AfDaq is an open-source, plug and play, MATLAB based tool that offers the capabilities of multi-channel real-time data acquisition, visualization, manipulation, and local saving of data for offline analysis. The MATLAB Arduino package suffers from serious timing jitter during real-time data acquisition. This timing jitter associated with four main commands (Analog Read, Digital Read, Digital Write and PWM Set) available in MATLAB Arduino package is statistically analyzed and a simple post-hoc timing jitter correction mechanism is proposed to acquire data points with high timing accuracy. The benchmark of the final program is conducted at various sampling rates for multichannel acquisition with 10 Hz comes as the maximum sampling rate for 5 channel recording. In the end, a use case of the developed tool for physiological data acquisition in multimodal biofeedback is presented. The software tool, data, and analysis scripts that support the findings of this study are released as an open-source project to support the replicability and reproducibility of the research.
AfDaq是一个开源，即插即用，基于MATLAB的工具，提供多通道实时数据采集，可视化，操作和本地保存数据以进行离线分析的功能。MATLAB Arduino包在实时数据采集过程中存在严重的时序抖动。统计分析了MATLAB Arduino包中四个主要命令(Analog Read, Digital Read, Digital Write和PWM Set)的时序抖动，并提出了一种简单的事后时序抖动校正机制，以获得具有高时序精度的数据点。最终程序的基准测试以不同的采样率进行多通道采集，10 Hz作为5通道记录的最大采样率。最后，给出了该工具在多模态生物反馈中生理数据采集的用例。支持本研究结果的软件工具、数据和分析脚本作为开源项目发布，以支持研究的可复制性和可再现性。
{"title":"MATLAB-Based Real-Time Data Acquisition Tool for Multimodal Biofeedback and Arduino-Based Instruments: Arduino Firmata Data Acquisition (AfDaq)","authors":"Kulbhushan Chand, A. Khosla","doi":"10.4018/jitr.299922","DOIUrl":"https://doi.org/10.4018/jitr.299922","url":null,"abstract":"AfDaq is an open-source, plug and play, MATLAB based tool that offers the capabilities of multi-channel real-time data acquisition, visualization, manipulation, and local saving of data for offline analysis. The MATLAB Arduino package suffers from serious timing jitter during real-time data acquisition. This timing jitter associated with four main commands (Analog Read, Digital Read, Digital Write and PWM Set) available in MATLAB Arduino package is statistically analyzed and a simple post-hoc timing jitter correction mechanism is proposed to acquire data points with high timing accuracy. The benchmark of the final program is conducted at various sampling rates for multichannel acquisition with 10 Hz comes as the maximum sampling rate for 5 channel recording. In the end, a use case of the developed tool for physiological data acquisition in multimodal biofeedback is presented. The software tool, data, and analysis scripts that support the findings of this study are released as an open-source project to support the replicability and reproducibility of the research.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124048763","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}
With the steady rise in the use of smartphones, specifically android smartphones, there is an ongoing need to build strong Intrusion Detection Systems to protect ourselves from malicious software attacks, especially on Android smartphones. This work focuses on a sub-group of android malware, scareware. The novelty of this work lies in being able to detect the various scareware families individually using a small number of network attributes, determined by a recursive feature elimination process based on information gain. No work has yet been done on analyzing the scareware families individually. Results of this work show that the number of bytes initially sent back and forth, packet size, amount of time between flows and flow duration are the most important attributes that would be needed to classify a scareware attack. Three classifiers, Decision Tree, Naïve Bayes and OneR, were used for classification. The highest average classification accuracy (79.5%) was achieved by the Decision Tree classifier with a minimum of 44 attributes.
{"title":"Machine Learning for Android Scareware Detection","authors":"S. Bagui, Hunter Brock","doi":"10.4018/jitr.298326","DOIUrl":"https://doi.org/10.4018/jitr.298326","url":null,"abstract":"With the steady rise in the use of smartphones, specifically android smartphones, there is an ongoing need to build strong Intrusion Detection Systems to protect ourselves from malicious software attacks, especially on Android smartphones. This work focuses on a sub-group of android malware, scareware. The novelty of this work lies in being able to detect the various scareware families individually using a small number of network attributes, determined by a recursive feature elimination process based on information gain. No work has yet been done on analyzing the scareware families individually. Results of this work show that the number of bytes initially sent back and forth, packet size, amount of time between flows and flow duration are the most important attributes that would be needed to classify a scareware attack. Three classifiers, Decision Tree, Naïve Bayes and OneR, were used for classification. The highest average classification accuracy (79.5%) was achieved by the Decision Tree classifier with a minimum of 44 attributes.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116010024","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}
Abstract— Machine learning can be used to provide systems the ability to automatically learn and improve from experiences without being explicitly programmed. It is fundamentally a multidisciplinary field that draws on results from Artificial intelligence, probability and statistics, information theory and analysis, among other fields that impact the field of Machine Learning. Ensemble methods are techniques that can be used to improve the predictive ability of a Machine Learning model. An ensemble comprises of individually trained classifiers whose predictions are combined when classifying instances. Some of the currently popular ensemble methods include Boosting, Bagging and Stacking. In this paper, we review these methods and demonstrate why ensembles can often perform better than single models. Additionally, some new experiments are presented to demonstrate the computational ability of Stacking approach.
{"title":"Target Sentiment Analysis Ensemble for Product Review Classification","authors":"Rhoda Viviane Achieng Ogutu, R. Rimiru, C. Otieno","doi":"10.4018/jitr.299382","DOIUrl":"https://doi.org/10.4018/jitr.299382","url":null,"abstract":"Abstract— Machine learning can be used to provide systems the ability to automatically learn and improve from experiences without being explicitly programmed. It is fundamentally a multidisciplinary field that draws on results from Artificial intelligence, probability and statistics, information theory and analysis, among other fields that impact the field of Machine Learning. Ensemble methods are techniques that can be used to improve the predictive ability of a Machine Learning model. An ensemble comprises of individually trained classifiers whose predictions are combined when classifying instances. Some of the currently popular ensemble methods include Boosting, Bagging and Stacking. In this paper, we review these methods and demonstrate why ensembles can often perform better than single models. Additionally, some new experiments are presented to demonstrate the computational ability of Stacking approach.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126296811","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}
Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans. There are mainly two types of pneumonia: bacterial and viral. Likewise, patients with coronavirus can develop symptoms that belong to the common flu, pneumonia, and other respiratory diseases. Chest X-rays are the common method used to diagnose coronavirus pneumonia and it needs a medical expert to evaluate the result of X-ray. Furthermore, DL has garnered great attention among researchers in recent years in a variety of application domains such as medical image processing, computer vision, bioinformatics, and many others. In this paper, we present a comparison of Deep Convolutional Neural Networks models for automatically binary classification query chest X-ray & CT images dataset with the goal of taking precision tools to health professionals based on fined recent versions of ResNet50, InceptionV3, and VGGNet. The experiments were conducted using a chest X-ray & CT open dataset of 5856 images and confusion matrices are used to evaluate model performances.
{"title":"Coronavirus Pneumonia Classification Using X-Ray and CT Scan Images With Deep Convolutional Neural Network Models","authors":"Menaouer Brahami, Zoulikha Dermane, Nour El Houda Kebir, Sabri Mohammed, Nada Matta","doi":"10.4018/jitr.299391","DOIUrl":"https://doi.org/10.4018/jitr.299391","url":null,"abstract":"Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans. There are mainly two types of pneumonia: bacterial and viral. Likewise, patients with coronavirus can develop symptoms that belong to the common flu, pneumonia, and other respiratory diseases. Chest X-rays are the common method used to diagnose coronavirus pneumonia and it needs a medical expert to evaluate the result of X-ray. Furthermore, DL has garnered great attention among researchers in recent years in a variety of application domains such as medical image processing, computer vision, bioinformatics, and many others. In this paper, we present a comparison of Deep Convolutional Neural Networks models for automatically binary classification query chest X-ray & CT images dataset with the goal of taking precision tools to health professionals based on fined recent versions of ResNet50, InceptionV3, and VGGNet. The experiments were conducted using a chest X-ray & CT open dataset of 5856 images and confusion matrices are used to evaluate model performances.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129828605","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}
This study investigated the utility of the Internet of Things in the public sector and the factors influencing the satisfaction of its users. The study followed two directions, the first investigated managers’ perceptions and their satisfaction with using sensors for tracking vehicles. The second direction investigated drivers’ satisfaction with the system used. Results collected from 20 interviews conducted with managers revealed that cost reduction and more control over drivers’ behaviors are the contributions expected from the system. They reported the dissatisfaction of drivers based on violation of their privacy, inequity of implementation, and the low awareness of its utility. Surveys collected from drivers supported the role of trust and privacy, but failed to support the role of usefulness. The qualitative and quantitative nature of this research revealed valuable insights and concluded to important recommendations and future work.
{"title":"Utilizing the Internet of Things in the Public Sector","authors":"Mai Al-Sebae, E. Abu-Shanab","doi":"10.4018/jitr.299915","DOIUrl":"https://doi.org/10.4018/jitr.299915","url":null,"abstract":"This study investigated the utility of the Internet of Things in the public sector and the factors influencing the satisfaction of its users. The study followed two directions, the first investigated managers’ perceptions and their satisfaction with using sensors for tracking vehicles. The second direction investigated drivers’ satisfaction with the system used. Results collected from 20 interviews conducted with managers revealed that cost reduction and more control over drivers’ behaviors are the contributions expected from the system. They reported the dissatisfaction of drivers based on violation of their privacy, inequity of implementation, and the low awareness of its utility. Surveys collected from drivers supported the role of trust and privacy, but failed to support the role of usefulness. The qualitative and quantitative nature of this research revealed valuable insights and concluded to important recommendations and future work.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122790337","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}
X. He, Siqi Li, X. He, Wenqiang Wang, Xiang Zhang, Bin Wang
Credit scoring, aiming to distinguish potential loan defaulter, has played an important role in financial industry. To further improve the accuracy and efficiency of classification, this paper develops an ensemble model combined extreme gradient boosting (XGBoost) and deep neural network (DNN). In the method, training set is divided into different subsets by bagging sampling at first. Then, each subset is trained as a feature extractor by DNN and the extracted features is taken as the input of XGBoost to construct the base classifier. At last, the prediction result is the average of outputs of different base classifiers. In the training verification process, three credit datasets from the UCI machine learning repository are used to evaluate the proposed model. The outcome shows that this model is superior with a significant improvement.
{"title":"A Novel Ensemble Learning Model Combined XGBoost With Deep Neural Network for Credit Scoring","authors":"X. He, Siqi Li, X. He, Wenqiang Wang, Xiang Zhang, Bin Wang","doi":"10.4018/jitr.299924","DOIUrl":"https://doi.org/10.4018/jitr.299924","url":null,"abstract":"Credit scoring, aiming to distinguish potential loan defaulter, has played an important role in financial industry. To further improve the accuracy and efficiency of classification, this paper develops an ensemble model combined extreme gradient boosting (XGBoost) and deep neural network (DNN). In the method, training set is divided into different subsets by bagging sampling at first. Then, each subset is trained as a feature extractor by DNN and the extracted features is taken as the input of XGBoost to construct the base classifier. At last, the prediction result is the average of outputs of different base classifiers. In the training verification process, three credit datasets from the UCI machine learning repository are used to evaluate the proposed model. The outcome shows that this model is superior with a significant improvement.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124422445","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}
Mafizur Rahman, Md. Rifayet Azam Talukder, Lima Akter Setu, A. Das
In today's world, around 230 million people used the Bengali or Bangla language to communicate. These individuals are progressively associated with online exercises on famous micro-blogging and long-range interpersonal communication locales, imparting insights what's more, musings, and also the vast majority of articles are in the Bengali language. Thus, Bengali people express their emotions using the Bangla language by reviewing, commenting, or recommendations. Sentiment analysis helps determine the people's emotions expressed on social media or several online platforms. Therefore, this study focused on extracting their emotion from a Bengali text by utilizing Word2vector, Skip-Gram, and Continuous Bag of Words (CBOW) with a new Word to Index model by focusing on three individual classes happy, angry, and excited. The authors achieved the highest accuracy of 75% by utilizing the skip-gram model to classify those three types of emotions. This study also outperformed other existing works with LSTM, CNN model with existing datasets.
在当今世界，大约有2.3亿人使用孟加拉语或孟加拉语进行交流。这些人逐渐与著名的微博和远程人际交流场所的在线练习联系在一起，传授见解，更重要的是，沉思，而且绝大多数文章都是用孟加拉语写的。因此，孟加拉人用孟加拉语来表达他们的情感，通过评论、评论或推荐。情绪分析有助于确定人们在社交媒体或几个在线平台上表达的情绪。因此，本研究的重点是利用Word2vector、Skip-Gram和Continuous Bag of Words (CBOW)和一个新的Word to Index模型，以happy、angry和excited三个单独的类别为重点，从孟加拉语文本中提取他们的情感。作者利用skip-gram模型对这三种情绪进行分类，达到了75%的最高准确率。本研究也优于其他使用LSTM、CNN模型和现有数据集的现有工作。
{"title":"A Dynamic Strategy for Classifying Sentiment From Bengali Text by Utilizing Word2vector Model","authors":"Mafizur Rahman, Md. Rifayet Azam Talukder, Lima Akter Setu, A. Das","doi":"10.4018/jitr.299919","DOIUrl":"https://doi.org/10.4018/jitr.299919","url":null,"abstract":"In today's world, around 230 million people used the Bengali or Bangla language to communicate. These individuals are progressively associated with online exercises on famous micro-blogging and long-range interpersonal communication locales, imparting insights what's more, musings, and also the vast majority of articles are in the Bengali language. Thus, Bengali people express their emotions using the Bangla language by reviewing, commenting, or recommendations. Sentiment analysis helps determine the people's emotions expressed on social media or several online platforms. Therefore, this study focused on extracting their emotion from a Bengali text by utilizing Word2vector, Skip-Gram, and Continuous Bag of Words (CBOW) with a new Word to Index model by focusing on three individual classes happy, angry, and excited. The authors achieved the highest accuracy of 75% by utilizing the skip-gram model to classify those three types of emotions. This study also outperformed other existing works with LSTM, CNN model with existing datasets.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132756625","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}
This paper represent a deep study of the Local Binary Pattern (LBP) method and its variants of patterns regrouping , which is largely used in texture classification as well in other domain. The analysis of LBP’s two hundred fifty-six patterns has led us to propose a new organization of uniform and no uniform patterns into twenty-eight groups; each group assembled a number of patterns varied according to specific terms. The principal idea is to preserve the low complexity of LBP and simultaneously increase the method robustness against quality degradation caused by image operations like rotation, grey level changes, illumination and mirror effects. The experiments are done with the two texture databases Outex and Brodatz; the tests are proving the robustness of Local Binary Pattern Regrouping (LBPG) under circumstances.
{"title":"Local Binary Pattern Regrouping for Rotation Invariant Texture Classification","authors":"Asma Zitouni, B. Nini","doi":"10.4018/jitr.299945","DOIUrl":"https://doi.org/10.4018/jitr.299945","url":null,"abstract":"This paper represent a deep study of the Local Binary Pattern (LBP) method and its variants of patterns regrouping , which is largely used in texture classification as well in other domain. The analysis of LBP’s two hundred fifty-six patterns has led us to propose a new organization of uniform and no uniform patterns into twenty-eight groups; each group assembled a number of patterns varied according to specific terms. The principal idea is to preserve the low complexity of LBP and simultaneously increase the method robustness against quality degradation caused by image operations like rotation, grey level changes, illumination and mirror effects. The experiments are done with the two texture databases Outex and Brodatz; the tests are proving the robustness of Local Binary Pattern Regrouping (LBPG) under circumstances.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133154051","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 primary requirements of a heterogeneous wireless network topology, adaptive and smart resource allocation to users, protocols for routing and lifetime enhancement, access to the network with security and appropriate network selections. Routing algorithms deliberate on the performance of the network to evenly distribute load and thus enhance the lifespan of individual nodes, clustering algorithm decides on allowing the right nodes into the network for enhanced security feature, and finally the ability to analyse, predict the context of individual nodes/sensors in the network. Architecture of the proposed network includes the parameters such as decision making ability to sustain the clusters, decision on members of the clusters until the communication process is completed, local network abilities and disabilities, price, preferences of individuals, terminal and access points of the service providers. Network lifetime of the entire network is observed to be enhanced up to 91% with triple layer architecture.
{"title":"Security-Enabled Retransmission and Energy Conservation Architecture With Cluster-Based Multipath Routing in Heterogeneous Wireless Networks","authors":"G. Asha, S. Srivatsa","doi":"10.4018/jitr.299951","DOIUrl":"https://doi.org/10.4018/jitr.299951","url":null,"abstract":"The primary requirements of a heterogeneous wireless network topology, adaptive and smart resource allocation to users, protocols for routing and lifetime enhancement, access to the network with security and appropriate network selections. Routing algorithms deliberate on the performance of the network to evenly distribute load and thus enhance the lifespan of individual nodes, clustering algorithm decides on allowing the right nodes into the network for enhanced security feature, and finally the ability to analyse, predict the context of individual nodes/sensors in the network. Architecture of the proposed network includes the parameters such as decision making ability to sustain the clusters, decision on members of the clusters until the communication process is completed, local network abilities and disabilities, price, preferences of individuals, terminal and access points of the service providers. Network lifetime of the entire network is observed to be enhanced up to 91% with triple layer architecture.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126464494","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}
User Authentication plays a crucial role in smart card based systems. Multi-application smart cards are easy to use as a single smart card supports more than one application. These cards are broadly divided into single identity cards and Multi-identity cards. In this paper we have tried to provide a secure Multi-identity Multi-application Smart Card Authentication Scheme. Security is provided to user’s data by using dynamic tokens as verifiers and nested cryptography. A new token is generated after every successful authentication for next iteration. Anonymity is also provided to data servers which provides security against availability attacks. An alternate approach to store data on servers is explored which further enhances the security of the underlying system.
{"title":"A Robust Authentication System With Application Anonymity in Multiple Identity Smart Cards","authors":"Varun Prajapati, B. Gupta","doi":"10.4018/jitr.2022010107","DOIUrl":"https://doi.org/10.4018/jitr.2022010107","url":null,"abstract":"User Authentication plays a crucial role in smart card based systems. Multi-application smart cards are easy to use as a single smart card supports more than one application. These cards are broadly divided into single identity cards and Multi-identity cards. In this paper we have tried to provide a secure Multi-identity Multi-application Smart Card Authentication Scheme. Security is provided to user’s data by using dynamic tokens as verifiers and nested cryptography. A new token is generated after every successful authentication for next iteration. Anonymity is also provided to data servers which provides security against availability attacks. An alternate approach to store data on servers is explored which further enhances the security of the underlying system.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133808950","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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