Credibility Evaluation of Web Big Data Information Based on Particle Swarm Optimization

Zhao, Nannan

doi:10.13052/jwe1540-9589.21212

Cited by 2 publications

(3 citation statements)

References 11 publications

(12 reference statements)

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“…Multi-objective genetic algorithm is used to solve the big data view selection problem as a bi-objective optimization problem [33]. The improved particle swarm optimization is set up to make it easier to evaluate the credibility of big data found on the web [34].…”

Section: Wrapper Methodsmentioning

confidence: 99%

Filter and Embedded Feature Selection Methods to Meet Big Data Visualization Challenges

ElDahshan¹,

AlHabshy²,

Mohammed³

2023

Computers, Materials &Amp; Continua

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This study focuses on meeting the challenges of big data visualization by using of data reduction methods based the feature selection methods. To reduce the volume of big data and minimize model training time (Tt) while maintaining data quality. We contributed to meeting the challenges of big data visualization using the embedded method based "Select from model (SFM)" method by using "Random forest Importance algorithm (RFI)" and comparing it with the filter method by using "Select percentile (SP)" method based chi square "Chi2" tool for selecting the most important features, which are then fed into a classification process using the logistic regression (LR) algorithm and the k-nearest neighbor (KNN) algorithm. Thus, the classification accuracy (AC) performance of LR is also compared to the KNN approach in python on eight data sets to see which method produces the best rating when feature selection methods are applied. Consequently, the study concluded that the feature selection methods have a significant impact on the analysis and visualization of the data after removing the repetitive data and the data that do not affect the goal. After making several comparisons, the study suggests (SFMLR) using SFM based on RFI algorithm for feature selection, with LR algorithm for data classify. The proposal proved its efficacy by comparing its results with recent literature.

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Section: Wrapper Methodsmentioning

confidence: 99%

Filter and Embedded Feature Selection Methods to Meet Big Data Visualization Challenges

ElDahshan¹,

AlHabshy²,

Mohammed³

2023

Computers, Materials &Amp; Continua

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“…Then the multiple convolutional layers and the max pooling layer are concatenated before the fully connected layer. In this research, various scenarios of kernel number [3,5,7,9,11] and dropout rate [0,1,0.3,0.5,0.7,0.9] were carried out. Another recent study experimented with ML and DL methods, namely decision tree, ANN, random forest, gradient boosting, fully connected feedforward deep neural networks, LSTM and CNN.…”

Section: Existing Studies On Phishing Detectionmentioning

confidence: 99%

“…Phishing, a form of cybercrime, poses considerable risks to unsuspecting users who encounter cloned web pages mimicking authentic websites. These deceptive replicas can lead to the inadvertent transmission of sensitive user data to malicious servers, thereby compromising the security and privacy of individuals [6,7].…”

Section: Introductionmentioning

confidence: 99%

HCNN-LSTM: Hybrid Convolutional Neural Network with Long Short-Term Memory Integrated for Legitimate Web Prediction

Zonyfar,

Lee,

Kim

2023

JWE

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Phishing techniques are the most frequently used threat by attackers to deceive Internet users and obtain sensitive victim information, such as login credentials and credit card numbers. So, it is important for users to know the legitimate website to avoid the traps of fake websites. However, it is difficult for lay users to distinguish legitimate websites, considering that phishing techniques are always developing from time to time. Therefore, a legitimate website detection system is an easy way for users to avoid phishing websites. To address this problem, we present a hybrid deep learning model by combining a convolution neural network and long short-term memory (HCNN-LSTM). A one-dimensional CNN with a LSTM network shared estimation of all sublayers, then implements the proposed model in the benchmark dataset for phishing prediction, which consists of 11430 URLs with 87 attributes extracted of which 56 parameters are selected from URL structure and syntax. The HCNN-LSTM model was successful in binary classification with accuracy, precision, recall, and F1-score of 95.19%, 95.00%, 95.00%, 95.00%, successively outperforming the CNN and LSTM. Thus, the results show that our proposed model is a competitive new model for the legitimate web prediction tasks.

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Credibility Evaluation of Web Big Data Information Based on Particle Swarm Optimization

Cited by 2 publications

References 11 publications

Filter and Embedded Feature Selection Methods to Meet Big Data Visualization Challenges

Filter and Embedded Feature Selection Methods to Meet Big Data Visualization Challenges

HCNN-LSTM: Hybrid Convolutional Neural Network with Long Short-Term Memory Integrated for Legitimate Web Prediction

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