Simanjuntak, David Allister and Nugroho, Anto S. (2010) Text Classification Techniques Used to Facilitate Cyber Terrorism Investigation. Bachelor thesis, Swiss German University.

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Abstract

The rising of computer violence, such as Distributed Denial of Service (DDoS), web vandalism, and cyber bullying become more serious issues when they are politically motivated and intentionally conducted to generate fear in society. These kinds of activities 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. Meanwhile, web mining is one of significant technologies applied to extract information from the Web. In this case, web mining facilitates data acquisition related to cyber terrorism information from the Web. This research aims to create text classification technique based upon number of occurrences of certain relevant words in the term of Cyber Terrorism. This research compared the result of accuracy of several algorithms including Naïve Bayes, Nearest Neighbor, Support Vector Machine (SVM), Decision Tree, and Multilayer Perceptron Neural Network. The result shows that SVM outperform by achieving 100% of accuracy. According to this result, it concludes the excellent performance of SVM in handling high dimensional of data.

Item Type:	Thesis (Bachelor)
Uncontrolled Keywords:	Cyber Terrorism; Data Mining; Feature Selection; Text Classification; Web Mining
Subjects:	Q Science > QA Mathematics > QA76 Computer software > > QA76.91 Data mining Q Science > QA Mathematics > QA76 Computer software > > QA76.93 Computer networks--Security measures T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5105 Web Sites-Design > TK5105.59 Intrusion detection systems (Computer security, Firewalls
Divisions:	Faculty of Engineering and Information Technology > Department of Information Technology
Depositing User:	Astuti Kusumaningrum
Date Deposited:	03 Mar 2021 15:57
Last Modified:	03 Mar 2021 15:57
URI:	http://repository.sgu.ac.id/id/eprint/1028

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