Decision Tree-Based Sensitive Information Identification and Encrypted Transmission System

Liu, Shuang; Yang, Ziheng; Li, Yi; Wang, Shuiqing

doi:10.3390/e22020192

Cited by 10 publications

(13 citation statements)

References 10 publications

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“…Then, it can split the node based on the feature that has the largest information gain. The entropy utilizes the expected information to detect if the set needs to be divided into multiple classes or not [ 28 ]. The information index of symbol x i is formulated as follows:

where P ( x i ) represents the probability of the selected category.…”

Section: Machine Learning Principlesmentioning

confidence: 99%

“…After the calculation of the entropy probability of the estimated data, the empirical entropy that corresponds to this probability is formulated as follows:

where c is the maximum integer limit of the dataset Y , n i is the size of Y , and

. Then, the uncertainty of variable Y under the information of known variable X can be represented by the conditioned entropy as follows [ 28 ]:

…”

Section: Machine Learning Principlesmentioning

confidence: 99%

See 1 more Smart Citation

Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters

Elsisi

Mahmoud

Lehtonen

et al. 2021

Sensors

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The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters’ data. The data monitoring is carried based on the industrial digital twins’ platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0.

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where P ( x i ) represents the probability of the selected category.…”

Section: Machine Learning Principlesmentioning

confidence: 99%

“…After the calculation of the entropy probability of the estimated data, the empirical entropy that corresponds to this probability is formulated as follows:

where c is the maximum integer limit of the dataset Y , n i is the size of Y , and

. Then, the uncertainty of variable Y under the information of known variable X can be represented by the conditioned entropy as follows [ 28 ]:

…”

Section: Machine Learning Principlesmentioning

confidence: 99%

Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters

Elsisi

Mahmoud

Lehtonen

et al. 2021

Sensors

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show abstract

“…A decision tree-based encryption transmission system and the system recognition system was proposed by Liu et al [20] employed to protect the sensitive information. Here, Maximum depth, number of components, cross-validation error, recall, precision are the performance measures employed in this approach.…”

Section: Related Workmentioning

confidence: 99%

“…From equation (20), the maximum number of iterations and the current iterations are represented by l and M T respectively.…”

Section: Exponential Step Sizementioning

confidence: 99%

“…In the encryption module, the subkeys storage modules read the thirty two wheel keys and participate in the iterative operations of the encryption modules encryption. The sub chipper text storage block rk_ram_en stores the 32 encrypted sub cipher texts and as a result it read the very last four round of the sub chipper text thereby reversing them into final chipper text data [20].…”

Section: Encryption Modulementioning

confidence: 99%

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EMFO based Supervision Kernel Entropy Component Analysis for High Speed Encrypted Transmission Model

Skg¹

2021

Preprint

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Recently, Encryption technology plays a vital role in providing secure information. In the multimedia sector, several images and posts are broadcasted via the internet on daily basis; in which the images are transmitted on social media and are subjected to several security attacks. Therefore it is necessary to protect the images from illegal or forbidden access. This paper aims in developing a novel SKECA-EMFO based encrypted transmission system. Here, an effective encryption transmission system is designed using a Bayes minimum risk classifier to secure the sensitive information during the transmission processes. In addition to this, the SM4 encryption algorithm is employed to perform high speed encrypted transmission as well as to achieve intelligent recognition. The novel SKECA-EMFO approach is employed in demonstrating facial expression recognition thereby obtaining an optimal feature set. Finally, seven benchmark functions are utilized to examine and evaluate the effectiveness of the newly developed proposed approach. The comparative analysis is carried out with few approaches and the results reveal that the proposed approach provides better performances when compared with other approaches.

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Enhanced moth flame optimization based Supervision Kernel Entropy Component Analysis for high‐speed encrypted transmission model

Babu¹,

Kumar²

2021

Int J Communication

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Decision Tree-Based Sensitive Information Identification and Encrypted Transmission System

Cited by 10 publications

References 10 publications

Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters

Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters

EMFO based Supervision Kernel Entropy Component Analysis for High Speed Encrypted Transmission Model

Enhanced moth flame optimization based Supervision Kernel Entropy Component Analysis for high‐speed encrypted transmission model

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