Decision Rule-Based Learning of Terrorist Threats

Nida Meddouri, Loïc Salmon, David Beserra, Elloh Adja

2025

Abstract

Artificial Intelligence (AI) offers powerful tools for analyzing criminal data and predicting security threats. This paper focuses on the interpretable prediction of terrorist threats in France using official crime datasets from 2012 to 2021. We propose a preprocessing methodology to aggregate and label spatio-temporal crime data at the departmental level, addressing challenges such as data imbalance and structural heterogeneity. To ensure explainability, we adopt symbolic learning approaches based on decision rule generators implemented in WEKA, including MODLEM, NNge, and MOEFC. We evaluate these models through nine experiments simulating real-world prediction scenarios, using metrics such as misclassification rate, Recall, Kappa statistic, AUC-ROC, and AUPR. Results show that rule-based models achieve stable performance across periods, with Recall averaging 96% and AUPR close to 0.93, despite severe class imbalance. Among the tested methods, NNge and MOEFC provide the best trade-off between interpretability and predictive accuracy. These findings highlight the potential of interpretable rule-based models for supporting counter-terrorism strategies.

Paper Citation

in Harvard Style

Meddouri N., Salmon L., Beserra D. and Adja E. (2025). Decision Rule-Based Learning of Terrorist Threats. In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN , SciTePress, pages 448-456. DOI: 10.5220/0013774400004000

in Bibtex Style

@conference{kdir25,
author={Nida Meddouri and Loïc Salmon and David Beserra and Elloh Adja},
title={Decision Rule-Based Learning of Terrorist Threats},
booktitle={Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2025},
pages={448-456},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013774400004000},
isbn={},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Decision Rule-Based Learning of Terrorist Threats
SN -
AU - Meddouri N.
AU - Salmon L.
AU - Beserra D.
AU - Adja E.
PY - 2025
SP - 448
EP - 456
DO - 10.5220/0013774400004000
PB - SciTePress