Browsing by Author "Koca, M."

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Cybersecurity Attack Detection Model, Using Machine Learning Techniques
(Budapest Tech Polytechnical Institution, 2023) Avcı, İ.; Koca, M.
Millions of people use the web every day, in this age of technology and the internet. Protecting the privacy and security of these users is a significant challenge for cybersecurity developers. With tremendous technological advancements, there is a noticeable improvement in the cyber-attackers' capabilities. At the same time, traditional Intrusion Detection Systems (IDS) are no longer effective at detecting intrusions. After the tremendous competences achieved by Artificial Intelligence (AI) techniques in all fields, great interest has developed in its use in the field of cybersecurity. There have been many studies that use Machine Learning (ML)-based intrusion detection systems. Despite the strong performance of ML techniques in detecting malicious activities, some challenges still reduce accuracy of performance. Knowing the proper technique, as well as knowing the features, is essential for effective intrusion detection. Therefore, this study proposes an effective network intrusion detection system based on ML and feature selection techniques. The performance of four ML techniques, the Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and the Decision Tree (DT) systems for intrusion detection are explored. In addition, feature selection techniques are employed for the selection of important features. Among the techniques used, the RF technique achieved the best performance, outperforming other techniques, with an accuracy of 99.72%. This study elaborates on the detection of malicious and benign cyber-attacks, with a new-level, high accuracy. © 2023, Budapest Tech Polytechnical Institution. All rights reserved.
Optimization Planning Techniques With Meta-Heuristic Algorithms in Iot: Performance and Qos Evaluation
(Sakarya University, 2024) Koca, M.; Avcı, İ.
Big data analysis used by Internet of Things (IoT) objects is one of the most difficult issues to deal with today due to the data increase rate. Container technology is one of the many technologies available to address this problem. Because of its adaptability, portability, and scalability, it is particularly useful in IoT micro-services. The most promising lightweight virtualization method for providing cloud services has emerged owing to the variety of workloads and cloud resources. The scheduler component is critical in cloud container services for optimizing performance and lowering costs. Even though containers have gained enormous traction in cloud computing, very few thorough publications address container scheduling strategies. This work organizes its most innovative contribution around optimization scheduling techniques, which are based on three metaheuristic algorithms. These algorithms include the particle swarm algorithm, the genetic algorithm, and the ant colony algorithm. We examine the main advantages, drawbacks, and significant difficulties of the existing approaches based on performance indicators. In addition, we made a fair comparison of the employed algorithms by evaluating their performance through Quality of Service (QoS) while each algorithm proposed a contribution. Finally, it reveals a plethora of potential future research areas for maximizing the use of emergent container technology. © 2024, Sakarya University. All rights reserved.
Classification of Malicious Urls Using Naive Bayes and Genetic Algorithm
(Sakarya University, 2023) Koca, M.; Avcı, İ.; Al-Hayani, M.A.S.
The financial losses of vulnerable and insecure websites are increasing day by day. The proposed system in this research presents a strategy based on factor analysis of website categories and accurate identification of unknown information to classify safe and dangerous websites and protect users from the previous one. Probability calculations based on Naive Bayes and other powerful approaches are used throughout the website classification procedure to evaluate and train the website classification model. According to our study, the Naive Bayes approach was benign and showed successful results compared to other tests. This strategy is best optimized to solve the problem of distinguishing secure websites from unsafe ones. The vulnerability data categorization training model included in this datasheet had a better degree of precision. In this study, the best accuracy probability of 96% was achieved in Naive Bayes' NSL-KDD data set categorization. © 2023, Sakarya University. All rights reserved.