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Unsupervised Hybrid Algorithm to Detect Anomalies for Predicting Terrorists Attacks

by Francesco Curia
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 35
Year of Publication: 2020
Authors: Francesco Curia
10.5120/ijca2020920432

Francesco Curia . Unsupervised Hybrid Algorithm to Detect Anomalies for Predicting Terrorists Attacks. International Journal of Computer Applications. 176, 35 ( Jul 2020), 1-8. DOI=10.5120/ijca2020920432

@article{ 10.5120/ijca2020920432,
author = { Francesco Curia },
title = { Unsupervised Hybrid Algorithm to Detect Anomalies for Predicting Terrorists Attacks },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2020 },
volume = { 176 },
number = { 35 },
month = { Jul },
year = { 2020 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number35/31424-2020920432/ },
doi = { 10.5120/ijca2020920432 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:44:13.374852+05:30
%A Francesco Curia
%T Unsupervised Hybrid Algorithm to Detect Anomalies for Predicting Terrorists Attacks
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 35
%P 1-8
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This work presents a hybrid approach for unsupervised algorithms (UHA), in order to extract information and patterns from data concerning terrorist attacks. The reference data are those of the Global Terrorism Database. The work presents an approach based on autoencoders and k-modes type clustering. The results obtained are examined through some metrics presented in the article and it is also considered methodologically how to determine a robust threshold for anomaly detection problems.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Terrorism Unsupervised Learning Clustering Autoencoders Optimization

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