Detection of Data Breaching Websites using Machine Learning
M. Prathap, K. M. Nandhini, K S Vairavel, M V Suraj · 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA) · 2021
In pandemic situation, the world has upgraded to virtual mode. From classes to work everything turned to be online mode. So, the usage of internet and online services are rising to a greater extent. People lack knowledge about privacy risks and data breaching websites. These websites paved the way for lot of cybercrimes like data breaching. Many methodologies have been used to identify data breaching websites earlier. But phishers are succeeded in finding loop holes. This paved way to create solution to overcome security checks. Most of the cyber-attacks had same pattern. These patterns will give a brief knowledge about these cyber-attacks. For identifying data breaching websites, machine learning algorithms will be the better choice of selection. This project deals with design and development of a model to identify data breaching websites. Algorithm chosen for this project is Random Forest Algorithm, the best suited algorithm for classification of large number of characters. There are 3 major steps involved in this project such as data collection, feature extraction and testing and training. Important step in this process is feature extraction from website URL.A set of 17 features are extracted for analysis. Following the feature extraction, there will 2 phase such as training and testing. By the end, trained machine learning model will be obtained. This trained model can be used in future works. Numerous data breaching websites are created in current days. This machine learning technique will identify those data breaching websites in an effective manner.