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

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Abstract

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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Data availability

The entire implementations of the work will be carried out in Python platform. The performance metrics like Accuracy, precision, recall, F1 score and so on, will be examined for the phishing attack detection through deep learning methodologies. Furthermore, the performances will be compared with different existing approaches for enhanced analysis.The data that support this finding of this study are openly available at the following URL/DOI: Phishstorm dataset: https://research.aalto.fi/en/datasets/phishstorm-phishing-legitimate-url-dataset.

Code availability

The code that supports this finding of this study are openly available at the following URL/DOI: https://github.com/Anilyamarthy/Code.git.

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Acknowledgements

We declare that this manuscript is original, has not been published before and is not currently being considered for publication elsewhere.

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Authors and Affiliations

  1. School of Computer Science and Engineering, VIT-AP University, Amaravati, Vijayawada, 522237, India

    Anil Kumar Yamarthy & Ch Koteswararao

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  1. Anil Kumar Yamarthy
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Contributions

Anil Kumar Yamarthy: Conceptualization, Data Curation, Formal Analysis, Investigation, Resources, Software, Writing original draft. Ch Koteswararao: Methodology, Project administration, Supervision, Validation, Visualization, Writing-Review&editing, Funding acquisition.

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Correspondence to Ch Koteswararao.

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Yamarthy, A.K., Koteswararao, C. MDepthNet based phishing attack detection using integrated deep learning methodologies for cyber security enhancement. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04313-w

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