WEB MINING TO DETECT ONLINE SPREAD OF TERRORISM

Abstract

To fight online terrorism, integrating web mining techniques with data mining algorithms is crucial. Various algorithms like Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Naive Bayes (NB), Decision Trees (DT), Random Forests (RF), and Gradient Boosting (GB) are studied for detecting terrorist activities online, extracting data, identifying patterns, and relevant information. Logistic Regression provides a probabilistic framework, KNN uses similarity metrics, SVM constructs hyper-planes, NB assumes feature independence, DT builds decision trees, RF applies ensemble learning, and GB boosts weak learners' performance. These algorithms aid in proactive monitoring and prevention of online terrorism through efficient analysis of structured and unstructured web data. By combining web mining and data mining strengths, this study emphasizes a comprehensive approach to combatting online terrorism dissemination, helping security agencies anticipate evolving threats and prevent terrorist propagation effectively. Key Words: Terrorism, Naive-bayes, random forest, web mining, Gradient boosting