Prediction of Future Terrorist Activities Using Deep Neural Networks

Abstract

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.

Dinamit Destekli Terör Faaliyetlerinin Önlenmesi İçin Derin Öğrenme Temelli Güvenlik Destek Sistemi

“…Derin sinir ağlarını kullanarak terör faaliyetlerinin tahminini yapmaya çalışan bir çalışmada, terör faaliyetinin davranışını anlamak için saldırının türünün intihar olup olmadığı, saldırının başarılı olup olmadığı, ne tür bir silah kullanılabileceği, hangi bölgenin hedef alınabileceği ve ne tür bir teröristin kullanılacağına dayalı beş farklı faktör belirlenmektedir [16]. Çalışmada tek katmalı sinir ağı ve derin sinir ağı tabanlı modeller geliştirilerek, derin sinir ağına dayalı modelin başarı oranının daha yüksek olduğu görülmüştür [16].…”

“…Derin öğrenme, bu tahminleri verimli bir şekilde yapabilmekte ve bireylerin hayatlarını korumak için yeni yöntemlerin geliştirilmesine yardımcı olmaktadır. Bu yöntemler yardımıyla, bir terör eylemi fiilen gerçekleşmeden önce güvenlik güçlerinin terör olayları ile başa çıkmasını sağlanabilmektedir [16].…”

Efficient Processing of Image Processing Applications on CPU/GPU

“…In another research [23], GPU-based Hadoop framework is used for evaluation of Canny Edge Detection algorithm using the default scheduler of Hadoop for workload distribution and has demonstrated two times performance improvement than HIPI-based image processing [24]. In [25,26], face detection in video frames is performed by using CUDA-based Hadoop framework. It is observed that actual data processing takes 55% of the processor time, and the remaining 45% is wasted as idle or busy in performing other management activities.…”

Developing an Efficient Deep Learning-Based Trusted Model for Pervasive Computing Using an LSTM-Based Classification Model

“…Apriori algorithm was firstly used to extract the behavioral patterns, and then, Naïve Bayes classifier was used for decision making for trustworthiness of users. Uddin et al [14] proposed an approach for the detection of terrorist activities.…”