Open AccessUsing Global Terrorism Database (GTD) and Machine Learning Algorithms to Predict Terrorism and Threat
Author(s) -
S. Kalaiarasi*,
Ankit I. Mehta,
Devyash Bordia,
Sanskar
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a1768.109119
Subject(s) - terrorism , the internet , computer science , social media , database , event (particle physics) , machine learning , algorithm , computer security , artificial intelligence , world wide web , political science , law , physics , quantum mechanics
It is evident that there has been enormous growth in terrorist attacks in recent years. The idea of online terrorism has also been growing its roots in the internet world. These types of activities have been growing along with the growth in internet technology. These types of events include social media threats such as hate speeches and comments provoking terror on social media platforms such as twitter, Facebook, etc. These activities must be prevented before it makes an impact. In this paper, we will make various classifiers that will group and predict various terrorism activities using k-NN algorithm and random forest algorithm. The purpose of this project is to use Global Terrorism Database as a dataset to detect terrorism. We will be using GTD which stands for Global Terrorism Database which is a publicly available database which contains information on terrorist event far and wide from 1970 through 2017 to train a machine learning-based intelligent system to predict any future events that could bring threat to the society.