Winning the War on Terror: Using “Top-K” Algorithm and CNN to Assess the Risk of Terrorists

Winning the War on Terror: Using “Top-K” Algorithm and CNN to Assess the Risk of Terrorists

Yaojie Wang (Engineering University of PAP, China), Xiaolong Cui (Engineering University of PAP, China) and Peiyong He (Engineering University of PAP, China)
DOI: 10.4018/IJITWE.288038
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Abstract

From the perspective of counter-terrorism strategies, terrorist risk assessment has become an important approach for counter-terrorism early warning research. Combining with the characteristics of known terrorists, a quantitative analysis method of active risk assessment method with terrorists as the research object is proposed. This assessment method introduces deep learning algorithms into social computing problems on the basis of information coding technology. We design a special "Top-k" algorithm to screen the terrorism related features, and optimize the evaluation model through convolution neural network, so as to determine the risk level of terrorist suspects. This study provides important research ideas for counter-terrorism assessment, and verifies the feasibility and accuracy of the proposed scheme through a number of experiments, which greatly improves the efficiency of counter-terrorism early warning.
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Once terrorist activities occur, they will bring major social risks and irreparable losses, which are a huge challenge for counter-terrorism operations (Jennifer & Louise, 2009; Pooja & Archana, 2021). There are great differences in the research contents of counter-terrorism threat risk assessment. From the perspective of the role attributes of research object, it can be divided into global risk assessment, fixed object risk assessment and active risk assessment (Scott, 2001; Novikov & Koshkin, 2019; Karl & John, 2008). The latter two are more focused and easy to quantify, and generally serve as the basis of global risk assessment. Due to the different objects of risk assessment, the methods adopted are also very different, resulting in great differences in the difficulty, effect and scope of application of the assessment.

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