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A new hybrid deep learning-based phishing detection system using MCS-DNN classifier

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Abstract

Phishing is an attack that deceit online users by means of masquerading as a genuine website to pilfer their classified or personal information. This is one among the recognized cybercrime. Disparate phishing website detection systems were recently developed for the purpose of detecting the phishing websites. However, they fail to attain the desired output and are suffered from countless drawbacks like lower accuracy and higher training time. For trouncing such drawbacks, this paper proposes an effectual Hybrid Deep Learning (HDL)-centric Phishing Detection System (PDS) using the MCS-DNN classifier. At first, pre-processing is done on the input dataset for ameliorating its quality. Subsequently, clustering and feature selection (FS) are performed to lessen the processing time and elevate the accuracy using CoK-means and CM-WOA, respectively. The features which are chosen during FS are fed into the MCS-DNN classifier, which classifies the legitimate websites and phishing websites. Lastly, the K-fold cross-validations (KCV) are employed for effectively predicting the proposed system’s accurateness. The outcomes highlight the robustness and predictive ability of the proposed PDS to distinguish the phishing as well as legitimate sites.

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Data availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Abbreviations

HDL:

Hybrid deep learning

PDS:

Phishing detection system

FS:

Feature selection

KCV:

K-fold cross-validations

MCS-DNN:

Modified crow search-based deep learning neural network

CM-WOA:

Crossover mutation-based whale optimization algorithm

DNN:

Deep learning neural networks

RF:

Random forest

PAs:

Phishing attacks

PDS:

Phishing detection system

CDF-g:

Cumulative distribution function-gradient

FPRs:

False-positive rates

CNN:

Convolutional neural network

MHSA:

Multi-head self-attention

LSTM:

Long short-term memory

NLP:

Natural language processing

DF :

Domain-based features

KMC:

K-means clustering

ED:

Euclidean distance

FNR:

False-negative rates

WOA:

Whale optimization algorithm

CM:

Crossover mutation

CM-WOA:

CM-based WOA’

MCS:

Modified crow search

HL:

Hidden layer

ABC:

Ant bee colony

PSOs:

Particle swarm optimizations

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Acknowledgements

We thank the anonymous referees for their useful suggestions.

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Authors and Affiliations

  1. Department of Information Technology, Sri Ramakrishna Engineering College, Anna University, Coimbatore, India

    J. Anitha & M. Kalaiarasu

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  1. J. Anitha
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  2. M. Kalaiarasu
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by JA, MK. The first draft of the manuscript was written by JA and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to J. Anitha.

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Anitha, J., Kalaiarasu, M. A new hybrid deep learning-based phishing detection system using MCS-DNN classifier. Neural Comput & Applic 34, 5867–5882 (2022). https://doi.org/10.1007/s00521-021-06717-w

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