Assessment of Phishing Websites Prediction using Machine Learning Approaches - Prajapati, Ankit; Agarwal, Chetan; Meena, Pawan (International Journal of Computer Sciences and Engineering, 2024)
Phishing is a kind of cyberattack in which victims are tricked into divulging private information, including credit card numbers or passwords, by means of phoney emails or websites. Users may find it challenging to distinguish phishing websites from authentic websites due to their convincing appearance. This can lead to users entering their personal information on the phishing website, which can then be stolen by the attacker. An artificial intelligence technique called machine learning is used to train algorithms to find patterns in data. This can be used to create systems that automatically detect and alert users to potentially harmful websites, such as phishing website detection systems. The field of phishing website prediction currently faces some obstacles that require attention. The constant growth of phishing methods is one challenge. Artificial intelligence-based deep learning and machine learning techniques can identify phishing websites. Using machine learning techniques to predict phishing websites, we identify, monitor, and shield end users from monitoring based on phishing algorithms with respect to different publications. We present a machine learning method for phishing website identification in this research. Our method makes use of a number of characteristics, such as the URL structure, website content, and the existence of particular keywords or patterns, to discern between authentic and phishing websites. We test our method on a dataset of actual phishing websites, such as Google`s PhishCorp, Kaggle, and PhishTank, and we obtain a greater accuracy than the earlier studies on the detection of phishing websites. Our results show that machine learning can be an effective method for spotting phishing websites. With a better prototype and increased accuracy, our method is simple to use and can shield users from phishing assaults.