Abstract:
The trend of tricking web users has kept pace with the expanding utilization of online
surfing. The rise of phishing attacks postures a noteworthy risk to individuals and
organizations everywhere. Phishing is constantly advancing to receive modern methods
and techniques to steal important pieces of information from users. Phishing is a form of
attack initiated by an email or social media message which mainly forwards the casualties
to malicious web pages and these are extremely difficult to identify for security
administrators. Phishing is a part of social engineering. Through this, hackers design a web
page duplicate and send it to the user when the user enters information that data is directly
saved to a database created by hackers. The most commonly used phishing techniques are
link manipulation, filter evasion, website forgery, social engineering, and covert redirect.
To recognize unique patterns, Machine Learning algorithms continuously learn from huge
bulk data and in most research, it has been claimed that machine learning-based methods
are more effective than other methods. Here, we use Five machine-learning classification
techniques to detect phishing web pages and legitimate web pages with desirable accuracy.
In our work, we apply Logistic regression, Decision tree, XGBoost, Random Forest, and
SVM algorithms. All algorithms perform incredibly well on dataset. The Random Forest
algorithm surpasses them all with a 98% accuracy rate.
