Advanced Phishing Website Detection Techniques in Internet of Things Using Machine Learning

There is a serious risk to information privacy and security in the Internet of Things (IoT) ecosystem from phishing attacks. Phishing assaults are futile attempts to fool people into disclosing their login credentials or infecting the beacon's computer with malware. This research focuses on identifying phishing websites by using feature extraction, which modifies raw data, image files as a general usage context, and numerical features that operate with machine learning techniques. Phishing attacks are increasingly being conducted mostly through emails. The most important thing in this situation is to recognize phishing websites. Using social media, text messaging, or spam, the attacker(s) creates a website by mimicking reputable websites, and then distributes the URL(s) to their intended audience. An attempt has been made to broaden the present scope of phishing website detection through a number of review studies. The reader gains a better understanding of the many strategies, tactics, advantages, and disadvantages of phishing website detection techniques from this review, along with the relative efficacy of the used algorithms. Subsequent investigations must to take into account the extra information suggested by this study, including typical paperwork along with performance reporting. Researchers attempting to confirm previous findings and evaluate the relative merits of recently presented technologies vs. existing systems may find this survey to be helpful.