One of the most important factors for successful agricultural production is the irrigation system in place. In this study, a precision irrigation system, which takes advantage of the various phases of plant growth, was developed and implemented using the sensor network technology integrated with IOS/Android. The amount of water in the soil was measured via sensors that were placed on certain points of the area to be irrigated. These sensors were placed near the root of the product. Data from sensors was transmitted via Wi-Fi in real-time to a mobile phone based on IOS/Android. In the light of obtained data, the seasonal precision irrigation system was created depending on the amount of water required by the plants at each stage of their growth stage. The required energy of the system was provided by solar energy. The system can be controlled by smart phones, which increases the usability of the system. When design performance was analyzed, it was observed that some important advantages such as obtaining high efficiency with water, time and energy saving and reducing the workforce were ensured. Five separate laterals were used for the irrigation system. There were valves on each lateral, which realized the opening and closing process depending on the water need. A total of 16 humidity sensors were used in the irrigation system and the data from these sensors was transferred to the IOS/Android server via the programmable controller (PLC). The basic electrical equipment in the irrigation system was monitored and controlled via mobile devices. Control parameters were obtained by comparing the real values and reference values by a closed-loop system and determine the new working status of the irrigation system.

İnsanlık tarihi teknolojik açıdan önemli aşamalardan geçmiştir ve geçmeye devam etmektedir. Bu aşamaların en önemlilerinden birisi de Endüstri 4.0 sürecidir. Bu süreçle birlikte geleneksel sanayi üretimi yerini bilişim sistemlerinin ön planda olduğu bir yaklaşıma bırakmıştır. Endüstriyel haberleşme sistemleri ilk üretildikleri zamanlarda tamamen izole olarak yönetilmişlerdir. Zamanla iç ağlara (intranet) devamında ise internete bağlanmaları ile Endüstri 4.0 süreci başlamıştır. Bu süreçte siber-fiziksel sistemlerin ortaya çıkmasıyla birlikte, bu sistemler siber tehditlere karşı açık hale gelmişlerdir. Bu zafiyetler akıllı sistemleri ve Endüstri 4.0 sistemlerini saldırganların gözdesi haline getirmiştir. Her ne kadar bu sistemler verimlilik, sürat, işlerlik gibi insan hayatına önemli katkılar sağlasa da siber güvenlik açıklıkları uygun şekilde değerlendirilmezse, Endüstri 4.0'ın gerçek potansiyeline asla ulaşılamayabilir. Bu nedenle, çalışmada Endüstri 4.0’ın tüm bileşenleri ele alınarak, Siber Güvenlik bileşeni üzerine odaklanılmıştır. Çalışma, Endüstri 4.0’ın siber güvenlik boyutuna yönelik çalışmalara önemli katkı sağlayacaktır.

With the increasing number of cyber attacks, cyber security has become an increasing concern for every institution/organization. Every small or large institution/organization takes precautions and invests in cyber threats. Firewalls, intrusion detection/prevention systems, antivirus, data loss prevention software, etc., are used within the scope of ensuring cyber security and preventing threats. In order to detect cyber threats, the records obtained from these devices/applications are sent to SIEM applications. It is of great importance to use cyber threat intelligence data in these devices and applications in order to accurately detect cyber threats. As hard as it is to win a war without intelligence, it is also very difficult to provide effective cyber security without cyber threat intelligence. Cyber threat intelligence can be provided by commercial software as well as from open source platforms. In this study, deep learning models were used to process the cyber security data obtained from Twitter. With recursive neural networks, the tweets in the data set are related to cyber threat intelligence, then the cyber threat intelligence (DDoS, malware, ransomware, etc.) is classified. As a result of the study, 88.64% success was achieved in terms of whether cyber threat intelligence is relevant or not, and 89.49% success was achieved in classifying the type of threat intelligence.