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    Cyber Attack Detection with Encrypted Network Connection Analysis
    (Springer Science and Business Media Deutschland GmbH, 2024) Gonen, Serkan; Karacayilmaz, Gokce; Artuner, Harun; Bariskan, Mehmet Ali; Yilmaz, Ercan Nurcan
    The evolution of science and technology has led to increasingly complex cyber security threats, with advanced evasion techniques and encrypted communication channels making attacks harder to detect. While encryption has improved privacy and confidentiality for users, it has also provided a new avenue for attackers to exploit. Traditional intrusion detection systems, which transitioned from signature-based to behavior-based approaches, have struggled to keep up with these challenges. To address this issue, researchers have turned to continuous system monitoring and network traffic packet analysis. However, this method can be resource-intensive and time-consuming, particularly when analyzing encrypted packets. In this study, the JA3 fingerprint infrastructure was examined as a potential solution for quickly detecting attacks conducted over encrypted sessions while minimizing system downtime and damage. The results demonstrated that the JA3 infrastructure effectively detected attacks carried out via encrypted channels. Although Windows 10 and Kali 2020.4 operating systems were used as the victim and attacker systems respectively, the methodology can be applied to other operating systems and network hardware by following the outlined steps. This research is expected to make a significant contribution to the field of encryption-based attack prevention. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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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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    Network Forensics of RPL-Based Attacks
    (Düzce Üniversitesi Fen Bilimleri Enstitüsü, 2020) Karacayılmaz, Gökçe; Gönen, Serkan; Artuner, Harun; Yılmaz, Ercan Nurcan; Sayan, Hasan Hüseyin; Sindiren, Erhan
    IoT devices, which are increasing in highly manner day by day, are now in everywhere in our life. WSNs are used together with IoT devices to monitor real environments. In this study, attacks against WSNs were carried out. The attack chosen for this study is a flood attack. In addition, solution suggestions for this attack are presented. In this context, firstly reference and attack packages have been collected, and then the collected packages have been compared with the reference packages and forensic investigations have been carried out. The result of the evaluation has shown the importance continuous monitoring on 24/7 basis and detecting abnormal behaviors in IoT traffic with forensics analysis for preventing attacks.
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    A New Approach in Cyber Security of Industrial Control Systems: Li-Fi
    (Gazi Üniversitesi, 2022) Gönen, Serkan; Sayan, Hasan Hüseyin; Karacayılmaz, Gökçe; Sindiren, Erhan; Üstünsoy, Furkan; Artuner, Harun; Yılmaz, Ercan Nurcan; Işık, Mehmet Fatih
    Industrial Control Systems used in the control of critical infrastructures, especially in smart grids, have made a rapid transition from an isolated network architecture to wired and wireless external networks due to the factors that facilitate human life such as productivity, economy and speed. With this transition, industrial control systems specific protocols have become insufficient and hybrid protocols have started to be used. As a result, the vulnerabilities specific to the internet and new protocols besides the vulnerabilities specific to industrial control systems have started to threaten control systems. In this study, the usability of the Light Fidelity (Li-Fi) transmission infrastructure in data transmission was investigated as a solution proposal for the smart city / network infrastructure where attack of False Data Injection carried out. This study will make important contributions to the studies carried out for industrial control systems security.
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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.