
Phishing is one of the major cyber threats now, where the victims' credentials are obtained by an illegitimate website. This paper proposes a system which will detect old as well as newly generated phishing URLs that have completely no past behaviours to judge upon, using Data Mining. A cloud-based classification model will be created for the same wherein various extracted attributes through the URL will be used as input data. The model will be trained with an exhaustive dataset so as to ensure maximum accuracy.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 9 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
