Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach

Ding, Fang; Ge, Quansheng; Jiang, Dong; Fu, Jingying; Hao, Mengmeng

doi:10.1371/journal.pone.0179057

Cited by 55 publications

(31 citation statements)

References 28 publications

(24 reference statements)

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“…SVM, Naïve Bayes, and logistic regression were used, and they demonstrated an accuracy of up to 78%. In [19], Ding et al used machine learning methods (NNET, SVM, and Random Forest) to simulate the risk of terrorist attacks. e model was able to predict the places where terrorist events might occur with a success rate of 96%.…”

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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Section: Related Workmentioning

confidence: 99%

Prediction of Future Terrorist Activities Using Deep Neural Networks

et al. 2020

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“…This approach of using data-driven predictive models to predict malicious behaviour is not new and current models using machine learning technologies have a high degree of theoretical accuracy in detecting terrorist behaviour (Salih et al 2017;RAND National Security Research Division 2005;Li et al 2018;Ding et al 2017;Schneider et al 2011). With accessibility to more data for machine learning and iterative improvements, these terrorist focussed models are likely to improve over time and the learnings from these models are likely to be useful inputs into an AI treachery model.…”

Section: Artificial Intelligence Treachery Threat Modelmentioning

confidence: 99%

Risk management standards and the active management of malicious intent in artificial superintelligence

Bradley¹

2019

AI & Soc

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The likely near future creation of artificial superintelligence carries significant risks to humanity. These risks are difficult to conceptualise and quantify, but malicious use of existing artificial intelligence by criminals and state actors is already occurring and poses risks to digital security, physical security and integrity of political systems. These risks will increase as artificial intelligence moves closer to superintelligence. While there is little research on risk management tools used in artificial intelligence development, the current global standard for risk management, ISO 31000:2018, is likely used extensively by developers of artificial intelligence technologies. This paper argues that risk management has a common set of vulnerabilities when applied to artificial superintelligence which cannot be resolved within the existing framework and alternative approaches must be developed. Some vulnerabilities are similar to issues posed by malicious threat actors such as professional criminals and terrorists. Like these malicious actors, artificial superintelligence will be capable of rendering mitigation ineffective by working against countermeasures or attacking in ways not anticipated by the risk management process. Criminal threat management recognises this vulnerability and seeks to guide and block the intent of malicious threat actors as an alternative to risk management. An artificial intelligence treachery threat model that acknowledges the failings of risk management and leverages the concepts of criminal threat management and artificial stupidity is proposed. This model identifies emergent malicious behaviour and allows intervention against negative outcomes at the moment of artificial intelligence's greatest vulnerability.

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“…This method used prospective space-time scan statistics and could detect outbreaks of terrorist events at an early stage. Ding et al (2017) demonstrated a deep learning method to evaluate risks of terrorist attacks on a global scale based on GTD dataset and other multiple resources datasets. The method performed well in predicting the terrorism incidents with a precision of 96.6%.…”

Section: Simulating and Forecasting The Geopolitical Eventsmentioning

confidence: 99%

Spatio-temporal simulation of the geopolitical environment system

Jiang

et al. 2018

J. Geogr. Sci.

Self Cite

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Geographical circumstances are the fundamental background for all kinds of geopolitical events. The geopolitical environment system (GES) refers to a system that combines both physical and anthropogenic subsystems. Research on the geopolitical environment system simulation is a key to understanding the international geopolitical phenomenon. The theory of GES arose from the integration of the traditional geopolitics and earth system sciences. As an interdisciplinary system composed of many different fields, integrated reviews and a metadata study of GES are urgently needed. This paper presents a comprehensive view into the origination and advance of the GES theory. The conceptual framework of the GES is described in detail. The methodology for simulating and forecasting geopolitical events is also provided. It is proposed that the core topics of the GES science may include, but are not limited to, issues as data acquisition technologies; principles on the interactions between multiple subsystems (or factors) at different scales; evaluating and mitigating the global geopolitical risks, including the political risks, economic risks, the social risks, the environmental risks and the technological risks; and forecasting the geopolitical events with machine learning and artificial intelligence techniques.

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Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach

Cited by 55 publications

References 28 publications

Prediction of Future Terrorist Activities Using Deep Neural Networks

Prediction of Future Terrorist Activities Using Deep Neural Networks

Risk management standards and the active management of malicious intent in artificial superintelligence

Spatio-temporal simulation of the geopolitical environment system

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