Table 4.
Top ten most impactful articles (papers sorted by local citations rate).
| Title | Research area | LC | GC | LCR (%) |
|---|---|---|---|---|
| Toward detection of phishing websites on client-side using machine learning based approach (Jain and Gupta, 2018b) | ML | 67 | 96 | 69.79 |
| Detection of phishing websites using an efficient feature-based machine learning framework (Rao and Pais, 2019) | ML | 78 | 133 | 58.65 |
| Phishing website detection based on multidimensional features driven by deep learning (Yang et al., 2019) | DL | 84 | 146 | 57.53 |
| Machine learning based phishing detection from URLs (Sahingoz et al., 2019) | ML | 180 | 323 | 55.73 |
| PhishStorm: detecting phishing with streaming analytics (Marchal et al., 2014) | ML | 70 | 132 | 53.03 |
| A machine learning based approach for phishing detection using hyperlinks information (Jain and Gupta, 2019) | ML | 57 | 114 | 50.00 |
| A new hybrid ensemble feature selection framework for machine learning-based phishing detection system (Chiew et al., 2019) | ML | 89 | 194 | 45.88 |
| A stacking model using URL and HTML features for phishing webpage detection (Li et al., 2019) | ML | 61 | 134 | 45.52 |
| CANTINA+: a feature-rich machine learning framework for detecting phishing web sites (Xiang et al., 2011) | ML | 135 | 324 | 41.67 |
| A comprehensive survey of AI-enabled phishing attacks detection techniques (Basit et al., 2021) | – | 61 | 163 | 37.42 |
LC, local citations; GC, global citations; LCR, local citations/global citations Ratio (%).