2 sonuçlar
Arama Sonuçları
Listeleniyor 1 - 2 / 2
Yayın Healthy lifestyle behaviors of university students: the role of sense of coherence and family health climate(Dokuz Eylul University, 2025-01-31) Cerrahoğlu, Ece; Ünver, Buket; Ülkümen, İpekPurpose: This study aims to examine the predictive role of individual sense of coherence, family sense of coherence and family health climate variables on university students' healthy lifestyle behaviors. Material and Methods: The sample of the study consisted of 371 university students aged 18-25. Sociodemographic Information Form, Healthy Lifestyle Behaviors Scale, Sense of Coherence Scale, Family Sense of Coherence Scale, Family Health Climate Scale were applied to the participants in order to collect the research data. Correlation analysis, independent two-sample t-test, one-way ANOVA test and multiple linear regression analysis were used to analyze the data. Results: According to the results of correlation analysis, a positive relationship was found between healthy lifestyle behaviors and individual sense of coherence, family sense of coherence and family health climate (p<.05). As a result of the multiple linear hierarchical regression analysis, after controlling for the sex variable, individual sense of coherence and family health climate variables significantly predicted healthy lifestyle behaviors (p<.05), while family sense of coherence had no significant predictive role on healthy lifestyle behaviors (p>.05). Conclusion: The findings show that individual sense of coherence, family sense of coherence and family health climate variables are essential on university students' healthy lifestyle behaviors. The sense of coherence provides significant protection in adopting health behaviors that will determine future health and well-being. Similarly, increasing healthy living practices within the family is of great importance for young people to adopt healthy lifestyle behaviors.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.
