Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework

It is worth their reliability on [1], [5], [8], blacklists/whitelists [9], natural noting that past research work on phishing attack detection had language processing [15], visual similarity [15], rules [14], been largely based on approaches, classification, etc. RASHA [24], remains vulnerable to attack due to the following reasons; ZIENI et al.. [35] focus their review on list-based, similaritybased, and machine learning-based categories of approaches Having understood how the machine learning-based for phishing detection to identify pending research gap, Angad model works, attackers are now increasingly relying on et al.. [21] focus theirs on the advantages and limitations of asymmetrical methods by uploading images and videos existing approaches to phishing detection, while also using to evade detection under various pretexts, and none of the discussion of related application scenarios as guidance to propose proposed models can single-handedly be effective against a new method of anti-phishing detection, Yifei Wang [32] such.