2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON) 2021
DOI: 10.1109/gucon50781.2021.9573797
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Phishing URL Classification Analysis Using ANN Algorithm

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Cited by 12 publications
(2 citation statements)
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References 16 publications
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“…Mridha et al [44] This paper proposes RF and ANN-based algorithmic classification models for detecting BEC and phishing URLs accurately. The proposed RF and ANN models can classify BEC and phishing URL legitimacy labels with 99% accuracy.…”
Section: Mughaid Et Al [43]mentioning
confidence: 99%
See 1 more Smart Citation
“…Mridha et al [44] This paper proposes RF and ANN-based algorithmic classification models for detecting BEC and phishing URLs accurately. The proposed RF and ANN models can classify BEC and phishing URL legitimacy labels with 99% accuracy.…”
Section: Mughaid Et Al [43]mentioning
confidence: 99%
“…Evaluating the proposed BEC phishing detection models by various researchers also revealed another difference among the retrieved publications: some researchers utilised publicly available datasets, while other researchers utilised real-world and dynamic datasets that they created in specific circumstances to evaluate the effectiveness of their detection models. For example, Garces and Cazres [36], Ripa et al [41], Alam et al [38], Dewis and Viana [10], Mridha et al [44], and Nidhin et al [31] evaluated the effectiveness of their phishing detection model using Kaggle dataset, one of the most common datasets in phishing detection domain, while other researchers created their own datasets, such as Baykara and Gurel [26], Rawal et al [21], etc.…”
Section: Dewis Andmentioning
confidence: 99%