Intelligent security system detects the hidden objects in the smart grid

Altaher, Ammar Wisam; Hussein, Abdullah Hasan

doi:10.11591/ijeecs.v19.i1.pp188-195

Cited by 2 publications

(3 citation statements)

References 19 publications

(21 reference statements)

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“…T is a pixel average-gray value. The computation of information entropy is described by (5) to a pair of values (g, t) [28].…”

Section: Pixel Entropy Feature Extraction Using Two Dimensions' Maximum Entropy Threshold Methodsmentioning

confidence: 99%

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The security of RC4 algorithm using keys generation depending on user's retina

Salih

Mahdawi

2021

IJEECS

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Digital technologies grow more rapidly; information security threats are becoming increasingly dangerous. Advanced and various cyber-attacks and security threats, like targeted emails, and information exploitation, pose a critical threat that basically undermines our trust in the digital society. Rivest cipher 4 (RC4) algorithm is a significant cipher of a stream that could be utilized with protocols of the internet, the advantage of the RC4 algorithm is that it is simple and effective. There are several weak, especially after the pseudo-random generation algorithm (PRGA), PRGA's initially 256 rounds (the amount of the RC4 permutation). Several modified RC4 studies have been published thus far, however, they all face either standard privacy or achievement evaluation issues. This paper proposes a new RC4 algorithm that is based on the user's retina (RC4-Retina), which has solved both of these weak points it was indicated in the standard RC4 algorithm. The novelty of retina key scheduling algorithm (RKSA), which is generated by relying on the user's retina of the algorithm will modify the matrix of permutation used to configure the keys. The efficiency of the improved algorithm was measured by depending on the average security of ciphertext of different keys and different messages, results were good compared to the standard algorithm.

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“…T is a pixel average-gray value. The computation of information entropy is described by (5) to a pair of values (g, t) [28].…”

Section: Pixel Entropy Feature Extraction Using Two Dimensions' Maximum Entropy Threshold Methodsmentioning

confidence: 99%

“…The security of RC4 algorithm using keys generation depending on user's retina (Huda M. Salih) 453 aforementioned. The significant amount of RNG bits are delivered clearly and so a passive intruder has an easy ability to read the RNG performance and may influence any shortcomings contained there [5], [6].…”

Section: Introductionmentioning

confidence: 99%

The security of RC4 algorithm using keys generation depending on user's retina

Salih

Mahdawi

2021

IJEECS

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“…Recognition algorithms rely on individual variances in physical or behavioral traits [2]. A biometric recognition technology that is used to detect, recognize, identify, or authenticate a person in a digital image or video frame [3], computer vision [4], machine learning [5], real-web services [6], computer games [7], and time video [8]. Face recognition, authentication, tracking, expression categorization approaches, and feature expression mechanics are all under investigation [9].…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of chi-square and relief-F feature selection for facial expression recognition

Mahmood

Abdulrazzaq

2022

IJEECS

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Pattern recognition is a crucial part of machine learning that has recently piqued scientists' interest. The feature selection method utilized has an impact on the dataset's correctness and learning and training duration. Learning speed, comprehension and execution ease, and properly chosen features influence all high-quality outcomes. The two feature selection methods, relief-F and chi-square, are compared in this research. Each technique assesses and ranks attributes based on distinct criteria. Six of the most important features with the highest ranking have been chosen. The six features are utilized to compare the performance accuracy ratios of the four classifiers: k-nearest neighbor (KNN), naive Bayes (NB), multilayer perceptron (MLP), and random forests (RF) in terms of expression recognition. The final goal of the proposed strategy is to employ the least number of features from both feature selection methods to distinguish the four classifiers' accuracy performance. The proposed approach was trained and tested using the CK+ facial expression recognition dataset. According to the findings of the experiment, RF is the best accurate classifier on chi-square feature selection, with an accuracy of 94.23 %. According to a dataset utilized in this study, the relief-F feature selection approach had the best classifier, KNN, with an accuracy of 94.93 %

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Intelligent security system detects the hidden objects in the smart grid

Cited by 2 publications

References 19 publications

The security of RC4 algorithm using keys generation depending on user's retina

The security of RC4 algorithm using keys generation depending on user's retina

Performance evaluation of chi-square and relief-F feature selection for facial expression recognition

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