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    Gender Detection via Voice Using Artificial Intelligence Algorithms
    (Aydın Karapınar, 2022) Gönen, Serkan; Barışkan, Mehmet Ali; Karacayılmaz, Gökçe; Alhan, Birkan; Yılmaz, Ercan Nurcan; Artuner, Harun
    As a result of the developments in science and technology, all our living spaces, from health, education, and trade to our social life, have been moved to the digital environment. With this process, artificial intelligence, which is the ultimate goal of creating systems that think and act like human beings, has started to be used in all areas of our lives. This study focuses on gender determination by using artificial intelligence algorithms on voice data. Thanks to this determination, significant contributions will be made in various fields such as social engineering and cyber security such as fraud, person detection, and advertising investments. In the analysis of the study, R application, a completely open-source, for various artificial intelligence algorithms has been used. In this way, a solution has been provided to take the security as mentioned above measures with low cost instead of high- cost systems and increase the sales figures in areas such as marketing. In the study, supervised learning artificial intelligence algorithms have been examined. The artificial intelligence analysis results of the study have shown that the gender of the person could be determined above % 97 successful rates through the voice data.
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    A Novel Approach to Prevention of Hello Flood Attack in IoT Using Machine Learning Algorithm
    (TUBITAK, 2022) Gönen, Serkan; Barişkan, Mehmet Ali; Karacayilmaz, Gökçe; Alhan, Birkan; Yilmaz, Ercan Nurcan; Artuner, Harun; Sindiren, Erhan
    With the developments in information technologies, every area of our lives, from shopping to education, from health to entertainment, has transitioned to the cyber environment, defined as the digital environment. In particular, the concept of the Internet of Things (IoT) has emerged in the process of spreading the internet and the idea of controlling and managing every device based on IP. The fact that IoT devices are interconnected with limited resources causes users to become vulnerable to internal and external attacks that threaten their security. In this study, a Flood attack, which is an important attack type against IoT networks, is discussed. Within the scope of the analysis of the study, first of all, the effect of the flood attack on the system has been examined. Subsequently, it has been focused on detecting the at-tack through the K-means algorithm, a machine learning algorithm. The analysis results have been shown that the attacking mote where the flood attack has been carried out has been successfully detected. In this way, similar flood attacks will be detected as soon as possible, and the system will be saved from the attack with the most damage and will be activated as soon as possible. © 2022, TUBITAK. All rights reserved.
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    Password Attack Analysis Over Honeypot Using Machine Learning Password Attack Analysis
    (Matematikçiler Derneği, 2021) Taşçı, Hatice Beyza; Gönen, Serkan; Barışkan, Mehmet Ali; Karacayılmaz, Gökçe; Alhan, Birkan; Yılmaz, Ercan Nurcan
    Developing information and technology has caused the digitization of data in all areas of our lives. While this digitization provides entirely new conveniences, speed, efficiency, and effectiveness in our current life, it also created a new environment, space, and ultimately a risk area for attackers. This new space is called cyberspace. There is a constant struggle between security experts and attackers in cyberspace. However, as in any environment, the attacker is always in an advantageous position. In this fight, the newest approach for security experts to catch attackers is to use technologies based on prediction and detection, such as artificial intelligence, machine learning, artificial neural networks. Only in this way will it be possible to fight tens of thousands of pests that appear every second. This study focuses on detecting password attack types (brute force attack, dictionary attack, and social engineering) on real systems using Cowrie Honeypot. The logs obtained during the said attacks were used in the machine learning algorithm, and subsequent similar attacks were classified with the help of artificial intelligence. Various machine learning algorithms such as Naive Bayes, Decision tree, Random Forest, and Support Vector Machine (SVM) have been used to classify these attacks. As a result of this research, it was determined that the password attacks carried out by the attacker were phishing attacks, dictionary attacks, or brute force attacks with high success rates. Determining the type of password attack will play a critical role in determining the measures to be taken by the target institution to close the vulnerabilities in which the attack can be carried out. It has been evaluated that the study will make significant contributions to cybersecurity and password attacks.
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    Real-Time Cyber Attack Detection Over HoneyPi Using Machine Learning
    (UNIV OSIJEK, TECH FAC, TRG IVANE BRLIC-MAZURANIC 2, SLAVONSKI BROD HR-35000, CROATIA, 2022) Alhan, Birkan; Gönen, Serkan; Karacayılmaz, Gökçe; Barışkan, Mehmet Ali; Yılmaz, Ercan Nurcan
    The rapid transition of all areas of our lives to the digital environment has kept people away from their intertwined social lives and made them dependent on the isolated cyber environment. This dependency has led to increased cyber threats and, subsequently, cyber-attacks nationally or internationally. Due to the high cost of cybersecurity systems and the expert nature of these systems' management, the cybersecurity component has been mostly ignored, especially in small and medium-sized organizations. In this context, a holistic cybersecurity architecture is designed in which fully open source and free software and hardware-based Raspberry Pi devices with low-cost embedded operating systems are used as a honeypot. In addition, the architectural structure has an integrated, flexible, and easily configurable end-to-end security approach. It is suitable for different platforms by creating end-user screens with personalized software for network security guards and system administrators.