Detection of Phishing Sites

Detection of Phishing Sites

Prof.Baliram Deshmukh, Nageshwar Kauthale, Anushka Ingle, Dhanshri Salve, Madhusudan Vetal

Prof.Baliram Deshmukh, Comp Engin, SAE (baliramdeshmukh87@gmail.com)

Nageshwar Eknath Kauthale,CompEngin,SAE( nageshwarkauthale.sae.comp@gmail.com)

Anushka Sandip Ingle,CompEngin,SAE (anushkaingale.sae.comp@gmail.com)

Dhanshri Anil Salve,Comp Engin, SAE (dhanshrisalve.sae.comp@gmail.com)

Madhusudan Ramchndra Vetal Comp Engin, SAE (madhusudanvetal.sae.comp@gmail.com)

ABSTRACT

Phishing poses a significant threat to cybersecurity as attackers can easily replicate legitimate websites, tricking users into providing sensitive information. Despite security measures, many users fall victim to these attacks, which result in billions of dollars in losses annually. Traditionally, phishing detection relies on the blacklist method, where known malicious URLs and IPs are stored in databases. However, attackers circumvent these blacklists using techniques like URL obfuscation, fast-flux hosting, and generating new URLs algorithmically. Recent advancements in machine learning offer a promising alternative for detecting phishing websites. By analyzing website content and detecting suspicious patterns, machine learning models trained on large datasets of both legitimate and phishing sites can enhance detection accuracy. Complementary strategies include educating users about recognizing phishing attempts, such as checking URLs for irregularities and avoiding suspicious emails.

Key words
KEYWORDS: Cybersecurity; Social Engineering; Spear Phishing; SVM; Decision Tree; Random Forest.