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Table 1 Downsides of the existing phishing detection methods

From: Development of anti-phishing browser based on random forest and rule of extraction framework

Paper Method Disadvantages
Williams and Li (2017), Afroz and Greenstadt (2011) Detect and block the phishing web sites manually in time. • Most of the internet users do not have the knowledge to identify a phishing webpage in real-time.
• Even trained people fall into the attack because people tend to forget to check the website’s legitimacy while they are busy with their work.
• Security awareness training is not continuous.
Ma et al. (2009), Mao et al. (2017), Futai et al. (2016) Detection based on URL and Content of Websites. • They lack in new website URL detection
• These methods are not accurate and they tend to modest false-negative rate.
Roy et al. (2013), Pandey and Ravi (2013) Block the phishing E-mails by various spam filter software • These spam filters tend to block genuine messages.
• They fail to detect these attacks apart from email-threads.
Hu et al. (2016), Wu et al. (2019) Server-side Detection • Users will receive delayed responses from servers about the authenticity of the website.
• They underperform in slow internet connections.
Armano et al. (2016), Marchal et al. (2017) Client-side Detection • These software’s signature-based security controls are proving less and less effective as years pass by. For example, these solutions are not particularly good at identifying file-less malware.
• They utilize a lot of memory.
Mei et al. (2016) Other Detection methods • It is not effective on pages that are not visited previously and websites should be maintained by constantly updating to preserve better accuracy.