Thanks to the internet, the distances between the countries are easily overcome and the communication network rapidly expands. This situation also affects the cyber security of the countries to a great extent. Attacks on critical infrastructures, companies, and public institutions can be magnitude that make great harms. These developments in cyber space bring new problems. One of them is cyber terror. Cyber terror does not have a certain and well-known definition. Cyber terror is the realization of terrorist acts in the field of cyber war. In addition, cyber space is a place of display for terrorist acts. The effects of cyber terror attacks have reached a level to scare all countries. There is not enough information about the definition, characteristics, methods used in cyber terror attacks and cyber terror groups. It is important for national administrators and staff to become conscious and to become informed about cyber terror. In this chapter, information will be presented, endeavors on awareness-creation will be made, and a role of guiding the future studies will be taken.

Distributed Denial of Service Attacks (DDoS) threaten every device connected to the Internet. The fast progress and wide spreading DDoS attacks are among the most well-known features of them. Many studies have been conducted to reduce the impact of these fast-progressing and widespread attacks. However, due to the continuous development of attack types and the implementation of different techniques, the prevention of attacks has not been fully achieved. Therefore, within the scope of this study, a DDoS attack was examined first and applications used to detect it were investigated. A system has been proposed to detect DDoS attacks using data mining methods. For the proposed system, experiment mechanisms for Transmission Control Protocol (TCP) Flooding, Spoofing Internet Protocol (IP), SYN Flood with Spoofed IP, and User Datagram Protocol (UDP) Flooding, which are among the DDoS attack types, were established and the attacks were performed to obtain network flow data. The classification was made with appropriate data mining methods according to the specified features and ZeroR, OneR, Naive Bayes, Bayes Net, Decision Stump, and J48 algorithms were used. According to these algorithms, the best classification rate has been reached with J48 algorithm. The results have shown that the proposed system plays an important role in determining the DDoS attack type. The proposed system will ensure that appropriate detection mechanisms are applied more quickly, effectively and efficiently in real attacks.

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