In this paper, we research the probability theory and matrix transformation based technique to manage the data for processing and analysis. Clustering analysis research has a long history, over the decades, the import...In this paper, we research the probability theory and matrix transformation based technique to manage the data for processing and analysis. Clustering analysis research has a long history, over the decades, the importance and the cross characteristics with other research direction to get the affirmation of the people. The probability theory and linear algebra act as the powerful tool for analyzing and mining data. The experimental result illustrates the effectiveness. In the near future, we plan to conduct more theoretical analysis on the topic.展开更多
In this paper,we provide a new approach to data encryption using generalized inverses.Encryption is based on the implementation of weighted Moore–Penrose inverse A y MNenxmT over the nx8 constant matrix.The square He...In this paper,we provide a new approach to data encryption using generalized inverses.Encryption is based on the implementation of weighted Moore–Penrose inverse A y MNenxmT over the nx8 constant matrix.The square Hermitian positive definite matrix N8x8 p is the key.The proposed solution represents a very strong key since the number of different variants of positive definite matrices of order 8 is huge.We have provided NIST(National Institute of Standards and Technology)quality assurance tests for a random generated Hermitian matrix(a total of 10 different tests and additional analysis with approximate entropy and random digression).In the additional testing of the quality of the random matrix generated,we can conclude that the results of our analysis satisfy the defined strict requirements.This proposed MP encryption method can be applied effectively in the encryption and decryption of images in multi-party communications.In the experimental part of this paper,we give a comparison of encryption methods between machine learning methods.Machine learning algorithms could be compared by achieved results of classification concentrating on classes.In a comparative analysis,we give results of classifying of advanced encryption standard(AES)algorithm and proposed encryption method based on Moore–Penrose inverse.展开更多
Geo-data is a foundation for the prediction and assessment of ore resources, so managing and making full use of those data, including geography database, geology database, mineral deposits database, aeromagnetics data...Geo-data is a foundation for the prediction and assessment of ore resources, so managing and making full use of those data, including geography database, geology database, mineral deposits database, aeromagnetics database, gravity database, geochemistry database and remote sensing database, is very significant. We developed national important mining zone database （NIMZDB） to manage 14 national important mining zone databases to support a new round prediction of ore deposit. We found that attention should be paid to the following issues： ① data accuracy： integrity, logic consistency, attribute, spatial and time accuracy; ② management of both attribute and spatial data in the same system;③ transforming data between MapGIS and ArcGIS; ④ data sharing and security; ⑤ data searches that can query both attribute and spatial data. Accuracy of input data is guaranteed and the search, analysis and translation of data between MapGIS and ArcGIS has been made convenient via the development of a checking data module and a managing data module based on MapGIS and ArcGIS. Using AreSDE, we based data sharing on a client/server system, and attribute and spatial data are also managed in the same system.展开更多
Nowadays,one of the most important difficulties is the protection and privacy of confidential data.To address these problems,numerous organizations rely on the use of cryptographic techniques to secure data from illeg...Nowadays,one of the most important difficulties is the protection and privacy of confidential data.To address these problems,numerous organizations rely on the use of cryptographic techniques to secure data from illegal activities and assaults.Modern cryptographic ciphers use the non-linear component of block cipher to ensure the robust encryption process and lawful decoding of plain data during the decryption phase.For the designing of a secure substitution box(S-box),non-linearity(NL)which is an algebraic property of the S-box has great importance.Consequently,the main focus of cryptographers is to achieve the S-box with a high value of non-linearity.In this suggested study,an algebraic approach for the construction of 16×16 S-boxes is provided which is based on the fractional transformation Q(z)=1/α(z)^(m)+β(mod257)and finite field.This technique is only applicable for the even number exponent in the range(2-254)that are not multiples of 4.Firstly,we choose a quadratic fractional transformation,swap each missing element with repeating elements,and acquire the initial S-box.In the second stage,a special permutation of the symmetric group S256 is utilized to construct the final S-box,which has a higher NL score of 112.75 than the Advanced Encryption Standard(AES)S-box and a lower linear probability score of 0.1328.In addition,a tabular and graphical comparison of various algebraic features of the created S-box with many other S-boxes from the literature is provided which verifies that the created S-box has the ability and is good enough to withstand linear and differential attacks.From different analyses,it is ensured that the proposed S-boxes are better than as compared to the existing S-boxes.Further these S-boxes can be utilized in the security of the image data and the text data.展开更多
Intrusion detection is critical to guaranteeing the safety of the data in the network.Even though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior chara...Intrusion detection is critical to guaranteeing the safety of the data in the network.Even though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior characteristics are becoming increasingly complicated,posing significant hurdles to intrusion detection.The challenges in terms of false positives,false negatives,low detection accuracy,high running time,adversarial attacks,uncertain attacks,etc.lead to insecure Intrusion Detection System(IDS).To offset the existing challenge,the work has developed a secure Data Mining Intrusion detection system(DataMIDS)framework using Functional Perturbation(FP)feature selection and Bengio Nesterov Momentum-based Tuned Generative Adversarial Network(BNM-tGAN)attack detection technique.The data mining-based framework provides shallow learning of features and emphasizes feature engineering as well as selection.Initially,the IDS data are analyzed for missing values based on the Marginal Likelihood Fisher Information Matrix technique(MLFIMT)that identifies the relationship among the missing values and attack classes.Based on the analysis,the missing values are classified as Missing Completely at Random(MCAR),Missing at random(MAR),Missing Not at Random(MNAR),and handled according to the types.Thereafter,categorical features are handled followed by feature scaling using Absolute Median Division based Robust Scalar(AMDRS)and the Handling of the imbalanced dataset.The selection of relevant features is initiated using FP that uses‘3’Feature Selection(FS)techniques i.e.,Inverse Chi Square based Flamingo Search(ICS-FSO)wrapper method,Hyperparameter Tuned Threshold based Decision Tree(HpTT-DT)embedded method,and Xavier Normal Distribution based Relief(XavND-Relief)filter method.Finally,the selected features are trained and tested for detecting attacks using BNM-tGAN.The Experimental analysis demonstrates that the introduced DataMIDS framework produces an accurate diagnosis about the attack with low computation time.The work avoids false alarm rate of attacks and remains to be relatively robust against malicious attacks as compared to existing methods.展开更多
The opioid crisis has impacted the lives of millions of Americans.Digital technology has been applied in both research and clinical practice to mitigate this public health emergency.Blockchain technology has been impl...The opioid crisis has impacted the lives of millions of Americans.Digital technology has been applied in both research and clinical practice to mitigate this public health emergency.Blockchain technology has been implemented in healthcare and other industries outside of cryptocurrency,with few studies exploring its utility in dealing with the opioid crisis.This paper explores a novel application of blockchain technology and its features to increase uptake of medications for opioid use disorder.展开更多
文摘In this paper, we research the probability theory and matrix transformation based technique to manage the data for processing and analysis. Clustering analysis research has a long history, over the decades, the importance and the cross characteristics with other research direction to get the affirmation of the people. The probability theory and linear algebra act as the powerful tool for analyzing and mining data. The experimental result illustrates the effectiveness. In the near future, we plan to conduct more theoretical analysis on the topic.
基金the support of Network Communication Technology(NCT)Research Groups,FTSM,UKM in providing facilities for this research.This paper is supported under the Dana Impak Perdana UKM DIP-2018-040 and Fundamental Research Grant Scheme FRGS/1/2018/TK04/UKM/02/7.
文摘In this paper,we provide a new approach to data encryption using generalized inverses.Encryption is based on the implementation of weighted Moore–Penrose inverse A y MNenxmT over the nx8 constant matrix.The square Hermitian positive definite matrix N8x8 p is the key.The proposed solution represents a very strong key since the number of different variants of positive definite matrices of order 8 is huge.We have provided NIST(National Institute of Standards and Technology)quality assurance tests for a random generated Hermitian matrix(a total of 10 different tests and additional analysis with approximate entropy and random digression).In the additional testing of the quality of the random matrix generated,we can conclude that the results of our analysis satisfy the defined strict requirements.This proposed MP encryption method can be applied effectively in the encryption and decryption of images in multi-party communications.In the experimental part of this paper,we give a comparison of encryption methods between machine learning methods.Machine learning algorithms could be compared by achieved results of classification concentrating on classes.In a comparative analysis,we give results of classifying of advanced encryption standard(AES)algorithm and proposed encryption method based on Moore–Penrose inverse.
基金This paper is financially supported by the National I mportant MiningZone Database ( No .200210000004)Prediction and Assessment ofMineral Resources and Social Service (No .1212010331402) .
文摘Geo-data is a foundation for the prediction and assessment of ore resources, so managing and making full use of those data, including geography database, geology database, mineral deposits database, aeromagnetics database, gravity database, geochemistry database and remote sensing database, is very significant. We developed national important mining zone database （NIMZDB） to manage 14 national important mining zone databases to support a new round prediction of ore deposit. We found that attention should be paid to the following issues： ① data accuracy： integrity, logic consistency, attribute, spatial and time accuracy; ② management of both attribute and spatial data in the same system;③ transforming data between MapGIS and ArcGIS; ④ data sharing and security; ⑤ data searches that can query both attribute and spatial data. Accuracy of input data is guaranteed and the search, analysis and translation of data between MapGIS and ArcGIS has been made convenient via the development of a checking data module and a managing data module based on MapGIS and ArcGIS. Using AreSDE, we based data sharing on a client/server system, and attribute and spatial data are also managed in the same system.
基金The authors received the funding for this study from King Saud University,Riyadh,Saudi Arabia under the research supporting project Number RSP 2023R167.Sameh Askar received this grant from King Saud University。
文摘Nowadays,one of the most important difficulties is the protection and privacy of confidential data.To address these problems,numerous organizations rely on the use of cryptographic techniques to secure data from illegal activities and assaults.Modern cryptographic ciphers use the non-linear component of block cipher to ensure the robust encryption process and lawful decoding of plain data during the decryption phase.For the designing of a secure substitution box(S-box),non-linearity(NL)which is an algebraic property of the S-box has great importance.Consequently,the main focus of cryptographers is to achieve the S-box with a high value of non-linearity.In this suggested study,an algebraic approach for the construction of 16×16 S-boxes is provided which is based on the fractional transformation Q(z)=1/α(z)^(m)+β(mod257)and finite field.This technique is only applicable for the even number exponent in the range(2-254)that are not multiples of 4.Firstly,we choose a quadratic fractional transformation,swap each missing element with repeating elements,and acquire the initial S-box.In the second stage,a special permutation of the symmetric group S256 is utilized to construct the final S-box,which has a higher NL score of 112.75 than the Advanced Encryption Standard(AES)S-box and a lower linear probability score of 0.1328.In addition,a tabular and graphical comparison of various algebraic features of the created S-box with many other S-boxes from the literature is provided which verifies that the created S-box has the ability and is good enough to withstand linear and differential attacks.From different analyses,it is ensured that the proposed S-boxes are better than as compared to the existing S-boxes.Further these S-boxes can be utilized in the security of the image data and the text data.
文摘Intrusion detection is critical to guaranteeing the safety of the data in the network.Even though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior characteristics are becoming increasingly complicated,posing significant hurdles to intrusion detection.The challenges in terms of false positives,false negatives,low detection accuracy,high running time,adversarial attacks,uncertain attacks,etc.lead to insecure Intrusion Detection System(IDS).To offset the existing challenge,the work has developed a secure Data Mining Intrusion detection system(DataMIDS)framework using Functional Perturbation(FP)feature selection and Bengio Nesterov Momentum-based Tuned Generative Adversarial Network(BNM-tGAN)attack detection technique.The data mining-based framework provides shallow learning of features and emphasizes feature engineering as well as selection.Initially,the IDS data are analyzed for missing values based on the Marginal Likelihood Fisher Information Matrix technique(MLFIMT)that identifies the relationship among the missing values and attack classes.Based on the analysis,the missing values are classified as Missing Completely at Random(MCAR),Missing at random(MAR),Missing Not at Random(MNAR),and handled according to the types.Thereafter,categorical features are handled followed by feature scaling using Absolute Median Division based Robust Scalar(AMDRS)and the Handling of the imbalanced dataset.The selection of relevant features is initiated using FP that uses‘3’Feature Selection(FS)techniques i.e.,Inverse Chi Square based Flamingo Search(ICS-FSO)wrapper method,Hyperparameter Tuned Threshold based Decision Tree(HpTT-DT)embedded method,and Xavier Normal Distribution based Relief(XavND-Relief)filter method.Finally,the selected features are trained and tested for detecting attacks using BNM-tGAN.The Experimental analysis demonstrates that the introduced DataMIDS framework produces an accurate diagnosis about the attack with low computation time.The work avoids false alarm rate of attacks and remains to be relatively robust against malicious attacks as compared to existing methods.
基金This work was supported by the National Center for Complementary and Integrative Health under Grant 4R33AT010606-03 and National Institute on Drug Abuse.
文摘The opioid crisis has impacted the lives of millions of Americans.Digital technology has been applied in both research and clinical practice to mitigate this public health emergency.Blockchain technology has been implemented in healthcare and other industries outside of cryptocurrency,with few studies exploring its utility in dealing with the opioid crisis.This paper explores a novel application of blockchain technology and its features to increase uptake of medications for opioid use disorder.
