Web Defacement Identification And Detection System
V.M. Vinayagam, T. Sathish, K. Sreenivasulu, D. Sreenu, R. Snehit, Y. Abhilash Reddy · 2024
Although it has become a need in our daily lives, browsing the internet can be risky. New features and capabilities are always being added by browser vendors, which can increase the usability of websites but also increase their vulnerability to attacks. Because they aim to steal confidential data from gullible users, phishing websites pose a severe threat to internet security. In order to counter this issue, academics have created a number of methods, such as machine learning algorithms, for identifying phishing websites. By using extensive datasets of authentic and fraudulent websites, machine learning algorithms can be trained to identify patterns and traits that differentiate between the two. Then, before people become victims, these algorithms can be used to recognize and prevent phishing websites. Feature extraction is one method for detecting phishing websites using machine learning. In this method, different aspects of a website, like its content, age, and URL structure, are examined to detect phishing websites. Another approach involves using deep learning algorithms to automatically extract features and learn complex patterns in website data. Overall, machine learning-based phishing website detection techniques have shown promising results, achieving high accuracy rates and outperforming traditional rule-based methods. These strategies could become valuable weapons in the fight against online phishing attempts with more study and refinement.