In the ancient block Hill cipher, the cipher text is obtained by multiplying the blocks of the plain text with the key matrix. To strengthen the keymatrix, a double guard Hill cipher was proposed with two key matrices...In the ancient block Hill cipher, the cipher text is obtained by multiplying the blocks of the plain text with the key matrix. To strengthen the keymatrix, a double guard Hill cipher was proposed with two key matrices, a private key matrix and its modified key matrix along with permutation. In the ancient block Hill cipher, the cipher text is obtained by multiplying the blocks of the plain text with the key matrix. To strengthen the key matrix, a double guard Hill cipher was proposed with two key matrices, a private key matrix and its modified key matrix along with permutation. In this paper a novel modification is performed to the double guard Hill cipher in order to reduce the number of calculation to obtain the cipher text by using non-square matrices. This modified double guard Hill cipher uses a non-square matrix of order (p × q) as its private keymatrix.展开更多
With the emergence of cloud technologies,the services of healthcare systems have grown.Simultaneously,machine learning systems have become important tools for developing matured and decision-making computer applicatio...With the emergence of cloud technologies,the services of healthcare systems have grown.Simultaneously,machine learning systems have become important tools for developing matured and decision-making computer applications.Both cloud computing and machine learning technologies have contributed significantly to the success of healthcare services.However,in some areas,these technologies are needed to provide and decide the next course of action for patients suffering from diabetic kidney disease(DKD)while ensuring privacy preservation of the medical data.To address the cloud data privacy problem,we proposed a DKD prediction module in a framework using cloud computing services and a data control scheme.This framework can provide improved and early treatment before end-stage renal failure.For prediction purposes,we implemented the following machine learning algorithms:support vector machine(SVM),random forest(RF),decision tree(DT),naïve Bayes(NB),deep learning(DL),and k nearest neighbor(KNN).These classification techniques combined with the cloud computing services significantly improved the decision making in the progress of DKD patients.We applied these classifiers to the UCI Machine Learning Repository for chronic kidney disease using various clinical features,which are categorized as single,combination of selected features,and all features.During single clinical feature experiments,machine learning classifiers SVM,RF,and KNN outperformed the remaining classification techniques,whereas in combined clinical feature experiments,the maximum accuracy was achieved for the combination of DL and RF.All the feature experiments presented increased accuracy and increased F-measure metrics from SVM,DL,and RF.展开更多
Exchange of data in networks necessitates provision of security and confidentiality.Most networks compromised by intruders are those where the exchange of data is at high risk.The main objective of this paper is to pr...Exchange of data in networks necessitates provision of security and confidentiality.Most networks compromised by intruders are those where the exchange of data is at high risk.The main objective of this paper is to present a solution for secure exchange of attack signatures between the nodes of a distributed network.Malicious activities are monitored and detected by the Intrusion Detection System(IDS)that operates with nodes connected to a distributed network.The IDS operates in two phases,where the first phase consists of detection of anomaly attacks using an ensemble of classifiers such as Random forest,Convolutional neural network,and XGBoost along with genetic algorithm to improve the performance of IDS.The novel attacks detected in this phase are converted into signatures and exchanged further through the network using the blockchain framework in the second phase.This phase uses the cryptosystem as part of the blockchain to store data and secure it at a higher level.The blockchain is implemented using the Hyperledger Fabric v1.0 and v2.0,to create a prototype for secure signature transfer.It exchanges signatures in a much more secured manner using the blockchain architecture when implemented with version 2.0 of Hyperl-edger Fabric.The performance of the proposed blockchain system is evaluated on UNSW NB15 dataset.Blockchain performance has been evaluated in terms of execution time,average latency,throughput and transaction processing time.Experimental evidence of the proposed IDS system demonstrates improved performance with accuracy,detection rate and false alarm rate(FAR)as key parameters used.Accuracy and detection rate increase by 2%and 3%respectively whereas FAR reduces by 1.7%.展开更多
As more medical data become digitalized,machine learning is regarded as a promising tool for constructing medical decision support systems.Even with vast medical data volumes,machine learning is still not fully exploi...As more medical data become digitalized,machine learning is regarded as a promising tool for constructing medical decision support systems.Even with vast medical data volumes,machine learning is still not fully exploiting its potential because the data usually sits in data silos,and privacy and security regulations restrict their access and use.To address these issues,we built a secured and explainable machine learning framework,called explainable federated XGBoost(EXPERTS),which can share valuable information among different medical institutions to improve the learning results without sharing the patients’ data.It also reveals how the machine makes a decision through eigenvalues to offer a more insightful answer to medical professionals.To study the performance,we evaluate our approach by real-world datasets,and our approach outperforms the benchmark algorithms under both federated learning and non-federated learning frameworks.展开更多
The rise of the digital economy and the comfort of accessing by way of user mobile devices expedite human endeavors in financial transactions over the Virtual Private Network(VPN)backbone.This prominent application of...The rise of the digital economy and the comfort of accessing by way of user mobile devices expedite human endeavors in financial transactions over the Virtual Private Network(VPN)backbone.This prominent application of VPN evades the hurdles involved in physical money exchange.The VPN acts as a gateway for the authorized user in accessing the banking server to provide mutual authentication between the user and the server.The security in the cloud authentication server remains vulnerable to the results of threat in JP Morgan Data breach in 2014,Capital One Data Breach in 2019,and manymore cloud server attacks over and over again.These attacks necessitate the demand for a strong framework for authentication to secure from any class of threat.This research paper,propose a framework with a base of EllipticalCurve Cryptography(ECC)to performsecure financial transactions throughVirtual PrivateNetwork(VPN)by implementing strongMulti-Factor Authentication(MFA)using authentication credentials and biometric identity.The research results prove that the proposed model is to be an ideal scheme for real-time implementation.The security analysis reports that the proposed model exhibits high level of security with a minimal response time of 12 s on an average of 1000 users.展开更多
Wireless sensor networks(WSNs)are created and affect our daily lives.You can find applications in various fields such as health,accident,life,manufacturing,production management,network management and many other field...Wireless sensor networks(WSNs)are created and affect our daily lives.You can find applications in various fields such as health,accident,life,manufacturing,production management,network management and many other fields.WSN now connects to the Internet of Things,connects the sensor to the Internet,and then uses it for collaboration and collaboration.However,when WSN is part of the internet we need to be able to study and analyze related terms.In this article,we’re going to look at different ways to getWSN online and identify the challenges that address in future as well.展开更多
Together with a huge number of other countries, Germany signed the Paris Agreements in 2015 to prevent global temperature increase above 2℃. Within this agreement, all countries defined their own national contributio...Together with a huge number of other countries, Germany signed the Paris Agreements in 2015 to prevent global temperature increase above 2℃. Within this agreement, all countries defined their own national contributions to CO2 reduction. Since that, it was visible that CO2 emissions in Germany decreased, but not so fast than proposed in this German nationally determined contribution to the Paris Agreement. Due to increasing traffic, CO2 emissions from this mobility sector increased and CO2 emission from German power generation is nearly constant for the past 20 years, even a renewable generation capacity of 112 GW was built up in 2017, which is much higher than the peak load of 84 GW in Germany. That is why the German National Government has implemented a commission (often called "The German Coal Commission") to propose a time line: how Germany can move out of coal-fired power stations. This "Coal Commission" started its work in the late spring of 2018 and handed over its final report with 336 pages to the government on January 26th, 2019. Within this report the following proposals were made:①Until 2022: Due to a former decision of the German Government, the actual remaining nuclear power generation capacity of about 10 GW has to be switched off in 2022. Besides, the "Coal Commission" proposed to switch off additionally in total 12.5 GW of both, hard coal and lignite-fired power plants, so that Germany should reduce its conventional generation capacity by 22.5 GW in 2022.②Until 2030: Another 13 GW of German hard coal or lignite-fired power plants should be switched off.③Until 2038:The final 17 GW of German hard coal or lignitefired power plants should be switched off until 2038 latest. Unfortunately the "Coal Commission" has not investigated the relevant technical parameter to ensure a secured electric power supply, based on German's own national resources. Because German Energy Revolution mainly is based on wind energy and photovoltaic, this paper will describe the negligible contribution of these sources展开更多
文摘In the ancient block Hill cipher, the cipher text is obtained by multiplying the blocks of the plain text with the key matrix. To strengthen the keymatrix, a double guard Hill cipher was proposed with two key matrices, a private key matrix and its modified key matrix along with permutation. In the ancient block Hill cipher, the cipher text is obtained by multiplying the blocks of the plain text with the key matrix. To strengthen the key matrix, a double guard Hill cipher was proposed with two key matrices, a private key matrix and its modified key matrix along with permutation. In this paper a novel modification is performed to the double guard Hill cipher in order to reduce the number of calculation to obtain the cipher text by using non-square matrices. This modified double guard Hill cipher uses a non-square matrix of order (p × q) as its private keymatrix.
文摘With the emergence of cloud technologies,the services of healthcare systems have grown.Simultaneously,machine learning systems have become important tools for developing matured and decision-making computer applications.Both cloud computing and machine learning technologies have contributed significantly to the success of healthcare services.However,in some areas,these technologies are needed to provide and decide the next course of action for patients suffering from diabetic kidney disease(DKD)while ensuring privacy preservation of the medical data.To address the cloud data privacy problem,we proposed a DKD prediction module in a framework using cloud computing services and a data control scheme.This framework can provide improved and early treatment before end-stage renal failure.For prediction purposes,we implemented the following machine learning algorithms:support vector machine(SVM),random forest(RF),decision tree(DT),naïve Bayes(NB),deep learning(DL),and k nearest neighbor(KNN).These classification techniques combined with the cloud computing services significantly improved the decision making in the progress of DKD patients.We applied these classifiers to the UCI Machine Learning Repository for chronic kidney disease using various clinical features,which are categorized as single,combination of selected features,and all features.During single clinical feature experiments,machine learning classifiers SVM,RF,and KNN outperformed the remaining classification techniques,whereas in combined clinical feature experiments,the maximum accuracy was achieved for the combination of DL and RF.All the feature experiments presented increased accuracy and increased F-measure metrics from SVM,DL,and RF.
文摘Exchange of data in networks necessitates provision of security and confidentiality.Most networks compromised by intruders are those where the exchange of data is at high risk.The main objective of this paper is to present a solution for secure exchange of attack signatures between the nodes of a distributed network.Malicious activities are monitored and detected by the Intrusion Detection System(IDS)that operates with nodes connected to a distributed network.The IDS operates in two phases,where the first phase consists of detection of anomaly attacks using an ensemble of classifiers such as Random forest,Convolutional neural network,and XGBoost along with genetic algorithm to improve the performance of IDS.The novel attacks detected in this phase are converted into signatures and exchanged further through the network using the blockchain framework in the second phase.This phase uses the cryptosystem as part of the blockchain to store data and secure it at a higher level.The blockchain is implemented using the Hyperledger Fabric v1.0 and v2.0,to create a prototype for secure signature transfer.It exchanges signatures in a much more secured manner using the blockchain architecture when implemented with version 2.0 of Hyperl-edger Fabric.The performance of the proposed blockchain system is evaluated on UNSW NB15 dataset.Blockchain performance has been evaluated in terms of execution time,average latency,throughput and transaction processing time.Experimental evidence of the proposed IDS system demonstrates improved performance with accuracy,detection rate and false alarm rate(FAR)as key parameters used.Accuracy and detection rate increase by 2%and 3%respectively whereas FAR reduces by 1.7%.
文摘As more medical data become digitalized,machine learning is regarded as a promising tool for constructing medical decision support systems.Even with vast medical data volumes,machine learning is still not fully exploiting its potential because the data usually sits in data silos,and privacy and security regulations restrict their access and use.To address these issues,we built a secured and explainable machine learning framework,called explainable federated XGBoost(EXPERTS),which can share valuable information among different medical institutions to improve the learning results without sharing the patients’ data.It also reveals how the machine makes a decision through eigenvalues to offer a more insightful answer to medical professionals.To study the performance,we evaluate our approach by real-world datasets,and our approach outperforms the benchmark algorithms under both federated learning and non-federated learning frameworks.
文摘The rise of the digital economy and the comfort of accessing by way of user mobile devices expedite human endeavors in financial transactions over the Virtual Private Network(VPN)backbone.This prominent application of VPN evades the hurdles involved in physical money exchange.The VPN acts as a gateway for the authorized user in accessing the banking server to provide mutual authentication between the user and the server.The security in the cloud authentication server remains vulnerable to the results of threat in JP Morgan Data breach in 2014,Capital One Data Breach in 2019,and manymore cloud server attacks over and over again.These attacks necessitate the demand for a strong framework for authentication to secure from any class of threat.This research paper,propose a framework with a base of EllipticalCurve Cryptography(ECC)to performsecure financial transactions throughVirtual PrivateNetwork(VPN)by implementing strongMulti-Factor Authentication(MFA)using authentication credentials and biometric identity.The research results prove that the proposed model is to be an ideal scheme for real-time implementation.The security analysis reports that the proposed model exhibits high level of security with a minimal response time of 12 s on an average of 1000 users.
文摘Wireless sensor networks(WSNs)are created and affect our daily lives.You can find applications in various fields such as health,accident,life,manufacturing,production management,network management and many other fields.WSN now connects to the Internet of Things,connects the sensor to the Internet,and then uses it for collaboration and collaboration.However,when WSN is part of the internet we need to be able to study and analyze related terms.In this article,we’re going to look at different ways to getWSN online and identify the challenges that address in future as well.
文摘Together with a huge number of other countries, Germany signed the Paris Agreements in 2015 to prevent global temperature increase above 2℃. Within this agreement, all countries defined their own national contributions to CO2 reduction. Since that, it was visible that CO2 emissions in Germany decreased, but not so fast than proposed in this German nationally determined contribution to the Paris Agreement. Due to increasing traffic, CO2 emissions from this mobility sector increased and CO2 emission from German power generation is nearly constant for the past 20 years, even a renewable generation capacity of 112 GW was built up in 2017, which is much higher than the peak load of 84 GW in Germany. That is why the German National Government has implemented a commission (often called "The German Coal Commission") to propose a time line: how Germany can move out of coal-fired power stations. This "Coal Commission" started its work in the late spring of 2018 and handed over its final report with 336 pages to the government on January 26th, 2019. Within this report the following proposals were made:①Until 2022: Due to a former decision of the German Government, the actual remaining nuclear power generation capacity of about 10 GW has to be switched off in 2022. Besides, the "Coal Commission" proposed to switch off additionally in total 12.5 GW of both, hard coal and lignite-fired power plants, so that Germany should reduce its conventional generation capacity by 22.5 GW in 2022.②Until 2030: Another 13 GW of German hard coal or lignite-fired power plants should be switched off.③Until 2038:The final 17 GW of German hard coal or lignitefired power plants should be switched off until 2038 latest. Unfortunately the "Coal Commission" has not investigated the relevant technical parameter to ensure a secured electric power supply, based on German's own national resources. Because German Energy Revolution mainly is based on wind energy and photovoltaic, this paper will describe the negligible contribution of these sources
