A Comparative Study on AI-based Anti-terror Crime System
Sei-Youen Oh
Sei-Youen Oh, Department of Police Administration, Semyung University, Semyeong-Ro, Jecheon-Si, Chungcheongbuk-Do, Republic of Korea, East Asian.
Manuscript received on 05 June 2019 | Revised Manuscript received on 12 June 2019 | Manuscript Published on 22 June 2019 | PP: 1103-1107 | Volume-8 Issue-8S2 June 2019 | Retrieval Number: H11870688S219/19©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Recent terrorisms are not restrained in time and place, but have occurred simultaneously and throughout the world in various ways against random people. Therefore, the research suggests AI-based Anti-terror System module to predict terrorisms in advance, in a response to recognition of severity of the current terrorism state. Methods/Statistical analysis:AI-based Anti-terror System module consists of 6 components – Data Collection and Filtering Module, 1st Analysis of Data and Rule Generation Module, D/B, Monitoring Device, 2nd Analysis of Data and Rule Generation Module and Crime Response Module – more accurate and rapid terror responses is enabled through systematic process of collection, analysis, monitoring, response and feedback on terror-related risk data from each of the modules and devices. Findings: The proposal module collect relevant data to terror suspects with Big Data, analyze the realistically high terror-risk data, but filtered and stored, in two different phases, and generate and modify each rule based on extracted pattern of the analyzed data. Then, consistent monitoring on risky data of probable terrors is performed in concerns of the saved analysis and Crime Response Modules are triggered on the basis of the consequent results.Such a proposal module can improve the accuracy of related date, but save duration time in collection of particular data as it only collects relevant data to terror risks via Big Data Source and Filtering Module compared to the existing module. Furthermore, the operation manuals of 1st/2nd phase Data Analysis Modules systematically analyze the terror risks and probabilities, thus can minimize the risk of actual terrorisms in fields by triggering Terror Crime Response Modules rapidly by phases with an increased terror prediction accuracy. As Monitoring Device is additionally incorporated, constant supervision and feedbacks regarding certain risk data relevant to terrors are enabled – as a result, detailed analysis of high risk data is available. Improvements/Applications: Owing to connections among recent terror groups, terror risks are omnipresent throughout the world and prior data collection for time and patterns of terrors has become difficult, hence the AI-based Anti-terror System Module would allow minimization of consequent terror damages, enabling preliminary terror prevention and rapid crime responses.
Keywords: AI, Terror, Data Collection and Filtering Module, 1st/2nd Analysis of Data and Rule generation Module, Monitoring Device.
Scope of the Article: Analysis of Algorithms and Computational Complexity