A Neural Network for Counter-Terrorism

Dixon, Sam J.; Dixon, Mark; Elliott, John F.; Guest, Elizabeth; Mullier, Duncan

doi:10.1007/978-1-4471-2318-7_18

Cited by 6 publications

(5 citation statements)

References 5 publications

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“…In every country, dealing with terrorism is the top most priority of the government. ey seek for techniques to Input: the whole dataset of GTD along with labels Output: optimized values of W and b Data: GTD Datasets (1) W [1..L] � random numbers //Glorot Uniform initializer (2) b [1..L] � random numbers (3) while i ≤ num iteration do (4) k ⟵ 1 (5) while j ≤ L do (6) Z [j] � W [j]T .A [j− 1] + b [j] 7A [j] � g(Z [j] )//g(Z) � max(0, z) (8) increment j by 1 (9) L(A [L] , Y) � − (1/m) m i�0 Y i log(A [L] i ) //Binary cross-entropy loss (10) k ⟵ L (11) while k ≥ 0 do (12) W [k] � W [k] − αzL/zW[k] (13) b [k] � b [k] − αzL/zb[k] (14) decrement k by 1 ALGORITHM 2: e training of deep neural network using gradient descent optimization algorithm. Complexity understand the different factors involved in terrorism and how to deal with those factors in order to completely stop or reduce terrorist activities.…”

Section: Resultsmentioning

confidence: 99%

“…e authors demonstrated that ensemble framework has better figures compared to individual models. In 2011, Dixon et al [14] developed a neural network-based framework for counterterrorism. e authors used a game that is designed by criminologist and psychologists to generate data that can test 2 Complexity the suitability of AI techniques to look for counterterrorism.…”

Section: Related Workmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Prediction of Future Terrorist Activities Using Deep Neural Networks

et al. 2020

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“…The scholarship on a terrorist network has witnessed a manifold increase in recent times, especially concerning gaining insight into a terrorist network. Diverse approaches to understanding terrorism such as the use of AI [24], including the CNN model [29], ], machines learning [25], [26], [27], [31], and natural language processing [28] have all been applied. The work in [20] suggests the aggregation of knowledge from diverse sources in solving complex issues collectively.…”

Section: Related Workmentioning

confidence: 99%

A New Model for Predicting and Dismantling a Complex Terrorist Network

et al. 2022

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The perennial nature of terrorist attacks in recent times has ushered in a new wave of study in the field of terrorism known as social network analysis (SNA). Terrorist groups operate as a social network stealthily to enhance their efficacy rate, thereby making sure their activities are cloaked. This study proposes a new method to uncover and dismantle a terror network by applying a 4-centrality measure. After establishing link prediction using centrality measures, we dismantle the network based on Galton-Watson extinction probability. This study examines the 9/11 incident and measures the link prediction of the M-19 network. In curbing terrorist network activities, the elimination of a highly ranked node in the network is a condition sine qua non. We further examine the lethality of the network as well as network bonding to obtain the degree of cohesiveness, which is a crucial determinant for network survival. Our experimental results indicate that link prediction is vital in uncloaking a terror network, which is the core of counterterrorism.

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“…The results showed that the occurrence was far from random and had a periodicity of around 1 month, but the victimization rate had no constant trend. Moreover, in [13], the authors highlighted the importance of neural networks in predicting terrorism behavior. A game was designed by criminologists and psychologists to test the effectiveness of different AI technologies/algorithms.…”

Section: Related Workmentioning

confidence: 99%

Cluster Analysis of Pakistani Terrorism Events to Support Counterterrorism

2018

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The frequency of terrorist events in Pakistan has increased considerably in the past several years. These events are frequent and not random, making it important to identify useful patterns in their occurrences to assist counterterrorism organizations. In this paper, we conducted such an analytical activity for the first time in Pakistan. We acquired data of terrorist events from reliable online sources and applied data preprocessing techniques followed by cluster analysis. Based on statistical correlation, we discovered clusters over the following combinations: (1) "Event of Terrorism-Target of Terrorism"; and (2) "Event of Terrorism-Method of Terrorism". A more significant clustering is one which groups distinct combinations into separate clusters. We analyzed these clusters along three dimensions: (1) Annually for the time period 1988-2012; (2) for each Pakistani province; and (3) for different types of terrorist events. We also proposed a statistic for gauging the intensity of terrorism and analyzed it along the same three dimensions. Our results were extensive, but generally indicated significant Event-Target and Event-Method clusters, as well as increasing and decreasing trends in terrorism intensity. These can assist counterterrorism authorities in thwarting future attacks and arresting the responsible criminals.

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A Neural Network for Counter-Terrorism

Cited by 6 publications

References 5 publications

Prediction of Future Terrorist Activities Using Deep Neural Networks

Prediction of Future Terrorist Activities Using Deep Neural Networks

A New Model for Predicting and Dismantling a Complex Terrorist Network

Cluster Analysis of Pakistani Terrorism Events to Support Counterterrorism

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