Prediction of Unsolved Terrorist Attacks Using Group Detection Algorithms

Ozgul, Fatih; Erdem, Zeki; Bowerman, Chris

doi:10.1007/978-3-642-01393-5_4

Cited by 11 publications

(9 citation statements)

References 3 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 paper has claimed that the system reveals relationships between entities that are not easily detectable using traditional methods. In 2009, Ozgul et al [13] proposed an ensemble framework that can classify and predict terrorist groups using four different classifiers: Naïve Bayes, K-NN, Iterative Dichotomiser 3, and decision stump. e authors demonstrated that ensemble framework has better figures compared to individual models.…”

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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“…As quickly as the name of the involved group is sorted, the strategies to catch those culprits could be made. The general process followed to detect the terrorist group responsible is by using email, telephone signal information, terrorist web sites and social network analysis [8,10]. Terrorist activities occurred in the past are available in .…”

Section: Predicting Terrorist Groupmentioning

confidence: 99%

“…TGPM uses the concept of Crime Prediction Model [2,10], Group Detection Model (GDM) [16] and Offender Group Detection Model (OGDM) [16,17]. The predictive accuracy of the system is 80.41%.…”

Section: Terrorist Group Prediction Model (Tgpm)mentioning

confidence: 99%

“…Many researchers are working on Dark Web Analysis for the same purpose as these dark webs are hot spot for terrorist groups to exchange their ideas, spread propaganda, recruitment of new members, and plan attacks [9]. The prediction of terrorist group using historical data of attacks has very less explored; this is due to the lack of meticulous terrorist data which contains terrorist group's attacks and activities information [10]. The use of data mining technologies in counter terrorism has been flourishing since the U.S. Government encouraged the use of information technologies in security [11].…”

Section: Introductionmentioning

confidence: 99%

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E-TGPS: Enhanced Terrorist Group Prediction System for Counter Terrorism

Sachan¹

2015

IJCA

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Terrorist group prediction using historical data of terrorist incidents has been less explored due to the lack of meticulous terrorist data which contains terrorist group's attacks and activities information. There are many reasons for less exploration like its confidentiality & sensitivity. This paper presents an enhanced system that helps to predict the terrorist groups involved in the attack under investigation named E-TGPS. This system initially learns similarities of terrorist activities from various past terrorist incidents to predict the responsible group. This system can be considered as a vital tool for security agencies and intelligence analysts, by providing more reliable and predictive solutions to take effective counterterrorism measures. The system has been validated by experimental results. The overall performance of the system displays a fair degree of accuracy. This paper also lays emphases on the meticulous analysis of optimal parameters weight estimation, to improve the predictive accuracy of the system.

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Predicting the Nature of Terrorist Attacks in Nigeria Using Bayesian Neural Network Model

Ogundunmade,

Adepoju

2023

STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics &Amp; Health

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Prediction of Unsolved Terrorist Attacks Using Group Detection Algorithms

Cited by 11 publications

References 3 publications

Prediction of Future Terrorist Activities Using Deep Neural Networks

Prediction of Future Terrorist Activities Using Deep Neural Networks

E-TGPS: Enhanced Terrorist Group Prediction System for Counter Terrorism

Predicting the Nature of Terrorist Attacks in Nigeria Using Bayesian Neural Network Model

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