Prediction of Phishing Websites Using AI Techniques

Prediction of Phishing Websites Using AI Techniques

Gururaj H. L., Prithwijit Mitra, Soumyadip Koner, Sauvik Bal, Francesco Flammini, Janhavi V., Ravi Kumar V.
Copyright: © 2022 |Pages: 14
DOI: 10.4018/IJISP.310069
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Abstract

The increase of internet usage in recent times has been a noticeable change in this generation. Users from all over the world use social sites to interact across the world. Countless websites are present today. With countless networks and sites, some people or companies tend to create new ways to lure out the random users using the web, such as phishing. In phishing, the normal users are swindled to use the fraudulent websites. The aim is to identify the phishing websites with great accuracy and compare different methods by which phishing websites can be tracked in an easier and more accurate way. Comparative studies of various algorithms are tested with the help of 10,000 datasets, each tested with 18 different parameters to increase the accuracy score of each algorithm. The paper shows the methods used for phishing detection are more accurate than other practices done so far using certain appropriate parameters and more useful.
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2. Literature Survey

In this section various literature works through different peoples were summarized.

Here,(Pujara & Chaudhari, 2018) the methodologies-Heuristic based method, Blacklist method, Visual similarity were used. If the URLs that were caught inside the databases were found, it was marked as a phishing URL and showed warnings, if not it was seen as legitimate site. The heuristic method, identifies phishing potentials by applying features collected through phishing websites for detecting fraud attacking. Visual similarity extracts images which are found in legitimate sites. The approaches performed efficiently in large datasets as was seen in (Pujara & Chaudhari, 2018). Were as, (Revoredo da Silva et al., 2020) shows that new heuristic approaches can be developed through the evaluation of the results which can improve the durability, stability and performance of the existing ones described in (Revoredo da Silva et al., 2020).

The paper (Jain & Gupta, 2017) shows several procedures for phishing detections, with limitations, while detecting embedded objects, identifying fraud websites, countermeasures towards newly built phishing sites. Different features for a webpage to detect fraudulence were utilized like text similarity, font size, font color. The similarity approach of text basing could not detect phishing sites if it interchanged texts with images instead. The other approach for such drawbacks was Image processing-based algorithms. But, as it becomes way complex, it was unable to be applied in (Jain & Gupta, 2017)

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