Detect phishing URLs as well as narrow down to the best machine learning algorithm by comparing the accuracy rate, false positive and false negative rate of each algorithm.
✴️Phishing is the practice of sending fraudulent communications that appear to come from a legitimate and reputable source, usually through email and text messaging.
✴️Phishing attacks can also occur through other means, such as phishing websites, where attackers create fake websites that mimic the appearance of popular sites to deceive users into entering their login information
📌Web Browsers
📌Email Clients
📌Antivirus and Endpoint Security
📌Online Banking
📌Mobile Security Apps
⭐KNN algorithm
⭐Support Vector Machine Algorithm
⭐Gradient Boosting
⭐Logistic Regression
⭐Decision Tree
⭐Random Forest
⭐XGBoost
⭐AdaBoosting Classifier
◾It is found that phishing attacks is very crucial and it is important for us to get a mechanism to detect it.
◾personal information of the user can be leaked through phishing websites, it becomes more critical to take care of this issue.
◾This problem can be easily solved by using any of the machine learning algorithm with the classifier.
🔥ML & AI - Continous development in the field of Machine Learning and AI that can improve the accuracy of phishing detection.
🔥Email Filtering - Enhance email filtering systems to better detect and block phishing emails before they reach a user's inbox.
🔥Phishing Feeds - Utilize threat intelligence feeds and services that provide up-to-date information on known phishing websites.
🔥Multi-Factor-Authentication - Encourage or enforce the use of MFA for online services. Even if users fall for a phishing attack and provide their credentials, MFA can provide an additional layer of protection.
