Industrial Control Systems (ICS) are vital for countries' smart grids and critical infrastructures. In addition to the advantages such as controlling and monitoring geographically distributed structures, increasing productivity and efficiency, ICS have brought some security problems. Specific solutions are needed to be produced for these security issues. The most important information security component for ICS is availability and the most devastating threat to this component is Denial of Service (DoS) attack. For this reason, DoS attacks carried out on Programmable Logic Controllers (PLC), an important component of ICS, have been analyzed in the paper. In the test environment where attack scenarios were implemented, real PLC devices were used in order to get the most accurate results. The destructive effects of insiders, particularly in the case of cyber attacks against ICS, in bypassing the system security measure and discovery phase also emphasized in the paper.

Along with the recent advancements in video streaming, concerns over the security of transferred data have increased. Thus, the development of fast and reliable image encryption methodologies has become an emerging research area in the field of communications. In this paper, a systolic array-based image encryption architecture is proposed. Systolic arrays are used to apply the convolution operation, and a Lü–Chen chaotic oscillator is used to obtain a convolutional filter. To decrease resource consumption, a method to fuse confusion and diffusion processes by using systolic arrays is also proposed in this paper. The results show that the proposed method is highly secure against some differential and statistical attacks. It is also shown that the proposed method has a high speed of encryption compared to other methods.

İnternet kullanımının artmasıyla daha çok erişilebilir hale gelen sosyal ağlar insanların çeşitli konular hakkındaki duygu ve görüşlerini paylaştığı mecralar haline gelmiştir. Dijital ortamda var olan verinin yüzde 90’ı geçen 2 yılda oluşturulmuştur. Fakat bu veriler üzerinde geleneksel yollarla analizler yapmak günler hatta aylar sürebilmektedir. Bu sebeple makine öğrenmesi yöntemleriyle sosyal ağlar üzerinden duygu analizleri yapılması tercih edilmektedir. Bu çalışmada Twitter üzerindeki tweetlerin İslamofobik olup olmadığı duygu analizi ile öğrenilmeye çalışılmıştır. Çalışmada Lineer ridge regresyonu ve Naive Bayes Sınıflandırıcı ile eğitilen modeller üzerinden precision, Recall, F1 ölçütlerinde hesaplamalarda bulunulmuştur. Sonuç olarak pozitif Tweetler için Ridge modelinde Naive Bayes sınıflandırıcıya göre daha iyi sonuçlar alınmış ve Ridge Regresyonunda %96,3 , Naive Bayes Sınıflandırıcıda %95.3 oranında doğru sonuca ulaşılmıştır.