
In today's era, due to the surge in the usage of the internet and other online platforms, security has been major attention. Many cyberattacks take place each day out of which website phishing is the most common issue. It is an act of imitating a legitimate website and thereby tricking the users and stealing their sensitive information. So, concerning this problem, this paper will introduce a possible solution to avoid such attacks by checking whether the provided URLs are phishing URLs or legitimate URLs. It is a Machine Learning based system especially Supervised learning where we have provided 2000 phishing and 2000 legitimate URL dataset. We have taken into consideration the Random Forest Algorithm due to its performance and accuracy. It considers 9 features and hence detects whether the URL is safe to access or a phishing URL.
| 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). | 5 | |
| 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). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
