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Yayın A robust Gradient boosting model based on SMOTE and NEAR MISS methods for intrusion detection in imbalanced data sets(Işık Üniversitesi, 2022-01-18) Arık, Ahmet Okan; Çavdaroğlu Akkoç, Gülsüm Çiğdem; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Enformasyon Teknolojileri Yüksek Lisans ProgramıNovel technologies cause many security vulnerabilities and zero-day attack risks. Intrusion Detection Systems (IDS) are developed to protect computer networks from threats and attacks. Many challenging problems need to be solved in existing methods. The class imbalance problem is one of the most difficult problems of IDS, and it reduces the detection rate performance of the classifiers. The highest IDS detection rate in the literature is 96.54%. This thesis proposes a new model called ROGONG-IDS (Robust Gradient Boosting) based on Gradient Boosting. ROGONGIDS model uses Synthetic Minority Over-Sampling Technique (SMOTE) and Near Miss methods to handle class imbalance. Three different gradient boosting-based classification algorithms (GBM, LightGBM, XGBoost) were compared. The performance of the proposed model on multiclass classification has been verified in the UNSW-NB15 dataset. It reached the highest attack detection rate and F1 score in the literature with a 97.30% detection rate and 97.65% F1 score. ROGONG-IDS provides a robust, efficient solution for IDS built on datasets with the imbalanced class distribution. It outperforms state-of-the-art and traditional intrusion detection methods.Yayın Yeni savaş ve siber güvenlik arasında NATO’nun yeniden doğuşu(Uluslararası İlişkiler Konseyi Derneği, 2012-06-01) Bıçakcı, Ahmet SalihSoğuk Savaşın bitişinden sonra uluslararası sistemin güvenlik dinamikleri değişti. Soğuk Savaş tehditlerinin ortadan kalkmasıyla birlikte Kuzey Atlantik Paktı Örgütü (NATO) yeni durumun gereklerine göre yeniden yapılanmak zorunda kaldı. Bu makale NATO’ya karşı siber tehditlerin ortaya çıkışını ve onun bu yeni güvenlik ortamına nasıl tepki vereceğini incelemektedir. Soğuk Savaş sonrasındaki dönemde, geleneksel savaş taktikleri savaş meydanının gereklerini yerine getirmekte yetersiz kalıyordu. Asimetrik savaş diğer yöntemlere göre daha öne çıktı. Kosova çatışması sırasında, NATO bombalamasına Sırp bilgisayar korsanları tarafından siber saldırılarla karşılık verilmiştir. Farklı durumlarda da benzer eğilimler görülmüştür. NATO yeni bir siber savunma stratejisi inşa etmeye ve uluslararası sistemdeki güncel tehditleri de kapsayacak bir strateji oluşturmaya başladı. Lizbon Zirvesinde siber savunma ve kritik bilgi altyapısının korunmasını da içeren yeni stratejinin hazırlanmasına onay verildi. NATO, siber savunmayı içeren hibrit savaş stratejisini başlattı ve bu yaklaşımı bütün üyelerinde uygulamaya başladı.Yayın Relationships among organizational-level maturities in artificial intelligence, cybersecurity, and digital transformation: a survey-based analysis(Institute of Electrical and Electronics Engineers Inc., 2025-05-19) Kubilay, Burak; Çeliktaş, BarışThe rapid development of digital technology across industries has highlighted the growing need for enhanced competencies in Artificial Intelligence (AI), Cyber security (CS), and Digital Transformation (DT). While there is extensive research on each of these domains in isolation, few studies have investigated their relationship and joint impact on organizational maturity. This study aims to address this gap by analyzing the relationships among the maturity levels of AI, CS, and DT at the organizational level using Structural Equation Modeling (SEM) and descriptive statistical methods. A mixed-methods design combines quantitative survey data with synthetic modeling techniques to assess organizational preparedness. The findings demonstrate significant bidirectional correlations among AI, CS, and DT, with technology and finance being more advanced than government and education. The research highlights the necessity of an integrated AI-CS strategy and provides actionable recommendations to increase investments in these domains. In contrast to the preceding fragmented evaluations, the current research establishes a comprehensive, empirically grounded framework that acts as a strategic reference point for digital resilience. Follow-up studies will involve collecting real-world industry data in support of empirical validation and predictive ability in measuring AI and CS maturity. This research adds to the existing literature by filling the gaps among fragmented digital maturity models and providing a consistent empirical base for organizations to thrive in an evolving technological environment.Yayın An intrusion detection approach based on the combination of oversampling and undersampling algorithms(Istanbul University Press, 2023-06-14) Arık, Ahmet Okan; Çavdaroğlu, Gülsüm ÇiğdemThe threat of network intrusion has become much more severe due to the increasing network flow. Therefore, network intrusion detection is one of the most concerned areas of network security. As demand for cybersecurity assurance increases, the requirement for intrusion detection systems to meet current threats is also growing. However, network-based intrusion detection systems have several shortcomings due to the structure of the systems, the nature of the network data, and uncertainty related to future data. The imbalanced class problem is also crucial since it significantly negatively affects classification performance. Although high performance has been achieved in deep learning-based methodologies in recent years, machine learning techniques may also provide high performance in network intrusion detection. This study suggests a new intrusion detection system called ROGONG-IDS (Robust Gradient Boosting – Intrusion Detection System) which has a unique two-stage resampling model to solve the imbalanced class problem that produces high accuracy on the UNSW-NB15 dataset using machine learning techniques. ROGONGIDS is based on gradient boosting. The system uses Synthetic Minority Over-Sampling Technique (SMOTE) and NearMiss-1 methods to handle the imbalanced class problem. The proposed model's performance on multi-class classification was tested with the UNSW-NB15, and then its robust structure was validated with the NSL-KDD dataset. ROGONG-IDS reached the highest attack detection rate and F1 score in the literature, with a 97.30% detection rate and 97.65% F1 score using the UNSW-NB15 dataset. ROGONG-IDS provides a robust, efficient intrusion detection system for the UNSW-NB15 dataset, which suffered from imbalanced class distribution. The proposed methodology outperforms state-of-the-art and intrusion detection methods.
