Treffer: Phishing Website Identification via Kit Analysis.

This kit-based identification relies on the discovery of recurring patterns and artifacts left by phishing kits, such as prepackaged tools used in generating fake websites. The works of this investigation focus on code snippets, file structures, and common assets among different phishing websites. This solution uses a Python-based program paired with website scraping and signature-based detection. It applies for content extraction libs like BeautifulSoup and on the other hand uses hashlib to generate signatures for files by methods such as SHA-512. These features are then used in a system involving the classification of web sites as a possible malware employing techniques in machine learning like scikit-learn. This is meant to proactively present itself as some sort of defense mechanism against the development of phishing attempts by going at backend infrastructure. It complements previous approaches. It will integrate with real-time sources and yield better detection accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Grenze International Journal of Engineering & Technology (GIJET) is the property of GRENZE Scientific Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)