Real time big data analytics for predicting terrorist incidents

Toure, Ibrahim; Gangopadhyay, Aryya

doi:10.1109/ths.2016.7568906

Cited by 13 publications

(6 citation statements)

References 30 publications

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“…In 2016, Toure and Gangopadhyay [16] collected incident data from a real-time system to develop a risk model that calculates the terrorism risk level of different locations. A set of rules was also proposed along with the risk model to make prediction of the future terrorist activities.…”

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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“…Many application domains such as healthcare, transportation systems, environmental monitoring, and smart cities will require realtime decision making and control [12]. For example, Twitter data can be real-time analyzed to enhance the prediction process and to provide useful recommendations to users [168]; terrorist incidents data can be real-time analyzed to predict future incidents [169]; big data stream in healthcare can be analyzed to help medical staff make decisions in real-time, which can help save patients' lives and improve the healthcare services provided, while reducing medical costs [170]. Near real-time big data analysis architecture for vehicular networks was proposed in [171], which consists of a centralized data storage for data processing and a distributed data storage for streaming processed data in real-time analysis.…”

Section: B Real-time Analyticsmentioning

confidence: 99%

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

et al. 2018

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The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world via creating new services and applications in a variety of sectors such as environmental monitoring, mobilehealth systems, intelligent transportation systems and so on. The information and communication technology (ICT) sector is experiencing a significant growth in data traffic, driven by the widespread usage of smartphones, tablets and video streaming, along with the significant growth of sensors deployments that are anticipated in the near future. It is expected to outstandingly increase the growth rate of raw sensed data. In this paper, we present the CPS taxonomy via providing a broad overview of data collection, storage, access, processing and analysis. Compared with other survey papers, this is the first panoramic survey on big data for CPS, where our objective is to provide a panoramic summary of different CPS aspects. Furthermore, CPS require cybersecurity to protect them against malicious attacks and unauthorized intrusion, which become a challenge with the enormous amount of data that is continuously being generated in the network. Thus, we also provide an overview of the different security solutions proposed for CPS big data storage, access and analytics. We also discuss big data meeting green challenges in the contexts of CPS.

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“…Real-time data analytics for predictions are used in many data-driven applications. A prediction system designed using real-time news data sources to predict future terrorist incidents is proposed by Toure and Gangopadhyay [20]. A predictive model is proposed by Zhang and Yuan [21] for air quality monitoring by analysis of real-time meteorology data from Beijing city.…”

Section: Related Workmentioning

confidence: 99%

Real-time Big Data Analytics Framework with Data Blending Approach for Multiple Data sources in Smart City Applications

Manjunatha

Annappa

2020

SCPE

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Advancement in Information Communication Technology (ICT) and the Internet of Things (IoT) has to lead tothe continuous generation of a large amount of data. Smart city projects are being implemented in various parts of the world where analysis of public data helps in providing a better quality of life. Data analytics plays a vital role in many such data-driven applications. Real-time analytics for finding valuable insights at the right time using smart city data is crucial in making appropriate decisions for city administration. It is essential to use multiple data sources as input for the analysis to achieve better and more accurate data-driven solutions. It helps in finding more accurate solutions and making appropriate decisions. Public safety is one of the major concerns in any smart city project in which real-time analytics is much useful in the early detection of valuable data patterns. It is crucial to find early predictions of crime-related incidents and generating emergency alerts for making appropriate decisions to provide security to the people and safety of the city infrastructure. This paper discusses the proposed real-time big data analytics framework with data blending approach using multiple data sources for smart city applications. Analytics using multiple data sources for a specific data-driven solution helps in finding more data patterns, which in turn increases the accuracy of analytics results. The data preprocessing phase is a challenging task in data analytics when data being ingested continuously in real-time into the analytics system. The proposed system helps in the preprocessing of real-time data with data blending of multiple data sources used in the analytics. The proposed framework is beneficial when data from multiple sources are ingested in real-time as input data and is also flexible to use any additional data source of interest. The experimental work carried out with the proposed framework using multiple data sources to find the crime-related insights in real-time helps the public safety solutions in the smart city. The experimental outcome shows that there is a significant increase in the number of identified useful data patterns as the number of data sources increases. A real-time based emergency alert system to help the public safety solution is implementedusing a machine learning-based classification algorithm with the proposed framework. The experiment is carried out with different classification algorithms, and the results show that Naive Bayes classification performs better in generating emergency alerts.

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Real time big data analytics for predicting terrorist incidents

Cited by 13 publications

References 30 publications

Prediction of Future Terrorist Activities Using Deep Neural Networks

Prediction of Future Terrorist Activities Using Deep Neural Networks

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

Real-time Big Data Analytics Framework with Data Blending Approach for Multiple Data sources in Smart City Applications

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