Machine Learning Techniques to Visualize and Predict Terrorist Attacks Worldwide using the Global Terrorism Database

Huamaní, Enrique Lee; Mantari, Alicia Alva; Román-Gonzalez, Avid

doi:10.14569/ijacsa.2020.0110474

Cited by 20 publications

(17 citation statements)

References 16 publications

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“…Huamaní et al [34] used AI techniques to quantify and predict possible terrorist attacks using decision trees and random forest classification models. However the study did not identify patterns of terrorist attacks.…”

Section: Literature Reviewmentioning

confidence: 99%

Suicide Bomb Attack Identification and Analytics through Data Mining Techniques

et al. 2021

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Suicide bomb attacks are a high priority concern nowadays for every country in the world. They are a massively destructive criminal activity known as terrorism where one explodes a bomb attached to himself or herself, usually in a public place, taking the lives of many. Terrorist activity in different regions of the world depends and varies according to geopolitical situations and significant regional factors. There has been no significant work performed previously by utilizing the Pakistani suicide attack dataset and no data mining-based solutions have been given related to suicide attacks. This paper aims to contribute to the counterterrorism initiative for the safety of this world against suicide bomb attacks by extracting hidden patterns from suicidal bombing attack data. In order to analyze the psychology of suicide bombers and find a correlation between suicide attacks and the prediction of the next possible venue for terrorist activities, visualization analysis is performed and data mining techniques of classification, clustering and association rule mining are incorporated. For classification, Naïve Bayes, ID3 and J48 algorithms are applied on distinctive selected attributes. The results exhibited by classification show high accuracy against all three algorithms applied, i.e., 73.2%, 73.8% and 75.4%. We adapt the K-means algorithm to perform clustering and, consequently, the risk of blast intensity is identified in a particular location. Frequent patterns are also obtained through the Apriori algorithm for the association rule to extract the factors involved in suicide attacks.

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Section: Literature Reviewmentioning

confidence: 99%

Suicide Bomb Attack Identification and Analytics through Data Mining Techniques

et al. 2021

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“…The potential of machine learning in the public sphere is grounded in the tremendous data availability and policy prediction needs of this field. Machine learning has already demonstrated considerable potential to enhance the effectiveness and accuracy of many decisions-making scenarios ranging from medical diagnosis, granting mortgages, tax evasion, and terrorist activities identification (Kononenko, 2001;Nowshath et al, 2019;Rodríguez et al, 2019;Mantari et al, 2020). Machine Learning is an umbrella term encompassing a wide range of algorithms for fields such as natural language processing, data mining, image processing, and predictive analytics.…”

Section: Why the Use Of Machine Learning In Public Policy?mentioning

confidence: 99%

Machine learning in Governments: Benefits, Challenges and Future Directions

2021

JeDEM

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The unprecedented increase in computing power and data availability has signifi-cantly altered the way and the scope that organizations make decisions relying on technologies. There is a conspicuous trend that organizations are seeking the use of frontier technologies with the purpose of helping the delivery of services and making day-to-day operational deci-sions. Machine learning (ML) is the fastest growing and at the same time, the most debated and controversial of these technologies. Although there is a great deal of research in the literature related to machine learning applications, most of them focus on the technical aspects or pri-vate sector use. The governmental machine learning applications suffer the lack of theoretical and empirical studies and unclear governance framework. This paper reviews the literature on the use of machine learning by government, aiming to identify the benefits and challenges of wider adoption of machine learning applications in the public sector and to propose the direc-tions for future research.

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“…Therefore, we set the fusion rules by referring to the classification and measurement index of F1-Score [59]. The fusion rule is shown in Equation (14).…”

Section: I-mlknnmentioning

confidence: 99%

“…According to the GTD, currently the types of terrorist attacks are divided into eight categories, including Assassination, Armed Assault, Bombing/Explosion, Hijacking, Hostage Taking (Barricade Incident), Hostage Taking (Kidnapping), Facility/Infrastructure Attack and Unarmed Assault. The details of the attack information are as follows [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Spatial Distribution Assessment of Terrorist Attack Types Based on I-MLKNN Model

Zhao

Xie

Zhang

et al. 2021

IJGI

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Terrorist attacks are harmful to lives and property and seriously affect the stability of the international community and economic development. Exploring the regularity of terrorist attacks and building a model for assessing the risk of terrorist attacks (a kind of public safety risk, and it means the possibility of a terrorist attack) are of great significance to the security and stability of the international community and to global anti-terrorism. We propose a fusion of Inverse Distance Weighting (IDW) and a Multi-label k-Nearest Neighbor (I-MLKNN)-based assessment model for terrorist attacks, which is in a grid-scale and considers 17 factors of socio-economic and natural environments, and applied the I-MLKNN assessment model to assess the risk of terrorist attacks in Southeast Asia. The results show the I-MLKNN multi-label classification algorithm is proven to be an ideal tool for the assessment of the spatial distribution of terrorist attacks, and it can assess the risk of different types of terrorist attacks, thus revealing the law of distribution of different types of terrorist attacks. The terrorist attack risk assessment results indicate that Armed Attacks, Bombing/Explosions and Facility/Infrastructure Attacks in Southeast Asia are high-risk terrorist attack events, and the southernmost part of Thailand and the Philippines are high-risk terrorist attack areas for terrorism. We do not only provide a reference for incorporating spatial features in multi-label classification algorithms, but also provide a theoretical basis for decision-makers involved in terrorist attacks, which is meaningful to the implementation of the international counter-terrorism strategy.

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Machine Learning Techniques to Visualize and Predict Terrorist Attacks Worldwide using the Global Terrorism Database

Cited by 20 publications

References 16 publications

Suicide Bomb Attack Identification and Analytics through Data Mining Techniques

Suicide Bomb Attack Identification and Analytics through Data Mining Techniques

Machine learning in Governments: Benefits, Challenges and Future Directions

Spatial Distribution Assessment of Terrorist Attack Types Based on I-MLKNN Model

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