Table 1 Downsides of the existing phishing detection methods

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.