A Data Mining Approach to Monitor Terrorism Dissemination Online
M. Asha Priyadarshini, T. V. L. Bhavani, P. Geya Geeta Sree, S. K. Darga Mastan Vali, P. Ashok Chakravarthi · Advances in computer science research · 2024
Web data mining is essential for identifying the online propagation of terrorism.Terrorist groups are using phishing websites more frequently to spread their beliefs, find new members, and plan events.We can evaluate web data to differentiate between websites linked to terrorist activity and those that are legal by using machine learning algorithms like XGBoost, Gradient Boosting, Adaboost, SVM, and Random Forest.These algorithms are capable of efficiently identifying suspicious patterns suggestive of sites linked to terrorism by extracting data such as URL structure, domain age, and content.We can determine the precision and effectiveness of these techniques by conducting a thorough assessment, which will allow us to take preventative action like blocking locations known to be used by terrorists.Web data mining, terrorism detection, machine learning techniques, XGBoost, Gradient Boosting, Adaboost, SVM, Random Forest, feature extraction, website analysis, cybersecurity, global security.