MDepthNet based phishing attack detection using integrated deep learning methodologies for cyber security enhancement

Developing technologies over digitalization have become more popular and become a threat to society and cybersecurity. Generally, the phishing method is used by hackers to access the data without the influence of users whose data was stolen. Several techniques are used to detect whether the data is...

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Veröffentlicht in:Cluster computing 2024-08, Vol.27 (5), p.6377-6395
Hauptverfasser: Yamarthy, Anil Kumar, Koteswararao, Ch
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description Developing technologies over digitalization have become more popular and become a threat to society and cybersecurity. Generally, the phishing method is used by hackers to access the data without the influence of users whose data was stolen. Several techniques are used to detect whether the data is phished or non-phished. Some anti-phishing software is used to identify the phishing data. However, few of these techniques did not provide efficient performance. Hence, the proposed model is introduced to overcome the issues obtained and improve the efficiency of detecting whether the data is phished or non-phished. The data is gathered from the Phishstorm dataset, which is pre-processed using the Z score normalization method and data cleaning. Data balancing is done by the Advanced synthetic sampling approach (Adv-SyN) to balance the dataset, and the features are extracted using a Double self-sparse autoencoder (DSelSa). The Opposition Gazelle optimization algorithm (OpGoA) model is used for optimal feature selection, and finally, the data is classified using Multi Head Depth wise Tern integrated long short term memory (MDepthNet). The sooty tern optimization is used to evaluate the loss function of the network model. The performance of the proposed model is analyzed based on some evaluation metrics and compared with other models, which describes the efficiency of the proposed model. The main objective of proposed technique used to detect the phishing attack and phishing or non-phishing. An automated DL methodology introduced for effective detection of phishing attacks for enhancing the cyber security. The accuracy of the proposed model is obtained as 99.45%, and Precision is 99.45%. RMSE and MSE rate of the proposed model is reduced to 0.73 and 0.05 for better performance.
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subjects Accuracy
Algorithms
Classification
Computer Communication Networks
Computer Science
Confidentiality
Cybercrime
Cybersecurity
Datasets
Deep learning
Digitization
Electronic mail systems
Fraud
Internet
Machine learning
Malware
Methods
Neural networks
Operating Systems
Optimization
Optimization algorithms
Performance evaluation
Personal information
Phishing
Processor Architectures
Websites
title MDepthNet based phishing attack detection using integrated deep learning methodologies for cyber security enhancement
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