Pub Date : 2021-03-17DOI: 10.1109/INDIACom51348.2021.00088
M. Rani, Gagandeep
Feature selection in Intrusion Detection System (IDS) helps in optimizing the classification process. Being an optimization problem, it is vitally important to choose the appropriate subset of features from feature space. In this paper, Artificial Bee Colony (ABC) algorithm has been used for feature selection process followed by random forest classifier applied for classification task. The proposed model is evaluated over two well-known datasets, i.e. NSL KDD and UNSW-NB15. The experimental results show that the proposed approach is able to select good feature set from both datasets using 80.83% and 88.17% accuracy. The performance of the system is also compared with the existing literature work which uses same datasets.
{"title":"Employing Artificial Bee Colony Algorithm for Feature Selection in Intrusion Detection System","authors":"M. Rani, Gagandeep","doi":"10.1109/INDIACom51348.2021.00088","DOIUrl":"https://doi.org/10.1109/INDIACom51348.2021.00088","url":null,"abstract":"Feature selection in Intrusion Detection System (IDS) helps in optimizing the classification process. Being an optimization problem, it is vitally important to choose the appropriate subset of features from feature space. In this paper, Artificial Bee Colony (ABC) algorithm has been used for feature selection process followed by random forest classifier applied for classification task. The proposed model is evaluated over two well-known datasets, i.e. NSL KDD and UNSW-NB15. The experimental results show that the proposed approach is able to select good feature set from both datasets using 80.83% and 88.17% accuracy. The performance of the system is also compared with the existing literature work which uses same datasets.","PeriodicalId":415594,"journal":{"name":"2021 8th International Conference on Computing for Sustainable Global Development (INDIACom)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116801859","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 : 2021-03-17DOI: 10.1109/INDIACom51348.2021.00003
Mahendra Prasad Nath, S. Mohanty, S. Priyadarshini
Wireless Sensor Networks (WSNs) find broad range of adoration and wide spread uses in various applications. In such networks, the deployed sensors trap and transfer the data intellectually to the base station that is the ultimate destination. This paper briefs about the various applications of machine learning in sensor networks. The Principal Component Analysis (PCA) along with k-means clustering strategies, used in case of unsupervised learning, are discussed. A discussion on different functional challenges occurring in case of sensor networks is also presented.
{"title":"Application of Machine Learning in wireless Sensor Network","authors":"Mahendra Prasad Nath, S. Mohanty, S. Priyadarshini","doi":"10.1109/INDIACom51348.2021.00003","DOIUrl":"https://doi.org/10.1109/INDIACom51348.2021.00003","url":null,"abstract":"Wireless Sensor Networks (WSNs) find broad range of adoration and wide spread uses in various applications. In such networks, the deployed sensors trap and transfer the data intellectually to the base station that is the ultimate destination. This paper briefs about the various applications of machine learning in sensor networks. The Principal Component Analysis (PCA) along with k-means clustering strategies, used in case of unsupervised learning, are discussed. A discussion on different functional challenges occurring in case of sensor networks is also presented.","PeriodicalId":415594,"journal":{"name":"2021 8th International Conference on Computing for Sustainable Global Development (INDIACom)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115125919","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}
The recently developed Tilting Rotors Quadcopters (TRQs) embedded system has gained wide application because of its feature to track a particular trajectory with fix tilting. Attaining precise TRQ dynamic models, particularly by bearing in mind dynamics of motors, is vital for designing controllers. In this study, a TRQ platform is modeled on the basis of the Newton-Euler technique while taking into account motor dynamics. The motor speed is controlled as a closed loop system by applying a lead compensator. Mathematical models have been developed and simulated for the inertial sensors and the battery. The TRQ system was simulated using Matlab and tested with different conditions. The proposed model supports estimations of voltage drop under different environmental conditions. Initial sensors noise is also included in the proposed model.
{"title":"Tilting-rotors Quadcopters: A New Dynamics Modelling and Simulation based on the Newton-Euler Method with Lead Compensator Control","authors":"Izzat Al-Darraji, Morched Derbali, Georgios Tsaramirsis","doi":"10.1109/INDIACom51348.2021.00063","DOIUrl":"https://doi.org/10.1109/INDIACom51348.2021.00063","url":null,"abstract":"The recently developed Tilting Rotors Quadcopters (TRQs) embedded system has gained wide application because of its feature to track a particular trajectory with fix tilting. Attaining precise TRQ dynamic models, particularly by bearing in mind dynamics of motors, is vital for designing controllers. In this study, a TRQ platform is modeled on the basis of the Newton-Euler technique while taking into account motor dynamics. The motor speed is controlled as a closed loop system by applying a lead compensator. Mathematical models have been developed and simulated for the inertial sensors and the battery. The TRQ system was simulated using Matlab and tested with different conditions. The proposed model supports estimations of voltage drop under different environmental conditions. Initial sensors noise is also included in the proposed model.","PeriodicalId":415594,"journal":{"name":"2021 8th International Conference on Computing for Sustainable Global Development (INDIACom)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115480727","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 : 2021-03-17DOI: 10.1109/INDIACom51348.2021.00099
M. N, J. S
Every student has individual thought process which make them unique and intelligent in their own way. Intellectual behaviour is a relation between thoughts, learning and knowledge. This paper focus on Students' feedback which is crucial for an institution to evaluate intellectual behavior of student. Opinion Mining (OM) deals with classifying and identifying opinion expressed by the students. The requirement of OM is to understand and analyse dissimilar behaviour of different personality which is a collection of extracted information from various resources. A solution to analyse cognitive behaviour of students based on the OM is a Natural Language Processing (NLP) and task of extracting information which discovers user's opinion interpreted in terms of positive or negative comments. The OM has played an important role in cognizing the emotional and intellectual behavior of university student through social media which may either be accomplished with viable or untenable education. Machine Learning (ML) algorithm helps to find the accuracy of polarity present in the OM. The Support Vector Machine (SVM) has shown better accuracy which is 93% through OM on the analysis of intellectual behavior for the student based on their knowledge and learning methodology.
{"title":"Intellectual Behaviour of Student Based on Education Data Determined by Opinion Mining","authors":"M. N, J. S","doi":"10.1109/INDIACom51348.2021.00099","DOIUrl":"https://doi.org/10.1109/INDIACom51348.2021.00099","url":null,"abstract":"Every student has individual thought process which make them unique and intelligent in their own way. Intellectual behaviour is a relation between thoughts, learning and knowledge. This paper focus on Students' feedback which is crucial for an institution to evaluate intellectual behavior of student. Opinion Mining (OM) deals with classifying and identifying opinion expressed by the students. The requirement of OM is to understand and analyse dissimilar behaviour of different personality which is a collection of extracted information from various resources. A solution to analyse cognitive behaviour of students based on the OM is a Natural Language Processing (NLP) and task of extracting information which discovers user's opinion interpreted in terms of positive or negative comments. The OM has played an important role in cognizing the emotional and intellectual behavior of university student through social media which may either be accomplished with viable or untenable education. Machine Learning (ML) algorithm helps to find the accuracy of polarity present in the OM. The Support Vector Machine (SVM) has shown better accuracy which is 93% through OM on the analysis of intellectual behavior for the student based on their knowledge and learning methodology.","PeriodicalId":415594,"journal":{"name":"2021 8th International Conference on Computing for Sustainable Global Development (INDIACom)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130137356","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 : 2021-03-17DOI: 10.1109/INDIACom51348.2021.00055
Isha Malhotra, A. Tayal
COVID-19 has been a harsh reality impacting the worldwide population but resulting lockdown has shown a positive impact on the quality of air. The case study for India has been considered for statistical modeling and evaluation of the air quality amid pandemic. According to World Economic Forum, India had 6 out of 10 world's most polluted cities. The highest pollution level used to be around 900 in the extreme cases and sometimes beyond the measurable scale. According to WHO, anything above 25 is marked unsafe. When all the commotion was put to halt during lockdown, the AQI surprisingly fell below 20. On 3rd April 2020, i.e., just after a week of lockdown, the AQI at one of the stations in Delhi was measured 19 and that was a huge positive impact on environment. Spatial graph, bar-charts, time-series plots and boxplots have been incorporated for the analysis. Hypothesis testing proves that the lockdown has resulted in significant improvement in the level of polluting elements. This validates that tweaking daily activities can help maintaining the air-quality.
{"title":"Statistical Modeling and Evaluation of Air Quality Impact due to COVID-19 Lockdown","authors":"Isha Malhotra, A. Tayal","doi":"10.1109/INDIACom51348.2021.00055","DOIUrl":"https://doi.org/10.1109/INDIACom51348.2021.00055","url":null,"abstract":"COVID-19 has been a harsh reality impacting the worldwide population but resulting lockdown has shown a positive impact on the quality of air. The case study for India has been considered for statistical modeling and evaluation of the air quality amid pandemic. According to World Economic Forum, India had 6 out of 10 world's most polluted cities. The highest pollution level used to be around 900 in the extreme cases and sometimes beyond the measurable scale. According to WHO, anything above 25 is marked unsafe. When all the commotion was put to halt during lockdown, the AQI surprisingly fell below 20. On 3rd April 2020, i.e., just after a week of lockdown, the AQI at one of the stations in Delhi was measured 19 and that was a huge positive impact on environment. Spatial graph, bar-charts, time-series plots and boxplots have been incorporated for the analysis. Hypothesis testing proves that the lockdown has resulted in significant improvement in the level of polluting elements. This validates that tweaking daily activities can help maintaining the air-quality.","PeriodicalId":415594,"journal":{"name":"2021 8th International Conference on Computing for Sustainable Global Development (INDIACom)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125374845","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 : 2021-03-17DOI: 10.1109/INDIACom51348.2021.00061
M. Almutairi, M. Yamin, G. Halikias
The Internet generates very large amounts of structured and unstructured data which creates storage, maintenance, management, sharing, privacy and security challenges. In the world today, organizations exchange and merge different types of data at a centralized location for the purpose of analysis and benefitting the organisations and individuals in achieving their business, economic, social, educational, cultural, and health objectives. The data merging or integration is a challenging process because of different type of data formats, structures, models, schemas, entities, attributes, and features. Integration is a complex and tedious process, and involves a number of technologies and extensive processing, and so it is not straightforward to integrate very large data with a variety of data formats and types. This paper discusses issues and complexities faced in data integration processes. The paper also discusses different methods of data integration, their advantages and disadvantages, and provides a comparative analysis to gain better insights from examples of recently completed projects.
{"title":"An Analysis of Data Integration Challenges from Heterogeneous Databases","authors":"M. Almutairi, M. Yamin, G. Halikias","doi":"10.1109/INDIACom51348.2021.00061","DOIUrl":"https://doi.org/10.1109/INDIACom51348.2021.00061","url":null,"abstract":"The Internet generates very large amounts of structured and unstructured data which creates storage, maintenance, management, sharing, privacy and security challenges. In the world today, organizations exchange and merge different types of data at a centralized location for the purpose of analysis and benefitting the organisations and individuals in achieving their business, economic, social, educational, cultural, and health objectives. The data merging or integration is a challenging process because of different type of data formats, structures, models, schemas, entities, attributes, and features. Integration is a complex and tedious process, and involves a number of technologies and extensive processing, and so it is not straightforward to integrate very large data with a variety of data formats and types. This paper discusses issues and complexities faced in data integration processes. The paper also discusses different methods of data integration, their advantages and disadvantages, and provides a comparative analysis to gain better insights from examples of recently completed projects.","PeriodicalId":415594,"journal":{"name":"2021 8th International Conference on Computing for Sustainable Global Development (INDIACom)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127807225","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 : 2021-03-17DOI: 10.1109/INDIACom51348.2021.00133
Yashu Verma, M. S. Manna
In the recent times BLDC motors have been gaining popularity in the field of electrical vehicles. This paper presents a simulation-based analysis of Permanent magnet BLDC motor having high slot and pole configuration for the application of electric vehicles. A 1500W, 120V, 3000rpm BLDC motor was designed using ANSYS maxwell software. The initial geometry was designed in RMxprt tool of ANSYS Maxwell and its analytical results were obtained, this design was then taken into Maxwell 2D environment where 2D model of the designed motor was obtained. After obtaining 2D model the designed motors dynamic performance was analyzed using finite element analysis. The results obtained were good and high efficiency was obtained.
{"title":"Simulation Based Analysis of Outer Rotor Brushless DC Motor for Electric Vehicle Application","authors":"Yashu Verma, M. S. Manna","doi":"10.1109/INDIACom51348.2021.00133","DOIUrl":"https://doi.org/10.1109/INDIACom51348.2021.00133","url":null,"abstract":"In the recent times BLDC motors have been gaining popularity in the field of electrical vehicles. This paper presents a simulation-based analysis of Permanent magnet BLDC motor having high slot and pole configuration for the application of electric vehicles. A 1500W, 120V, 3000rpm BLDC motor was designed using ANSYS maxwell software. The initial geometry was designed in RMxprt tool of ANSYS Maxwell and its analytical results were obtained, this design was then taken into Maxwell 2D environment where 2D model of the designed motor was obtained. After obtaining 2D model the designed motors dynamic performance was analyzed using finite element analysis. The results obtained were good and high efficiency was obtained.","PeriodicalId":415594,"journal":{"name":"2021 8th International Conference on Computing for Sustainable Global Development (INDIACom)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124551384","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 : 2021-03-17DOI: 10.1109/INDIACom51348.2021.00110
Rishm, V. Laxmi
The research paper focuses on cancer prediction of patients based on DNA sequencing. The whole study is designed around collecting different DNA sequencing samples for patients over the years. The proposed technique in the current research paper is based on the hybridization of the Hidden Markov Model and Gaussian Mixture clustering. HMM, and GM is proposed to identify the expected probability of cancer of the patients. This hybrid model specifies the Bayesian-hidden-Markov model and Gaussian- Mixture clustering approach that is used to identify the genetic variation present in the human Genome. These changes in the Genome may cause cancer. The proposed algorithm is the hybridization of the Bayesian-hidden-Markov model and Gaussian-Mixture clustering approach which provides the optimization of results. The result shows the prediction with better accuracy. The proposed approach shows the cancer prediction with a higher level of accuracy with an improvement of 4.45%. The error rate for the prediction has improved by 2.25%.
{"title":"Cancer Diagnosis through Hidden Markov Model and Gaussian Mixture based Novel DNA Sequencing Approach","authors":"Rishm, V. Laxmi","doi":"10.1109/INDIACom51348.2021.00110","DOIUrl":"https://doi.org/10.1109/INDIACom51348.2021.00110","url":null,"abstract":"The research paper focuses on cancer prediction of patients based on DNA sequencing. The whole study is designed around collecting different DNA sequencing samples for patients over the years. The proposed technique in the current research paper is based on the hybridization of the Hidden Markov Model and Gaussian Mixture clustering. HMM, and GM is proposed to identify the expected probability of cancer of the patients. This hybrid model specifies the Bayesian-hidden-Markov model and Gaussian- Mixture clustering approach that is used to identify the genetic variation present in the human Genome. These changes in the Genome may cause cancer. The proposed algorithm is the hybridization of the Bayesian-hidden-Markov model and Gaussian-Mixture clustering approach which provides the optimization of results. The result shows the prediction with better accuracy. The proposed approach shows the cancer prediction with a higher level of accuracy with an improvement of 4.45%. The error rate for the prediction has improved by 2.25%.","PeriodicalId":415594,"journal":{"name":"2021 8th International Conference on Computing for Sustainable Global Development (INDIACom)","volume":"164 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121427189","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 : 2021-03-17DOI: 10.1109/INDIACom51348.2021.00139
Tanmay Deshpande, Sarun Varghese, Pratik Dynaneshwar Kale, M. Atre
Indian Army is always being troubled by the constant attacks of the militants and different terrorist organizations. Such encounters always cost us the lives of the soldiers who work for us with an immeasurable selfless attitude, many a times getting slaughtered in different regions of India. Such brainwashed mobs exploit the loopholes of the system, where the ground level officials don't have on-the-spot decision making authority. They use guns, hand grenades, Improvised Explosive Devices (IED) as weapons. Incidents of stone pelting, damage to property, fire incident, mob lynching, etc set the tone for an unrest society. Unfortunately to deal with the rage of such brainwashed minds, soldiers are the first line of defense and are put into a heated situation which leads to bloodshed. To avoid this, authors implemented a model which uses Artificial Intelligence, to predict the location of the next probable ‘Anti-National’ incident and the number of casualties in that incident. By using the principles of Timeseries Analysis, Auto Regressive Integrated Moving Average model in combination with Random Forest Regressor, authors predict the location of the next probable incident and the number of casualties in it. Using Beautiful Soup Library database is created by scraping the news from webpages. A Logistic Regression, is used to classify whether the following news is ‘Anti-National’ or not. The developed algorithm is used effectively to find i) the patterns in these attacks, ii) the factors which spark such incidents and help to take precautionary actions or completely avoid them. This model implementation can track down the activities and assist the Indian Army.
{"title":"Incident Classification, Prediction of Location and Casualties","authors":"Tanmay Deshpande, Sarun Varghese, Pratik Dynaneshwar Kale, M. Atre","doi":"10.1109/INDIACom51348.2021.00139","DOIUrl":"https://doi.org/10.1109/INDIACom51348.2021.00139","url":null,"abstract":"Indian Army is always being troubled by the constant attacks of the militants and different terrorist organizations. Such encounters always cost us the lives of the soldiers who work for us with an immeasurable selfless attitude, many a times getting slaughtered in different regions of India. Such brainwashed mobs exploit the loopholes of the system, where the ground level officials don't have on-the-spot decision making authority. They use guns, hand grenades, Improvised Explosive Devices (IED) as weapons. Incidents of stone pelting, damage to property, fire incident, mob lynching, etc set the tone for an unrest society. Unfortunately to deal with the rage of such brainwashed minds, soldiers are the first line of defense and are put into a heated situation which leads to bloodshed. To avoid this, authors implemented a model which uses Artificial Intelligence, to predict the location of the next probable ‘Anti-National’ incident and the number of casualties in that incident. By using the principles of Timeseries Analysis, Auto Regressive Integrated Moving Average model in combination with Random Forest Regressor, authors predict the location of the next probable incident and the number of casualties in it. Using Beautiful Soup Library database is created by scraping the news from webpages. A Logistic Regression, is used to classify whether the following news is ‘Anti-National’ or not. The developed algorithm is used effectively to find i) the patterns in these attacks, ii) the factors which spark such incidents and help to take precautionary actions or completely avoid them. This model implementation can track down the activities and assist the Indian Army.","PeriodicalId":415594,"journal":{"name":"2021 8th International Conference on Computing for Sustainable Global Development (INDIACom)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130706081","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}
Cloud Computing is a technology which provides flexibility through scalability. Like, Cloud computing, nowadays, Fog computing is considered more revolutionary and dynamic technology. But the main problem with the Fog computing is to take care of its security as in this also person identification is done by single Sign-In system. To come out from the security problem raised in Fog computing, an innovative approach has been suggested here. In the present paper, an approach has been proposed that combines different biometric techniques to verify the authenticity of a person and provides a complete model that will be able to provide a necessary level of verification and security in fog computing. In this model, several biometric techniques have been used and each one of them individually helps extract out more authentic and detailed information after every step. Further, in the presented paper, different techniques and methodologies have been examined to assess the usefulness of proposed technology in reducing the security threats. The paper delivers a capacious technique for biometric authentication for bolstering the fog security.
{"title":"Multi-factor Biometric Authentication Approach for Fog Computing to ensure Security Perspective","authors":"F. Ahmadi, Sonia, Gaurav Gupta, Syed Rameem Zahra, Preeti Baglat, Puja Thakur","doi":"10.1109/INDIACom51348.2021.00031","DOIUrl":"https://doi.org/10.1109/INDIACom51348.2021.00031","url":null,"abstract":"Cloud Computing is a technology which provides flexibility through scalability. Like, Cloud computing, nowadays, Fog computing is considered more revolutionary and dynamic technology. But the main problem with the Fog computing is to take care of its security as in this also person identification is done by single Sign-In system. To come out from the security problem raised in Fog computing, an innovative approach has been suggested here. In the present paper, an approach has been proposed that combines different biometric techniques to verify the authenticity of a person and provides a complete model that will be able to provide a necessary level of verification and security in fog computing. In this model, several biometric techniques have been used and each one of them individually helps extract out more authentic and detailed information after every step. Further, in the presented paper, different techniques and methodologies have been examined to assess the usefulness of proposed technology in reducing the security threats. The paper delivers a capacious technique for biometric authentication for bolstering the fog security.","PeriodicalId":415594,"journal":{"name":"2021 8th International Conference on Computing for Sustainable Global Development (INDIACom)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128020696","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：