Comparison of Machine Learning Approaches in the Prediction of Terrorist Attacks

Agarwal, Palak; Sharma, Mahak; Chandra, Satish

doi:10.1109/ic3.2019.8844904

Cited by 19 publications

(12 citation statements)

References 13 publications

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“…Different data mining and ML techniques, including SVMs, random forest (RF), and logistic regression (LR), were employed to analyse the dataset and predict the wouldbe terrorist groups, the success or failure of the attacks, and their effects on external factors. In the implementation, k-means clustering and dummy classifiers showed an improvement, while RF with the GTD and dummy classifiers with the GTD peaked to the marks of 0.82 and 0.56, respectively [7].…”

Section: Literature Reviewmentioning

confidence: 94%

“…Agarwal et al [7] provided useful insights by tracking patterns and trends [10] in their analysis of a historical dataset of the GTD. They predicted the factors that can potentially correlate with the menace of terrorism.…”

Section: Literature Reviewmentioning

confidence: 99%

“…According to the Global Terrorism Database (GTD) maintained by the University of Maryland, more than 61,000 incidents occurred between 2000 and 2014, resulting in 140,000 casualties. Meanwhile, the Global Terrorism Index (GTI) reported an increase in terrorism from 65 countries in 2015 to 77 countries in 2016, indicating more devastation than over the last 17 years [7].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Extremist Behaviour and Suicide Bombing from Terrorism Contents Using Supervised Learning

Mahmood¹,

Khan²

2022

Computers, Materials &Amp; Continua

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This study proposes an architecture for the prediction of extremist human behaviour from projected suicide bombings. By linking 'dots' of police data comprising scattered information of people, groups, logistics, locations, communication, and spatiotemporal characters on different social media groups, the proposed architecture will spawn beneficial information. This useful information will, in turn, help the police both in predicting potential terrorist events and in investigating previous events. Furthermore, this architecture will aid in the identification of criminals and their associates and handlers. Terrorism is psychological warfare, which, in the broadest sense, can be defined as the utilisation of deliberate violence for economic, political or religious purposes. In this study, a supervised learning-based approach was adopted to develop the proposed architecture. The dataset was prepared from the suicide bomb blast data of Pakistan obtained from the South Asia Terrorism Portal (SATP). As the proposed architecture was simulated, the supervised learning-based classifiers naïve Bayes and Hoeffding Tree reached 72.17% accuracy. One of the additional benefits this study offers is the ability to predict the target audience of potential suicide bomb blasts, which may be used to eliminate future threats or, at least, minimise the number of casualties and other property losses.

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Section: Literature Reviewmentioning

confidence: 94%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of Extremist Behaviour and Suicide Bombing from Terrorism Contents Using Supervised Learning

Mahmood¹,

Khan²

2022

Computers, Materials &Amp; Continua

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“…Random Forest is used to predict the risk of terrorist attacks using 15 driving factors. In 2019, Agarwal et al [25] focused on analyzing the dataset of GTD and made prediction on different factors that might have given a blow to terrorism. Different data mining and machine learning algorithms such as SVM, Random Forest, and logistic regression have been used to understand the dataset and predict different factors such as the success of terrorist attack, the group that was involved in terrorist attack and the effect of different external factors involved in terrorist attack.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Future Terrorist Activities Using Deep Neural Networks

et al. 2020

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One of the most important threats to today’s civilization is terrorism. Terrorism not only disturbs the law and order situations in a society but also affects the quality of lives of humans and makes them suppressed physically and emotionally and deprives them of enjoying life. The more the civilizations have advanced, the more the people are working towards exploring different mechanisms to protect the mankind from terrorism. Different techniques have been used as counterterrorism to protect the lives of individuals in society and to improve the quality of life in general. Machine learning methods have been recently explored to develop techniques for counterterrorism based on artificial intelligence (AI). Since deep learning has recently gained more popularity in machine learning domain, in this paper, these techniques are explored to understand the behavior of terrorist activities. Five different models based on deep neural network (DNN) are created to understand the behavior of terrorist activities such as is the attack going to be successful or not? Or whether the attack is going to be suicide or not? Or what type of weapon is going to be used in the attack? Or what type of attack is going to be carried out? Or what region is going to be attacked? The models are implemented in single-layer neural network (NN), five-layer DNN, and three traditional machine learning algorithms, i.e., logistic regression, SVM, and Naïve Bayes. The performance of the DNN is compared with NN and the three machine learning algorithms, and it is demonstrated that the performance in DNN is more than 95% in terms of accuracy, precision, recall, and F1-Score, while ANN and traditional machine learning algorithms have achieved a maximum of 83% accuracy. This concludes that DNN is a suitable model to be used for predicting the behavior of terrorist activities. Our experiments also demonstrate that the dataset for terrorist activities is big data; therefore, a DNN is a suitable model to process big data and understand the underlying patterns in the dataset.

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“…In this study, however, we are interested in the task of prediction rather than detection. Additionally, our task is to predict terrorist attacks or events, which is distinct from works that have sought to infer characteristics of an attack, such as the responsible group, after it has taken place [2,5,31,32]. Few works have attempted to predictively model terrorist attacks, with the recent work of Python et al being the noteworthy exception [25].…”

Section: Introductionmentioning

confidence: 99%

Predicting Terrorist Attacks in the United States using Localized News Data

Krieg,

Smith,

Chatterjee

et al. 2022

Preprint

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Terrorism is a major problem worldwide, causing thousands of fatalities and billions of dollars in damage every year. Toward the end of better understanding and mitigating attacks that take place in the United States, we present a set of machine learning models that learn from localized news data in order to predict whether a terrorist attack will occur on a given calendar date and in a given state. The best model-a Random Forest that learns from a novel variable-length moving average representation of the feature spaceachieves area under the receiver operating characteristic scores > .667 on four of the five states that were impacted most by terrorism between 2015 and 2018. Our key findings include that modeling terrorism as a set of independent events, rather than as a continuous process, is a fruitful approach-especially when the events are sparse and dissimilar. Additionally, our results highlight the need for localized models that account for differences between locations. From a machine learning perspective, we found that the Random Forest model outperformed several deep models on our multimodal, noisy, and imbalanced data set, thus demonstrating the efficacy of our feature representation method in such a context. Finally, we analyze factors that limit model performance, which include a noisy feature space and small amount of available data. These contributions provide an important foundation for the use of machine learning in efforts against terrorism in the United States and beyond.

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Comparison of Machine Learning Approaches in the Prediction of Terrorist Attacks

Cited by 19 publications

References 13 publications

Prediction of Extremist Behaviour and Suicide Bombing from Terrorism Contents Using Supervised Learning

Prediction of Extremist Behaviour and Suicide Bombing from Terrorism Contents Using Supervised Learning

Prediction of Future Terrorist Activities Using Deep Neural Networks

Predicting Terrorist Attacks in the United States using Localized News Data

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