Connecting the Dots of Sensitive Terrorism Information for Homeland Security

  • Ugochukwu Onwudebelu Department of Mathematics and Computer Science, Western Delta University, Delta State
  • Jackson Akpojaro Dept of Mathematics and Computer Science, Western Delta University, Oghara, Delta State, Nigeria
Keywords: Data mining, homeland security, threats, profiling, data set


As society becomes more and more dependent on information and as criminals are increasing their cyber activities in their daily life, it becomes necessary to connect their dots together to track them in this information age. Terrorism is not confined to one country and it has no borders or boundaries. The escalating magnitude of this threat is evident from the increasing rate of terrorist attacks against innocent people, especially in the Northern part of Nigeria. As we are seeing, one of the major concerns of many nations today is to identify and foil terrorist attacks emanating from different angles. Consequently, data mining which is being used for almost everything from improving service or performance to detecting specific identifiable terrorist threats is employed. Defeating terrorism requires quick intelligence machinery that operates more effectively and makes use of advanced information technology such as data mining and automated data-analysis techniques for a successful fight against terrorist as well as collaboration in data-sharing program between the three levels of government: federal, state and local. In this paper, we are looking at the need to design support information sharing among these levels of government. So that the government, as a whole will use its power to affect the lives of individuals increasingly with regards to safeguarding lives and properties.

Author Biography

Jackson Akpojaro, Dept of Mathematics and Computer Science, Western Delta University, Oghara, Delta State, Nigeria

Dept of Mathematics and Computer Science

Senoir Lecturer


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How to Cite
Onwudebelu, U., & Akpojaro, J. (2014). Connecting the Dots of Sensitive Terrorism Information for Homeland Security. Transactions on Machine Learning and Artificial Intelligence, 2(3), 35-47.