Deep Learning Based Security Support System For The Prevention Of Dynamite-Backed Terrorist Activities

Abstract

In today's society, one of the most important factors that threaten people is terrorism. In a society, terrorism affects the quality of life by disrupting the order of people. On the other hand, states are constantly developing different methods to fight terrorism. One of these methods is the use of deep learning, a subfield of machine learning, to fight terrorism. Deep learning has gained considerable popularity in the field of machine learning in recent years. In this study, a new model was proposed the basically VGG-16 architecture based on deep learning to recognize and prevent terrorist activities. With the proposed model, when dynamite was detected on human or train tracks in the images taken from the camera images used in security controls, a system that alerts the security guards in order to quickly identify the situation and take the appropriate measures. The data set used in the study was created by editing images of dynamite downloaded from the internet environment. In order to evaluate the performance of the proposed model, dynamite images found on human or train tracks were tested and dynamite images were determined with a success accuracy of 98.4% and a loss rate of 0.024.

Keywords

• Dynamite detection
• Deep learning
• Convolutional neural network

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

Öz

Günümüz toplumunda, insanları tehdit eden en önemli etmenlerden birisi terörizmdir. Terörizm bir toplumda, insanların düzen durumlarını bozarak, yaşam kalitesini etkilemektedir. Devletler ise terörle mücadele etmek için sürekli farklı yöntemler geliştirmektedir. Bu yöntemlerden birisi de terörle mücadele için makine öğrenmesinin bir alt alanı olan derin öğrenmenin kullanılmasıdır. Derin öğrenme, makine öğrenmesi alanında son yıllarda oldukça popülerlik kazanmıştır. Bu çalışmada, terör faaliyetlerini fark etmek ve önlemek için derin öğrenmeye dayalı VGG-16 mimarisi temel alınarak yeni bir model önerilmektedir. Önerilen model ile güvenlik kontrollerinde kullanılan kamera görüntülerinden alınan görüntülerde, insan ya da tren rayları üzerinde dinamit tespit edildiğinde, durumu hızla belirlemek ve uygun önlemleri almak için güvenlik görevlilerini uyaran bir sistem gerçekleştirilmiştir. Çalışmada kullanılan veri seti, internet ortamından indirilen dinamit resimleri düzenlenerek oluşturulmuştur. Önerilen modelin performansını değerlendirmek için, insan ya da tren rayları üzerinde bulunan dinamit resimleri test edilerek, %98,4’lük başarı doğruluğu ve 0,024 kayıp oranıyla dinamit görüntüleri tespit edilmektedir.

Anahtar Kelimeler

• Dinamit tespit
• Derin öğrenme
• Evrişimsel sinir ağı

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