Cloud computing is a collection of distributed storage Network which can provide various services and store the data in the efficient manner.The advantages of cloud computing is its remote access where data can access...Cloud computing is a collection of distributed storage Network which can provide various services and store the data in the efficient manner.The advantages of cloud computing is its remote access where data can accessed in real time using Remote Method Innovation(RMI).The problem of data security in cloud environment is a major concern since the data can be accessed by any time by any user.Due to the lack of providing the efficient security the cloud computing they fail to achieve higher performance in providing the efficient service.To improve the performance in data security,the block chains are used for securing the data in the cloud environment.However,the traditional block chain technique are not suitable to provide efficient security to the cloud data stored in the cloud.In this paper,an efficient user centric block level Attribute Based Encryption(UCBL-ABE)scheme is presented to provide the efficient security of cloud data in cloud environment.The proposed approach performs data transaction by employing the block chain.The proposed system provides efficient privacy with access control to the user access according to the behavior of cloud user using Data Level Access Trust(DLAT).Based on DLAT,the user access has been restricted in the cloud environment.The proposed protocol is implemented in real time using Java programming language and uses IBM cloud.The implementation results justifies that the proposed system can able to provide efficient security to the data present in and cloud and also enhances the cloud performance.展开更多
Mobile devices and social networks provide communication opportunities among the young generation,which increases vulnerability and cybercrimes activities.A recent survey reports that cyberbullying and cyberstalking c...Mobile devices and social networks provide communication opportunities among the young generation,which increases vulnerability and cybercrimes activities.A recent survey reports that cyberbullying and cyberstalking constitute a developing issue among youngsters.This paper focuses on cyberbullying detection in mobile phone text by retrieving with the help of an oxygen forensics toolkit.We describe the data collection using forensics technique and a corpus of suspicious activities like cyberbullying annotation from mobile phones and carry out a sequence of binary classification experiments to determine cyberbullying detection.We use forensics techniques,Machine Learning(ML),and Deep Learning(DL)algorithms to exploit suspicious patterns to help the forensics investigation where every evidence contributes to the case.Experiments on a real-time dataset reveal better results for the detection of cyberbullying content.The Random Forest in ML approach produces 87%of accuracy without SMOTE technique,whereas the value of F1Score produces a good result with SMOTE technique.The LSTM has 92%of validation accuracy in the DL algorithm compared with Dense and BiLSTM algorithms.展开更多
文摘Cloud computing is a collection of distributed storage Network which can provide various services and store the data in the efficient manner.The advantages of cloud computing is its remote access where data can accessed in real time using Remote Method Innovation(RMI).The problem of data security in cloud environment is a major concern since the data can be accessed by any time by any user.Due to the lack of providing the efficient security the cloud computing they fail to achieve higher performance in providing the efficient service.To improve the performance in data security,the block chains are used for securing the data in the cloud environment.However,the traditional block chain technique are not suitable to provide efficient security to the cloud data stored in the cloud.In this paper,an efficient user centric block level Attribute Based Encryption(UCBL-ABE)scheme is presented to provide the efficient security of cloud data in cloud environment.The proposed approach performs data transaction by employing the block chain.The proposed system provides efficient privacy with access control to the user access according to the behavior of cloud user using Data Level Access Trust(DLAT).Based on DLAT,the user access has been restricted in the cloud environment.The proposed protocol is implemented in real time using Java programming language and uses IBM cloud.The implementation results justifies that the proposed system can able to provide efficient security to the data present in and cloud and also enhances the cloud performance.
文摘Mobile devices and social networks provide communication opportunities among the young generation,which increases vulnerability and cybercrimes activities.A recent survey reports that cyberbullying and cyberstalking constitute a developing issue among youngsters.This paper focuses on cyberbullying detection in mobile phone text by retrieving with the help of an oxygen forensics toolkit.We describe the data collection using forensics technique and a corpus of suspicious activities like cyberbullying annotation from mobile phones and carry out a sequence of binary classification experiments to determine cyberbullying detection.We use forensics techniques,Machine Learning(ML),and Deep Learning(DL)algorithms to exploit suspicious patterns to help the forensics investigation where every evidence contributes to the case.Experiments on a real-time dataset reveal better results for the detection of cyberbullying content.The Random Forest in ML approach produces 87%of accuracy without SMOTE technique,whereas the value of F1Score produces a good result with SMOTE technique.The LSTM has 92%of validation accuracy in the DL algorithm compared with Dense and BiLSTM algorithms.
