Text Classification Techniques Used to Faciliate Cyber Terrorism Investigation

Abstract

rising of computer violence, such as Distributed Denial of Service (DDoS), web vandalism, and cyber bullying are becoming more serious issues when they are politically motivated and intentionally conducted to generate fear in society. These kinds of activity are categorized as cyber terrorism. As the number of such cases increase, the availability of information regarding these actions is required to facilitate experts in investigating cyber terrorism. This research aims to create text classification system which classifies the document using several algorithms including Naïve Bayes, Nearest Neighbor, Support Vector Machine (SVM), Decision Tree, and Multilayer Perceptron. The result shows that SVM outperforms by achieving 100% of accuracy. This result concludes the excellent performance of SVM in handling high dimensional of data.