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    An Intelligent Terrorism Detection System using Machine Learning

    by Onyinye Nweke 11/10/2019 01:55 PM GMT

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          The rise of insurgency in Nigeria has led to massive attacks on the people and government of the nation. These attacks have also been extended into the cyberspace with acts of digital terrorism which brings a negative impact on our economy and community development, while threatening the security of the entire nation and the minds of citizens.

          The BokoHaram threat has become a growing pain in the lives of our people here in Nigeria, so also is ISIS and other terrorist groups to the world. In recent years, the social media has been used for such acts of terrorism, for both recruitment and a means of unleashing terror on users which should not be condoned anymore. In addition to sensitization, users of social media should be protected considering that a lot of these users are teenagers and young adults who maybe more vulnerable to such attacks and threats.

          The problem of cyberterrorism has maintained an exponential growth which can only be combatted by an intelligent technique. We hereby propose an efficient technique for combatting cyberterrorism through analysis of social media data sourced from twitter users in the African region. Our aim is to develop an intelligent system with high prediction rate, high accuracy and strong classification of cyberterrorism on the internet.

          Our objectives include;

          1. Mining of data using twitter API, in order to gather user data for a one month duration in Nigeria.
          2. Analysis of the data to find trends in cyberterrorism.
          3. Classification of tweets as negative and positive using supervised machine learning technique such as the Artificial Neural Network.
          4. Visualization of twitter data to show trends in online terrorism in Africa

          The result of which will be applied to make predictions of possible threats in the future, and help decision makers to devise methods for tackling them ahead of time. More so the resulting system will be applied in social media monitoring, content filtering and for the enhancement of current counter-terrorism techniques. This project will make available a dataset for further research in the field of cybersecurity and cyber terrorism.


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