Terrorism has become one of the most tedious problems to deal with and a prominent threat to mankind. To enhance counter-terrorism, several research works are developing efficient and precise systems, data mining is not an exception. Immense data is floating in our lives, though the scarce availability of authentic terrorist attack data in the public domain makes it complicated to fight terrorism. This manuscript focuses on data mining classification techniques and discusses the role of United Nations in counter-terrorism. It analyzes the performance of classifiers such as Lazy Tree, Multilayer Perceptron, Multiclass and Na\"ive Bayes classifiers for observing the trends for terrorist attacks around the world. The database for experiment purpose is created from different public and open access sources for years 1970-2015 comprising of 156,772 reported attacks causing massive losses of lives and property. This work enumerates the losses occurred, trends in attack frequency and places more prone to it, by considering the attack responsibilities taken as evaluation class.
翻译:恐怖主义已成为最棘手的问题之一,成为人类面临的最突出威胁。为了加强反恐,一些研究工作正在开发高效和精确的系统,数据挖掘并非例外。大量数据在我们生活中浮现,尽管在公共领域真实的恐怖袭击数据很少,使得打击恐怖主义变得复杂。这份手稿侧重于数据开采分类技术,并讨论了联合国在反恐中的作用。它分析了Lazy Tree、多层 Percepron、多级和纳伊夫贝斯分类师等分类师在观察世界各地恐怖袭击趋势方面的表现。为了实验目的,1970-2015年,从不同的公开和公开访问来源创建了数据库,由156.772起报告的袭击构成,造成巨大的生命和财产损失。这份手稿通过将攻击频率和较易发生地点的损失、趋势作为评估类别来列举。