, Feed Forward Neural Networks, and LSTM, and prediction results were compared. Based on the comparison, the proposed model yields a classification accuracy is 99.25% to the existing methods.","PeriodicalId":197525,"journal":{"name":"2023 International Conference on Networking and Communications (ICNWC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130791893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-05DOI: 10.1109/ICNWC57852.2023.10127434
A. S. Maria, R. Sunder, R. Kumar
Approximately about two billion peoples are affected by obesity that has drawn significant attention on social media. As the sedentary lifestyle which includes consumption of junk foods, no physical activities,spending more on screen,etc are one of the causes of obesity.Obesity generally refers to that a person’s body possessing an excessive amount of fat.There is a huge increase in obesity cases which resulting cardiac problems,stroke,insomnia, breathing problems,etc.Type-2 diabetes has been detected in the patients suffering from obesity recently. The studies showing that there are lot of young individuals and children’s who has been suffering from overweight and obesity issues in Bangladesh. Here, a strategy for predicting the risk of obesity is proposed that makes use of various machine learning methods. The dataset Obesity and Lifestyle taken from Kaggle site which is collection of different data based on the eating habits and physical conditions,such as height, weight,calorie intake,physical activities are just a few of the 17 different categories in the dataset that reflect the elements that cause obesity. Several machine learning methods include Gradient Boosting Classifier, Adaptive Boosting (ADA boosting), K-nearest Neighbor (K-NN), Support Vector Machine (SVM), Random Forest, and Decision Tree. A few important performance factors are used to group the models. Predicting the levels of high, medium, and low obesity in this case using the experimental results. The gradient boosting techniques have the highest accuracy 97.08% in comparison to other classifiers
{"title":"Obesity Risk Prediction Using Machine Learning Approach","authors":"A. S. Maria, R. Sunder, R. Kumar","doi":"10.1109/ICNWC57852.2023.10127434","DOIUrl":"https://doi.org/10.1109/ICNWC57852.2023.10127434","url":null,"abstract":"Approximately about two billion peoples are affected by obesity that has drawn significant attention on social media. As the sedentary lifestyle which includes consumption of junk foods, no physical activities,spending more on screen,etc are one of the causes of obesity.Obesity generally refers to that a person’s body possessing an excessive amount of fat.There is a huge increase in obesity cases which resulting cardiac problems,stroke,insomnia, breathing problems,etc.Type-2 diabetes has been detected in the patients suffering from obesity recently. The studies showing that there are lot of young individuals and children’s who has been suffering from overweight and obesity issues in Bangladesh. Here, a strategy for predicting the risk of obesity is proposed that makes use of various machine learning methods. The dataset Obesity and Lifestyle taken from Kaggle site which is collection of different data based on the eating habits and physical conditions,such as height, weight,calorie intake,physical activities are just a few of the 17 different categories in the dataset that reflect the elements that cause obesity. Several machine learning methods include Gradient Boosting Classifier, Adaptive Boosting (ADA boosting), K-nearest Neighbor (K-NN), Support Vector Machine (SVM), Random Forest, and Decision Tree. A few important performance factors are used to group the models. Predicting the levels of high, medium, and low obesity in this case using the experimental results. The gradient boosting techniques have the highest accuracy 97.08% in comparison to other classifiers","PeriodicalId":197525,"journal":{"name":"2023 International Conference on Networking and Communications (ICNWC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130923372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-05DOI: 10.1109/icnwc57852.2023.10127338
{"title":"ICNWC 2023 Cover Page","authors":"","doi":"10.1109/icnwc57852.2023.10127338","DOIUrl":"https://doi.org/10.1109/icnwc57852.2023.10127338","url":null,"abstract":"","PeriodicalId":197525,"journal":{"name":"2023 International Conference on Networking and Communications (ICNWC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114917812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-05DOI: 10.1109/ICNWC57852.2023.10127392
Asha Abraham, R. Kayalvizhi, Habeeb Shaik Mohideen, Ancy Abraham
The ability to accurately diagnose cancer is crucial to saving lives. Epithelial Ovarian Cancer (EOC) is a hard and serious disease that affects many women in worldwide. It has a poor prognosis and a molecular pathogenesis that is still unknown. Nowadays, RNA-Seq-based gene expression data have paved the way for more effective treatment in order to increase the early diagnosis of cancer. In this paper, a classweight balancing ANN is employed to detect recurrent ovarian cancer for RNA-Seq data. The model performed admirably, accurately classifying both primary and recurrent tumors without bias with 98% of accuracy rate. Later the DL model is saved using Python’s Pickle tool to avoid re-training and the pre-trained model generated for the same output. The proposed pretrained CWAOMT produced output within 12milliseconds as compared with 466milliseconds before pretraining. The experiment shows that the suggested CWAOMT performed better than the classification without data balancing. This pretrained model can be employed for later classifications of similar data without losing the achieved trained outcome.
{"title":"CWAOMT: Class Weight balanced Artificial Neural Network model for the Classification of Ovarian Malignancy from Transcriptomic Profiles","authors":"Asha Abraham, R. Kayalvizhi, Habeeb Shaik Mohideen, Ancy Abraham","doi":"10.1109/ICNWC57852.2023.10127392","DOIUrl":"https://doi.org/10.1109/ICNWC57852.2023.10127392","url":null,"abstract":"The ability to accurately diagnose cancer is crucial to saving lives. Epithelial Ovarian Cancer (EOC) is a hard and serious disease that affects many women in worldwide. It has a poor prognosis and a molecular pathogenesis that is still unknown. Nowadays, RNA-Seq-based gene expression data have paved the way for more effective treatment in order to increase the early diagnosis of cancer. In this paper, a classweight balancing ANN is employed to detect recurrent ovarian cancer for RNA-Seq data. The model performed admirably, accurately classifying both primary and recurrent tumors without bias with 98% of accuracy rate. Later the DL model is saved using Python’s Pickle tool to avoid re-training and the pre-trained model generated for the same output. The proposed pretrained CWAOMT produced output within 12milliseconds as compared with 466milliseconds before pretraining. The experiment shows that the suggested CWAOMT performed better than the classification without data balancing. This pretrained model can be employed for later classifications of similar data without losing the achieved trained outcome.","PeriodicalId":197525,"journal":{"name":"2023 International Conference on Networking and Communications (ICNWC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117332345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-05DOI: 10.1109/ICNWC57852.2023.10127350
Sangramjit Hazarika, A. M. Senthil Kumar
In this digital era, there must be a system which can summarize huge lot of data and categorize the documents under specific topic without its semantic meaning being detached. Some important information can be extracted out of these documents as and when it is needed. The system can ease out many cumbersome processes which in other times might require a lot of manual work. Additionally, it becomes easy to navigate through a summarized version of a document rather than investigating a huge lot. The efficiency gets increased and manual work gets decreased. The system is basically an integrated version of both topic modelling and question answering with suitable machine learning algorithms. So, in short, the system works out to ease out some traditional work and can also be a solution to some technical problems related to storage and processing since a summarized version of the document given as input is only stored and further processed to give specific answers to the queries raised by the users.
{"title":"A Novel Query Based Summerizer Model Of Product Reviews Using Modified LDA","authors":"Sangramjit Hazarika, A. M. Senthil Kumar","doi":"10.1109/ICNWC57852.2023.10127350","DOIUrl":"https://doi.org/10.1109/ICNWC57852.2023.10127350","url":null,"abstract":"In this digital era, there must be a system which can summarize huge lot of data and categorize the documents under specific topic without its semantic meaning being detached. Some important information can be extracted out of these documents as and when it is needed. The system can ease out many cumbersome processes which in other times might require a lot of manual work. Additionally, it becomes easy to navigate through a summarized version of a document rather than investigating a huge lot. The efficiency gets increased and manual work gets decreased. The system is basically an integrated version of both topic modelling and question answering with suitable machine learning algorithms. So, in short, the system works out to ease out some traditional work and can also be a solution to some technical problems related to storage and processing since a summarized version of the document given as input is only stored and further processed to give specific answers to the queries raised by the users.","PeriodicalId":197525,"journal":{"name":"2023 International Conference on Networking and Communications (ICNWC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116362335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-05DOI: 10.1109/ICNWC57852.2023.10127496
Sujana Ray, A. M. Senthil Kumar
Climate crisis is one of the most talked about issues in the world today. In spite of a global agreement on the necessity to protect the earth from global warming, people still lack awareness on the graveness of the situation. Social media platforms such as Twitter, Facebook, Instagram, Reddit and others offer immense opportunities for people to become vocal and participate with their opinions and thoughts on these practical challenges by exchanging information and talking about them. By looking at their attitudes and the issues they discuss, it is possible to determine in this research how users of Reddit, one of the most well-known and popular social media platforms in the world, feel about climate change. Retrieved comments and posts are classified into two sentiment classes: Positive and Negative. To understand the sentiments, we find sentiment targets by comparing two neural networks CNN and RNN and using the more accurate model to predict sentiments of the comments in the test dataset and analyse the nature of climate change discussion over time. Although the computational maximal accuracy for the two models is comparable, it was discovered that the CNN model scored marginally better than the RNN in terms of average precision, average accuracy, and average loss. The examination of Reddit users’ opinions demonstrates that the general attitude is negative, particularly when people acknowledge extreme weather events that have the potential to impact the public wellbeing framework.
{"title":"Prediction and Analysis of Sentiments of Reddit Users towards the Climate Change Crisis","authors":"Sujana Ray, A. M. Senthil Kumar","doi":"10.1109/ICNWC57852.2023.10127496","DOIUrl":"https://doi.org/10.1109/ICNWC57852.2023.10127496","url":null,"abstract":"Climate crisis is one of the most talked about issues in the world today. In spite of a global agreement on the necessity to protect the earth from global warming, people still lack awareness on the graveness of the situation. Social media platforms such as Twitter, Facebook, Instagram, Reddit and others offer immense opportunities for people to become vocal and participate with their opinions and thoughts on these practical challenges by exchanging information and talking about them. By looking at their attitudes and the issues they discuss, it is possible to determine in this research how users of Reddit, one of the most well-known and popular social media platforms in the world, feel about climate change. Retrieved comments and posts are classified into two sentiment classes: Positive and Negative. To understand the sentiments, we find sentiment targets by comparing two neural networks CNN and RNN and using the more accurate model to predict sentiments of the comments in the test dataset and analyse the nature of climate change discussion over time. Although the computational maximal accuracy for the two models is comparable, it was discovered that the CNN model scored marginally better than the RNN in terms of average precision, average accuracy, and average loss. The examination of Reddit users’ opinions demonstrates that the general attitude is negative, particularly when people acknowledge extreme weather events that have the potential to impact the public wellbeing framework.","PeriodicalId":197525,"journal":{"name":"2023 International Conference on Networking and Communications (ICNWC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125075774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-05DOI: 10.1109/ICNWC57852.2023.10127561
D. Denslin Brabin, Sriramulu Bojjagani
A vishing attack is a category of Phishing attack in which the attacker attempts to capture clandestine information through a phone call or Short Message Service (SMS). These types of attacks mostly target financial information and uneducated people are victims. In this paper, a user friendly security mechanism is proposed for preventing vishing attack in banking system under one nation. The proposed authentication mechanism uses a Central Banking Server (CBS) which act as an Authentication Server (AS) and a nationwide unique phone number. The proposed approach is simulated and analyzed by means of Scyther which is a protocol verification tool and the results show that our mechanism is more protected and harmless from vishing attacks.
{"title":"A Secure Mechanism for Prevention of Vishing Attack in Banking System","authors":"D. Denslin Brabin, Sriramulu Bojjagani","doi":"10.1109/ICNWC57852.2023.10127561","DOIUrl":"https://doi.org/10.1109/ICNWC57852.2023.10127561","url":null,"abstract":"A vishing attack is a category of Phishing attack in which the attacker attempts to capture clandestine information through a phone call or Short Message Service (SMS). These types of attacks mostly target financial information and uneducated people are victims. In this paper, a user friendly security mechanism is proposed for preventing vishing attack in banking system under one nation. The proposed authentication mechanism uses a Central Banking Server (CBS) which act as an Authentication Server (AS) and a nationwide unique phone number. The proposed approach is simulated and analyzed by means of Scyther which is a protocol verification tool and the results show that our mechanism is more protected and harmless from vishing attacks.","PeriodicalId":197525,"journal":{"name":"2023 International Conference on Networking and Communications (ICNWC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128545702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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