Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information quickly.The use of cloud computing has realized the rapid increase ...Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information quickly.The use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing efficiently.This cloud is nowadays highly affected by internal threats of the user.Sensitive applications such as banking,hospital,and business are more likely affected by real user threats.An intruder is presented as a user and set as a member of the network.After becoming an insider in the network,they will try to attack or steal sensitive data during information sharing or conversation.The major issue in today's technological development is identifying the insider threat in the cloud network.When data are lost,compromising cloud users is difficult.Privacy and security are not ensured,and then,the usage of the cloud is not trusted.Several solutions are available for the external security of the cloud network.However,insider or internal threats need to be addressed.In this research work,we focus on a solution for identifying an insider attack using the artificial intelligence technique.An insider attack is possible by using nodes of weak users’systems.They will log in using a weak user id,connect to a network,and pretend to be a trusted node.Then,they can easily attack and hack information as an insider,and identifying them is very difficult.These types of attacks need intelligent solutions.A machine learning approach is widely used for security issues.To date,the existing lags can classify the attackers accurately.This information hijacking process is very absurd,which motivates young researchers to provide a solution for internal threats.In our proposed work,we track the attackers using a user interaction behavior pattern and deep learning technique.The usage of mouse movements and clicks and keystrokes of the real user is stored in a database.The deep belief neural network is designed using a restricted Boltzma展开更多
Cloud computing becomes an important application development platform for processing user data with high security.Service providers are accustomed to providing storage centers outside the trusted location preferred by...Cloud computing becomes an important application development platform for processing user data with high security.Service providers are accustomed to providing storage centers outside the trusted location preferred by the data owner.Thus,ensuring the security and confidentiality of the data while processing in the centralized network is very difficult.The secured key transmission between the sender and the receiver in the network is a huge challenge in managing most of the sensitive data transmission among the cloud network.Intruders are very active over the network like real authenticated user to hack the personal sensitive data,such as bank balance,health data,personal data,and confidential documents over the cloud network.In this research,a secured key agreement between the sender and the receiver using Kerberos authentication protocol with fingerprint is proposed to ensure security in M-Healthcare.Conditions of patients are monitored using wireless sensor devices and are then transferred to the server.Kerberos protocol helps in avoiding unnecessary communication of authenticated data over the cloud network.Biometric security process is a procedure with the best security in most of the authentication field.Trust node is responsible in carrying data packets from the sender to the receiver in the cloud network.The Kerberos protocol is used in trust node to ensure security.Secured communication between the local health center and the healthcare server is ensured by using a fingerprint feature called minutiae form,which refers to the fingerprint image of both sender and receiver.The computational and communicational cost of the proposed system is lesser when compared with other existing authentication methods.展开更多
Purpose-We propose a Machine Learning(ML)approach that will be trained from the available financial data and is able to gain the trends over the data and then uses the acquired knowledge for a more accurate forecastin...Purpose-We propose a Machine Learning(ML)approach that will be trained from the available financial data and is able to gain the trends over the data and then uses the acquired knowledge for a more accurate forecasting of financial series.This work will provide a more precise results when weighed up to aged financial series forecasting algorithms.The LSTM Classic will be used to forecast the momentum of the Financial Series Index and also applied to its commodities.The network will be trained and evaluated for accuracy with various sizes of data sets,i.e.weekly historical data of MCX,GOLD,COPPER and the results will be calculated.Design/methodology/approach-Desirable LSTM model for script price forecasting from the perspective of minimizing MSE.The approach which we have followed is shown below.(1)Acquire the Dataset.(2)Define your training and testing columns in the dataset.(3)Transform the input value using scalar.(4)Define the custom loss function.(5)Build and Compile the model.(6)Visualise the improvements in results.Findings-Financial series is one of the very aged techniques where a commerce person would commerce financial scripts,make business and earn some wealth from these companies that vend a part of their business on trading manifesto.Forecasting financial script prices is complex tasks that consider extensive human-computer interaction.Due to the correlated nature of financial series prices,conventional batch processing methods like an artificial neural network,convolutional neural network,cannot be utilised efficiently for financial market analysis.We propose an online learning algorithm that utilises an upgraded of recurrent neural networks called long short-term memory Classic(LSTM).The LSTM Classic is quite different from normal LSTM as it has customised loss function in it.This LSTM Classic avoids long-term dependence on its metrics issues because of its unique internal storage unit structure,and it helps forecast financial time series.Financial Series Index is the combination of various commodi展开更多
文摘Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information quickly.The use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing efficiently.This cloud is nowadays highly affected by internal threats of the user.Sensitive applications such as banking,hospital,and business are more likely affected by real user threats.An intruder is presented as a user and set as a member of the network.After becoming an insider in the network,they will try to attack or steal sensitive data during information sharing or conversation.The major issue in today's technological development is identifying the insider threat in the cloud network.When data are lost,compromising cloud users is difficult.Privacy and security are not ensured,and then,the usage of the cloud is not trusted.Several solutions are available for the external security of the cloud network.However,insider or internal threats need to be addressed.In this research work,we focus on a solution for identifying an insider attack using the artificial intelligence technique.An insider attack is possible by using nodes of weak users’systems.They will log in using a weak user id,connect to a network,and pretend to be a trusted node.Then,they can easily attack and hack information as an insider,and identifying them is very difficult.These types of attacks need intelligent solutions.A machine learning approach is widely used for security issues.To date,the existing lags can classify the attackers accurately.This information hijacking process is very absurd,which motivates young researchers to provide a solution for internal threats.In our proposed work,we track the attackers using a user interaction behavior pattern and deep learning technique.The usage of mouse movements and clicks and keystrokes of the real user is stored in a database.The deep belief neural network is designed using a restricted Boltzma
文摘Cloud computing becomes an important application development platform for processing user data with high security.Service providers are accustomed to providing storage centers outside the trusted location preferred by the data owner.Thus,ensuring the security and confidentiality of the data while processing in the centralized network is very difficult.The secured key transmission between the sender and the receiver in the network is a huge challenge in managing most of the sensitive data transmission among the cloud network.Intruders are very active over the network like real authenticated user to hack the personal sensitive data,such as bank balance,health data,personal data,and confidential documents over the cloud network.In this research,a secured key agreement between the sender and the receiver using Kerberos authentication protocol with fingerprint is proposed to ensure security in M-Healthcare.Conditions of patients are monitored using wireless sensor devices and are then transferred to the server.Kerberos protocol helps in avoiding unnecessary communication of authenticated data over the cloud network.Biometric security process is a procedure with the best security in most of the authentication field.Trust node is responsible in carrying data packets from the sender to the receiver in the cloud network.The Kerberos protocol is used in trust node to ensure security.Secured communication between the local health center and the healthcare server is ensured by using a fingerprint feature called minutiae form,which refers to the fingerprint image of both sender and receiver.The computational and communicational cost of the proposed system is lesser when compared with other existing authentication methods.
文摘Purpose-We propose a Machine Learning(ML)approach that will be trained from the available financial data and is able to gain the trends over the data and then uses the acquired knowledge for a more accurate forecasting of financial series.This work will provide a more precise results when weighed up to aged financial series forecasting algorithms.The LSTM Classic will be used to forecast the momentum of the Financial Series Index and also applied to its commodities.The network will be trained and evaluated for accuracy with various sizes of data sets,i.e.weekly historical data of MCX,GOLD,COPPER and the results will be calculated.Design/methodology/approach-Desirable LSTM model for script price forecasting from the perspective of minimizing MSE.The approach which we have followed is shown below.(1)Acquire the Dataset.(2)Define your training and testing columns in the dataset.(3)Transform the input value using scalar.(4)Define the custom loss function.(5)Build and Compile the model.(6)Visualise the improvements in results.Findings-Financial series is one of the very aged techniques where a commerce person would commerce financial scripts,make business and earn some wealth from these companies that vend a part of their business on trading manifesto.Forecasting financial script prices is complex tasks that consider extensive human-computer interaction.Due to the correlated nature of financial series prices,conventional batch processing methods like an artificial neural network,convolutional neural network,cannot be utilised efficiently for financial market analysis.We propose an online learning algorithm that utilises an upgraded of recurrent neural networks called long short-term memory Classic(LSTM).The LSTM Classic is quite different from normal LSTM as it has customised loss function in it.This LSTM Classic avoids long-term dependence on its metrics issues because of its unique internal storage unit structure,and it helps forecast financial time series.Financial Series Index is the combination of various commodi
